Noise Generation in Audio Codecs
Description
The present invention is concerned with an audio codec supporting noise synthesis during
inactive phases.
The possibility of reducing the transmission bandwidth by taking advantage of inactive
periods of speech or other noise sources are known in the art' Such schemes generally use
some form of detection to distinguish between inactive (or silence) and active (non-silence)
phases. During inactive phases, a lower bitrate is achieved by stopping the transmission of
the ordinary data stream precisely encoding the recorded signal, and only sending silence
insertion description (SID) updates instead. SID updates may be transmitted in a regular
interval or when changes in the background noise characteristics are detected. The SID
frames may then be used at the decoding side to generate a background noise with
characteristics similar to the background noise during the active phases so that the stopping
of the transmission of the ordinary data stream encoding the recorded signal does not lead
to an unpleasant transition from the active phase to the inactive phase at the recipient's
side.
However, there is still a need for further reducing the transmission rate. An increasing
number of bitrate consumers, such as an increasing number of mobile phones, and an
increasing number of more or less bitrate intensive applications, such as wireless
transmission broadcast, require a steady reduction of the consumed bitrate.
01 the other hand, the synthesized noise should closely emulate the real noise so that the
synthesis is transparent for the users.
Accordingly, it is one objective of the present inventionto provide an audio codec scheme
supporting noise generation during inactive phases which enables reducing the
transmission bitrate and/or helps increasing the achievable noise generation quality'
This objective is achieved by the subject matter of a part of the pending independent
35 claims.
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An objective of the present invention is to provide an audio codec supporting synthetic
noise generation during inactive phases which enables a more realistic noise generation at
moderate overhead in terms of, for example, bitrate and/or computational complexity'
The latter object is also achieved by the subject matter of another part of the independent
claims of the present application.
In particular, it is a basic idea underlying the present invention that the spectral domain
may very efficiently be used in order to para-r.ne.terize the background noise thereby
yielding a background noise synthesis which is more realistic and thus leads to a more
transparent active to inactive phase switching. Moreover, it has been found out that
parameterizing the background noise in the spectral domain enables separating noise from
the useful signal and accordingly, parameteizing the background noise in the spectral
domain has an advantage when combined with the aforementioned continuous update of
the parametric background noise estimate during the active phases as a better separation
between noise and useful signal may be achieved in the spectral domain so that no
additional transition from one domain to the other is necessary when combining both
advantageous aspects of the present application.
In accordance with specific embodiments valuable bitrate may be saved with maintaining
the noise generation quality within inactive phases, by continuously updating the
parametric background noise estimate during an active phase so that the noise generation
may immediately be started with upon the entrance of an inactive phase following the
active phase. For example, the continuous update may be performed at the decoding side,
and there is no need to preliminarily provide the decoding side with a coded representation
of the background noise during a warm-up phase immediately following the detection of
the inactive phase which provision would consume valuable bitrate, since the decoding
side has continuously updated the parametric background noise estimate during the active
phase and is, thus, prepared at any time to immediately enter the inactive phase with an
appropriate noise generation. Likewise, such a warm-up phase may be avoided if the
parametric background noise estimate is done at the encoding side. Instead of preliminarily
continuing with providing the decoding side with a conventionally coded representation of
the background noise upon detecting the entrance of the inactive phase in order to learn the
background noise and inform the decoding side after the learning phase accordingly, the
encoder is able to provide the decoder with the necessary parametric background noise
estimate immediately upon detecting the entrance of the inactive phase by falling back on
the parametric background noise estimate continuously updated during the past active
phase thereby avoiding the bitrate consuming preliminary further prosecution of
supererogatorily encoding the background noise.
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Further advantageous details of embodiments of the present invention are the subject of the
dependent claims of the pending claim set.
5 Preferred embodiments of the present application are described below with respect to the
Figures among which:
Fig. 1 shows a block diagram showing -an audio encoder according to an
embodiment;
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Fig.2 shows a possible implementation of the encoding engine 14;
Fig. 3 shows a block diagram of an audio decoder according to an embodiment;
15 Fig. 4 shows a possible implementation of the decoding engine of Fig. 3 in
accordance with an embodiment;
Fig. 5 shows a block diagram of an audio encoder according to a further, more
detailed description of the embodiment;
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Fig. 6 shows a block diagram of a decoder which could be used in connection with
the encoder of Fig. 5 in accordance with an embodiment;
Fig.7 shows a block diagram of an audio decoder in accordance with a further,
25 more detailed description of the embodiment;
Fig. 8 shows a block diagram of a spectral bandwidth extension part of an audio
encoder in accordance with an embodiment;
30 Fig.9 shows an implementation of the CNG spectral bandwidth extension encoder
of Fig. 8 in accordance with an embodiment;
Fig. l0 shows a block diagram of an audio decoder in accordance with an
embodiment using spectral bandwidth extension;
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Fig. 11 shows a block diagram of a possible, more detailed description of an
embodiment tbr an audio decoder using spectral bandwidth replication;
Fig. l2 shows a block diagram of an audio encoder in accordance with a further
embodiment using spectral bandwidth extension; and
shows a block diagram of a further embodiment of an audio decoder.
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Fig. 13
Fig. 1 shows an audio encoder according to an embodiment of the present invention. The
audio encoder of Fig. 1 comprises a background noise estimator 12, an encoding engine
14, a detector 16, an audio signal input 18 an{. a..data stream output 20. Provider 12,
encoding engine 14 and detector 16 have an input connected to audio signal input 18,
respectively. Outputs of estimator 12 andencoding engine 14 are respectively connected to
data stream output 20 via a switch 22. Switch 22, estimator 12 and encoding engine 14
have a control input connected to an output ofdetector 16, respectively.
The encoder 14 encodes the input audio signal into a data stream 30 during an active phase
24 and the detector 16 is configured to detect an entrance 34 of an inactive phase 28
following the active phase 24 based on the input signal. The portion of data stream 30
output by encoding engine 14 is denoted44.
The background noise estimator 12 is configured to determine a parametric background
noise estimate based on a spectral decomposition representation of an input audio signal so
that the parametric background noise estimate spectrally describes a spectral envelope of a
background noise of the input audio signal. The determination may be corlmenced upon
entering the inactive phase 38, i.e. immediately following the time instant 34 at which
detector 16 detects the inactivity. In that case, normal portion 44 of data stream 30 would
slightly extend into the inactive phase, i.e. it would last for another brief period sufficient
for background noise estimator 12 to learn/estimate the background noise from the input
signal which would be, then, be assumed to be solely composed of background noise.
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However, the embodiments described below take another line. According to altemative
embodiments described further below, the determination may continuously be performed
during the active phases to update the estimate for immediate use upon entering the
inactive phase.
In any case, the audio encoder 10 is configured to encode into the data stream 30 the
35 parametric background noise estimate during the inactive phase 28 such as by use of SID
frames 32 and 38.
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Thus, although many of the subsequently explained embodiments refer to cases where the
noise estimate is continuously performed during the active phases so as to be able to
irnmediately commence noise synthesis this is not necessarily the case and the
iprplementation could be different therefrom. Generally, all the details presented in these
advantageous embodiments shall be understood to also explain or disclose embodiments
,uvhere the respective noise estimate is done in upon detecting the noise estimate, for
exarnple.
Thus, the background noise estimator 12 mayU" ,onngured to continuously update the
parametric background noise estimate during the active phase 24 based on the input audio
signal entering the audio encoder l0 at input 18. Although Fig. I suggests that the
background noise estimator 12 may derive the continuous update of the parametric
background noise estimate based on the audio signal as input at input 18, this is not
necessarily the case. The background noise estimator 12 may alternatively or additionally
obtain a version of the audio signal from encoding engine 14 as illustrated by dashed line
26.In that case, the background noise estimator 12 would altematively or additionally be
connected to input 18 indirectly via connection line 26 and encoding engine 14
respectively. In particular, different possibilities exist for background noise estimator 12 to
continuously update the background noise estimate and some of these possibilities are
described further below.
The encoding engine 14 is configured to encode the input audio signal arriving at input 18
into a data stream during the active phase 24. The active phase shall encompass all times
where a useful information is contained within the audio signal such as speech or other
useful sound of a noise source. On the other hand, sounds with an almost time-invariant
characteristic such as a time-invariance spectrum as caused, for example, by rain or traffic
in the background of a speaker, shall be classified as background noise and whenever
merely this background noise is present, the respective time period shall be classified as an
inactive phase 28. The detector 16 is responsible for detecting the entrance of an inactive
phase 28 following the active phase 24basedon the input audio signal at input 18. In other
words, the detector l6 distinguishes between two phases, namely active phase and inactive
phase wherein the detector 16 decides as to which phase is currently present. The detector
16 informs encoding engine 14 about the currently present phase and as already mentioned,
encoding engine 14 performs the encoding of the input audio signal into the data stream
during the active phases 24. Detector 16 controls switch 22 accordingly so that the data
stream output by encoding engine 14 is output at output 20. During inactive phases, the
encoding engine 14 may stop encoding the input audio signal. At least, the data stream
outputted at output 20 is no longer fed by any data stream possibly output by the encoding
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engine 14. In addition to that, the encoding engine 14 may only perform minimum
processing to support the estimator l2 with some state variable updates. This action will
greatly reduce the computational power. Switch 22 is, for example, set such that the output
of estirnator 12 is connected to output 20 instead of the encoding engine's output. This
way, valuable transmission bitrate for transmitting the bitstream output at output 20 is
reduced.
In case of the background noise estimator l2 being configured to continuously update the
parametric background noise estimate during the active phase 24 based on the input audio
signal 18 as already mentioned above, estimator l2 is able to insert into the data stream 30
output at output 20 the parametric background noise estimate as continuously updated
during the active phase 24 immediately following the transition from the active phase 24 to
the inactive phase 28, i.e. immediately upon the entrance into the inactive phase 28.
Background noise estimator 12 may, for example, insert a silence insertion descriptor
frame 32 into the data stream 30 immediately following the end of the active phase 24 and,
immediately following the time instant 34 at which the detector 16 detected the entrance of
the inactive phase 28. In other words, there is no time gap between the detectors detection
of the entrance of the inactive phase 28 and the insertion of the SID 32 necessary due to the
background noise estimator's continuous update of the parametric background noise
estimate during the active phase 24.
Thus, summarizing the above description the audio encoder l0 of Fig. I in accordance with
a preferred option of implementing the embodiment of Fig. 1, same may operate as
follows. Imagine, for illustration purposes, that an active phase 24 is currently present. In
this case, the encoding engine 14 currently encodes the input audio signal at input 18 into
the data stream 20. Switch 22 cowrccts the output of encoding engine l4 to the output 20.
Encoding engine 14 may use parametric coding and/transform coding in order to encode
the input audio signal 18 into the data stream. In particular, encoding engine 14 may
encode the input audio signal in units of frames with each frame encoding one of
consecutive - partially mutually overlapping - time intervals of the input audio signal.
Encoding engine 14 may additionally have the ability to switch between different coding
modes between the consecutive frames of the data stream. For example, some frames may
be encoded using predictive coding such as CELP coding, and some other frames may be
coded using transform coding such as TCX or AAC coding. Reference is made, for
example. to USAC and its coding modes as described in ISO/IEC CD 23003-3 dated
September 24,2010.
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The background noise estimator 12 continuously updates the parametric background noise
estimate during the active phase 24. Accordingly, the background noise estimator 12 may
be configured to distinguish between a noise component and a useful signal component
within the input audio signal in order to determine the parametric background noise
estimate merely from the noise component. The background noise estimator 12 performs
this updating in a spectral domain such as a spectral domain also used for transform coding
within encoding engine 14. Moreover, the background noise estimatot 12 may perform the
updating based on an excitation or residual signal obtained as an intermediate result within
encoding engine l4 during, for example, transform coding a LPC-based filtered version of
the input signal rather than the audio signal as entering input 18 or as lossy coded into the
data stream. By doing so, a large amount of the useful signal component within the input
audio signal would already have been removed so that the detection of the noise
component is easier for the background noise estimatot 12. As the spectral domain, a
lapped transform domain such as an MDCT domain, or a filterbank domain such as a
complex valued filterbank domain such as an QMF domain may be used.
During the active phase 24, detector 16 is also continuously running to detect an entrance
of the inactive phase 28. The detector 16 may be embodied as a voice/sound activity
detector (VAD/SAD) or some other means which decides whether a useful signal
component is currently present within the input audio signal or not. A base criterion for
detector 16 in order to decide whether an active phase 24 continues could be checking
whether a low-pass filtered power of the input audio signal remains below a certain
threshold, assuming that an inactive phase is entered as soon as the threshold is exceeded.
Independent from the exact way the detector 16 performs the detection of the entrance of
the inactive phase 28 following the active phase 24,the detector 16 immediately informs
the other entities 12, 14 and 22 of the entrance of the inactive phase 28. In case of the
background noise estimator's continuous update of the parametric background noise
estimate during the active phase 24, the data stream 30 output at output 20 may be
immediately prevented from being further fed from encoding engine 14. Rather, the
background noise estimator 12 would, immediately upon being informed of the entrance of
the inactive phase 28, insert into the data stream 30 the information on the last update of
the parametric background noise estimate in the form of the SID frame 32.That is, SID
frame 32 could immediately follow the last frame of encoding engine which encodes the
frame of the audio signal concerning the time interval within which the detector 16
detected the inactive phase entrance.
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Normally, the background noise does not change very often. In most cases, the backgror-rnd
noise tends to be something invariant in time. Accordingly, aftet the background noise
estimator 12 inserted SID liame 32 immediately after the detector 16 detecting the
beginning of the inactive phase 28, any data stream transmission may be intemrpted so that
5 in this interruption phase 34, the data stream 30 does not consume any bitrate or merely a
minimum bitrate required for some transmission purposes. In order to maintain a minimunt
bitrate, background noise estimator 12 may intermittently repeat the output of SID 32'
However, despite the tendency of background noise to not change in time, it nevertheless
may happen that the background noise changes. For example, imagine a mobile phone user
leaving the car so that the background noise changes from motor noise to traffic noise
outside the car during the user phoning. In order to track such changes ofthe background
noise, the background noise estimator 12 may be configured to continuously survey the
background noise even during the inactive phase 28. Whenever the background noise
estimator 12 determines that the parametric background noise estimate changes by an
amount which exceeds some threshold, background estimator 12 may insert an updated
version of parametric background noise estimate into the data stream 20 via another SID
38, whereinafter another intemrption phase 40 may follow until, for example, another
active phase 42 starts as detected by detector 16 and so forth. Naturally, SID frames
revealing the currently updated parametric background noise estimate may alternatively or
additionally interspersed within the inactive phases in an intermediate manner independent
from changes in the parametric background noise estimate.
Obviously, the data stream 44 output by encoding engine 14 and indicated in Fig. I by use
of hatching, consumes more transmission bitrate than the data stream fragments 32 and 38
to be transmitted during the inactive phases 28 and accordingly the bitrate savings are
considerable.
Moreover, in case of the background noise estimator 12 being able to immediately start
with proceeding to further feed the data stream 30 by the above optional continuous
estimate update, it is not necessary to preliminarily continue transmitting the data stream
44 of encoding engine 14 beyond the inactive phase detection point in time 34, thereby
further reducing the overall consumed bitrate.
As will be explained in more detail below with regard to more specific embodiments, the
encoding engine 14 may be configured to, in encoding the input audio signal, predictively
code the input audio signal into linear prediction coefficients and an excitation signal with
transform coding the excitation signal and coding the linear prediction coefficients into the
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data stream 30 and 44, respectively. One possible implementation is shown in Fig. 2.
According to Fig. 2, the encoding engine 14 comprises a transformer 50, a frequency
domain noise shap er 52 and a quantizer 54 which are serially connected in the order of
their mentioning between an audio signal input 56 and a data stream output 58 of encoding
engine 14. Furlher, the encoding engine 14 of Fig. 2 comprises a linear prediction analysis
module 60 which is configured to determine linear prediction coeff,rcients from the audio
signal 56 by respective analysis windowing of portions of the audio signal and applying an
autocorrelation on the windowed portions, or determine an autocorrelation on the basis of
the transforms in the transform domain of the input audio signal as output by transformer
50 with using the power spectrum thereof and applying an inverse DFT onto so as to
determine the autocorrelation, with subsequently performing LPC estimation based on the
autocorrelation such as using a (Wiener-) Levinson-Durbin algorithm.
Based on the linear prediction coefficients determined by the linear prediction analysis
module 60, the data stream output at output 58 is fed with respective information on the
LPCs, and the frequency domain noise shaper is controlled so as to spectrally shape the
audio signal's spectrogram in accordance with a transfer function corresponding to the
transfer function of a linear prediction analysis filter determined by the linear prediction
coefficients output by module 60. A quantization of the LPCs for transmitting them in the
data stream may be performed in the LSP/LSF domain and using interpolation so as to
reduce the transmission rate compared to the analysis rate in the analyzer 60. Further, the
LPC to spectral weighting conversion performed in the FDNS may involve applying a
ODFT onto the LPCs and appliying the resulting weighting values onto the transformer's
spectra as divisor.
Quantizer 54 then quantizes the transform coefficients of the spectrally formed (flattened)
spectrogram. For example, the transformer 50 uses a lapped transform such as an MDCT in
order to transfer the audio signal from time domain to spectral domain, thereby obtaining
consecutive transforms corresponding to overlapping windowed portions of the input audio
signal which are then spectrally formed by the frequency domain noise shaper 52 by
weighting these transforms in accordance with the LP analysis filter's transfer function.
The shaped spectrogram may be interpreted as an excitation signal and as it is illustrated
by dashed arrow 62, the background noise estimator 12 may be configured to update the
parametric background noise estimate using this excitation signal. Altematively, as
indicated by dashed alrow 64, the background noise estimator 12 may use the lapped
transform representation as output by transformer 50 as a basis for the update directly, i.e.
without the frequency domain noise shaping by noise shaper 52.
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Further details regarding possible implementation of the elements shown in Figs' I to 2 ate
clerivable from the subsequently more detailed embodiments and it is noted that all of these
details are individually transferable to the elements of Figs' 1 and2'
Before, however, describing these more detailed embodiments, reference is made to Fig' 3,
which shows that additionally or alternatively, the parametric background noise estimate
r,rpdate may be performed at the decoder side.
The audio decoder 80 of Fig. 3 is configured to decode a data stream entering at an input
g2 ofdecoder g0 so as to reconstruct therefrom an audio signal to be output at an output 84
of decoder 80. The data stream comprises at least an active phase 86 followed by an
inactive phase 88. Internally, the audio decoder 80 comprises a background noise estimator
90, a decoding engine 92, a parametric random generator 94 and a background noise
generator 96. Decoding engine 92 is connected between input 82 and output 84 and
likewise, the serial connection of provider 90, background noise generator 96 and
parametric random generator 94 are connected between input 82 and output 84' The
decoder 92 is configured to reconstruct the audio signal from the data stream during the
active phase, so that the audio signal 98 as output at output 84 comprises noise and useful
sound in an appropriate quality.
The background noise estimator 90 is conf,tgured to determine a parametric background
noise estimate based on a spectral decomposition representation of the input audio signal
obtained from the data stream so that the parametric background noise estimate spectrally
describes the spectral envelope of background noise of the input audio signal' The
parametric random generator 94 and the background noise generator 96 ate configured to
reconstruct the audio signal during the inactive phase by controlling the parametric random
generator during the inactive phase with the parametric background noise estimate.
However, as indicated by dashed lines in Fig. 3, the audio decoder 80 may not comprise
the estimator 90. Rather, the data strearn may have, as indicated above, encoded therein a
parametric background noise estimate which spectrally describes the spectral envelope of
the background noise. In that case, the decoder 92 may be configured to reconstruct the
audio signal from the data stream during the active phase, while parametric random
generator 94 and background noise generator 96 cooperate so that generator 96 synthesizes
the audio signal during the inactive phase by controlling the parametric random generator
94 duripg the inactive phase 88 depending on the parametric background noise estimate.
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If, however, estimator 90 is present, decoder 80 of Fig. 3 could be informed on the
entrance 106 of the inactive phase 106 by way of the data stream 88 such as by use of a
starting inactivity flag. Then, decoder 92 could proceed to continue to decode a
preliminarily further fed portion 102 and background noise estimator could leam/estimate
the background noise u,ithin that preliminary time foltowing time instant 106' However' in
compliance with the above embodiments of Fig. I and2, it is possible that the background
noise estimator 90 is configured to continuously update the parametric background noise
estimate from the data stream during the active phase'
The background noise estimator 90 may not be connected to input 82 directly but via the
decoding engine 92 as illustrated by dashed line 100 so as to obtain from the decoding
engine 92 some reconstructed version of the audio signal. In principle, the background
noise estimator 90 may be configured to operate very similar to the background noise
estimator 12, besides the fact that the background noise estimator 90 has merely access to
the reconstructible version of the audio signal, i'e' including the loss caused by
quantization at the encoding side.
The parametric random generato t 94 may comprise one or more true or pseudo random
number generators, the sequence of values output by which may conform to a statistical
distribution which may be parametrically set via the background noise generator 96'
The background noise generator 96 is configured to synthesizethe audio signal 98 during
the inactive phase 88 by controlling the parametric random generatot 94 during the
inactive phase 88 depending on the parametric background noise estimate as obtained from
the background noise estimator 90. Although both entities 96 and 94 are shown to be
serially connected, the serial connection should not be interpreted as being limiting' The
generators 96 and 94 could be interlinked. In fact, generatorg4 could be interpreted to be
part of generator 96'
Thus, in accordance with an advantageous implementation of Fig' 3' the mode of operation
of the audio decoder g0 of Fig. 3 may be as foilows. During an active phase 86 input 82 is
continuously provided with a data stream portion 102 which is to be processed by decoding
engine 92 during the active phase 86. The data stream 104 entering at input 82 then stops
the transmission of data stream portion 102 dedicated for decoding engine 92 at some time
instant 106. That is, no further frame of data stream portion is available at time instant 106
for decoding by engine 92. The signalization of the entrance of the inactive phase 88 may
either be the disruption of the transmission of the data stream portion 102' or may be
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signaled by some information 108 arranged immediately at the beginning of the inactive
phase 88.
In any case, the entrance of the inactive phase 88 occurs very suddenly, but this is not a
problem since the background noise estimator 90 has continuously updated the parametric
background noise estimate during the active phase 86 on the basis of the data stream
portion 102. Due to this, the background noise estimator 90 is able to provide the
background noise generator 96 with the newest vqfsipn of the parametric background noise
estimate as soon as the inactive phase 88 starts at 106. Accordingly, from time instant 106
on, decoding engine 92 stops outputting any audio signal reconstruction as the decoding
engine 92 is not further fed with a data stream portion 102, but the parametric random
generator 94 is controlled by the background noise generator 96 in accordance with a
parametric background noise estimate such that an emulation of the background noise may
be output at output 84 immediately following time instant 106 so as to gaplessly follow the
reconstructed audio signal as output by decoding engine 92 up to time instant 106' Crossfading
may be used to transit from the last reconstructed frame of the active phase as
output by engine 92 to thebackground noise as determined by the recently updated version
of the parametric background noise estimate.
As the background noise estimator 90 is configured to continuously update the parametric
background noise estimate from the data stream 104 during the active phase 86, same may
be configured to distinguish between a noise component and a useful signal component
within the version of the audio signal as reconstructed from the data stream 104 in the
active phase 86 and to determine the parametric background noise estimate merely from
the noise component rather than the useful signal component' The way the background
noise estimator 90 performs this distinguishing/separation corresponds to the way outlined
above with respect to the background noise estimator 12. For example, the excitation or
residual signal intemally reconstructed from the data stream 104 within decoding engine
92may be used.
Similar to Fig. 2, Fig.4 shows a possible implementation for the decoding engine 92'
Accordingto Fig.4, the decoding engine 92 comprises an input 110 forreceivingthe data
stream portion 102 and an output 112 for outputting the reconstructed audio signal within
the active phase 86. Serially connected therebetween, the decoding engine 92 compiises a
dequantizer ll4, afrequency domain noise shaper 116 and an inverse transformer 118'
which are connected between input ll0 and output 112 in the order of their mentioning'
The data stream portion 102 arriving at input 110 comprises a transform coded version of
the excitation signal. i.e. transform coefltcient levels representing the same, which are fed
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to the input of dequantizer 114, as well as information on linear prediction coefficients'
which information is fed to the frequency domain noise shaper 116' The dequant\zer ll4
dequantizes the excitation signal's spectral replesentation and forwards same to the
frequency domain noise shaper 1i6 which, in turn, spectrally forms the spectrogram of the
excitation signal (along with the flat quantization noise) in accordance with a transfer
function which corresponds to a linear prediction synthesis filter, thereby forming the
quantization noise. In principle, FDNS I 16 of Fig' 4 acts similar to FDNS of Fig' 2: LPCs
are extracted from the data stream and then subjgpt.to LPC to spectral weight conversion
by, for example, applying an oDFT onto the extracted LPCs with then applying the
resulting spectral weightings onto the dequantized spectra inbound from dequantizet 714
as multiplicators. The retransformer 118 then transfers the thus obtained audio signal
reconstruction from the spectral domain to the time domain and outputs the reconstructed
audio signal thus obtained at output ll2. A lappod transform may be used by the inverse
transformerl18suchasbyanIMDCT.Asillustratedbydashedalrowl20'theexcitation
signal's spectrogram may be used by the background noise estimator 90 for the parametric
background noise update. Alternatively, the spectrogram of the audio signal itself may be
used as indicated by dashed anow 122'
With regard to Fig. 2 and 4 it should by noted that these embodiments for an
implementation of the encoding/decoding engines are not to be interpreted as restrictive'
Alternative embodiments are also feasible. Moreover' the encoding/decoding engines may
beofamulti-modecodectypewherethepartsofFig.2and4merelyassumeresponsibility
for encoding/decoding frames having a specific frame coding mode associate therewith'
whereas other frames are subject to other parts of the encoding/decoding engines not
shown in Fig. 2 and4. Such another frame coding mode could also be a predictive coding
mode using linear prediction coding for example, but with coding in the time-domain
rather than using transform coding'
Fig.5showsamoredetailedembodimentoftheencoderofFig.l.Inparticular,the
30backgroundnoiseestimator|2isshowninmoredetailinFig.5inaccordancewitha
specific embodiment'
InaccordancewithFig.5,thebackgroundnoiseestimatorl2comprisesatransformerl40,
anFDNS|42,anLPanatysismodule|44,anoiseestimator146,aparameterestimator
35 148, a stationarity measurer 150, and a quantizer 152' Some of the components justmentioned
may be partially or fully co-owned by encoding engine 14' For example'
transformer 140 and transformer 50 of Fig. z may be the suune, LP analysis modules 60 and
144 rnay be the same, FDNSs 52 and l42may be the same and/or quantizers 54 and 152
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may be implemented in one module.
Fig. 5 also shows a bitstream packager 154 which assumes a passive responsibility for the
operation of switch 22 inFig.l. In particular, the VAD as the detector 16 of encoder of
Fig. 5 is exemplarity called, simply decides as to which path should be taken, either the
path of the audio encoding 14 or the path of the background noise estimator 12' To be
more precise, encoding engine 14 and background noise estimator 12 are both connected in
parallel between input l8 and packager 154, wherein within background noise estimator
12, transformer 140, FDNS l42,LP analysis module 144, noise estimator 146, parameter
estimator 148, and quantizer 152 arc serially connected between input 18 and packager 154
(in the order of their mentioning), while LP analysis module 144 is connected between
input 18 and an LPC input of FDNS module 142 and a further input of quantizet 152,
respectively, and stationarity measurer 150 is additionally connected between LP analysis
module 144 and a control input of quantizer 152. The bitstream packager 154 simply
performs the packaging if it receives an input from any of the entities connected to its
inputs.
In the case of transmittin g zero frames, i.e. during the intemrption phase of the inactive
phase, the detector 16 informs the background noise estimator 12, in particular the
quantizer l52,to stop processing and to not send anything to the bitstream packager 154.
In accordance with Fig. 5, detector 16 may operate in the time and/or transforn/spectral
domain so as to detect active/inactive phases.
The mode of operation of the encoder of Fig. 5 is as follows. As will get clear, the encoder
of Fig. 5 is able to improve the quality of comfort noise such as stationary noise in general,
such as car noise, babble noise with many talkers, some musical instruments, and in
particular those which are rich in harmonics such as rain drops.
In particular, the encoder of Fig. 5 is to control a random generator at the decoding side so
as to excite transform coefficients such that the noise detected at the encoding side is
emulated. Accordingly, before discussing the functionality of the encoder of Fig. 5 further,
reference is briefly made to Fig. 6 showing a possible embodiment for a decoder which
would be able to emulate the comfort noise at the decoding side as instructed by the
encoder of Fig. 5. More generally, Fig. 6 shows a possible implementation of a decoder
fitting to the encoder of Fig. 1.
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In particular, the decoder of Fig.6 comprises a decoding engine 160 so as to decode the
data stream portion 44 during the active phases and a comfort noise generating part 162 for
generating the comfort noise based on the information 32 and 38 provided in the data
stream concerning the inactive phases 28. The comfort noise generating part 162 comprises
a parametric random generator 164, an FDNS 166 and an inverse transformer (or
s.v-nthesizer) 16g. Modules 164 to 168 are serially connected to each other so that at the
oLltput of synthesizer 168, the comfort noise results, which fills the gap between the
reconstructed audio signal as output by the decoding'engine 160 during the inactive phases
28 as discussed with respect to Fig. 1. The processors FDNS 166 and inverse transformer
168 may be part of the decoding engine 160. In particular, they may be the same as FDNS
1 16 and I l8 in Fig. 4, for examPle
The mode of operation and functionality of the individual modules of Fig. 5 and 6 will
become clearer from the following discussion'
In particular, the transformer 140 spectrally decomposes the input signal into a
spectrogram such as by using a lapped transform. A noise estimator 146 is configured to
determine noise parameters therefrom. Concurrently, the voice or sound activity detector
16 evaluates the features derived from the input signal so as to detect whether a transition
from an active phase to an inactive phase or vice versa takes place. These features used by
the detector 16 may be in the form of transient/onset detector, tonality measurement, and
LPC residual measurement. The transienVonset detector may be used to detect attack
(sudden increase of energy) or the beginning of active speech in a clean environment or
denoised signal; the tonality measurement may be used to distinguish useful background
noise such as siren, telephone ringing and music; LPC residual may be used to get an
indication of speech presence in the signal. Based on these features, the detector 16 can
roughly give an information whether the current frame can be classified for example, as
speech, silence, music, or noise'
While the noise estimator 146 may be responsible for distinguishing the noise within the
spectrogram from the useful signal component therein, such as proposed in [R' Martin'
Noise Power Spectral Density Estimation Based on Optimal Smoothing and Minimum
Statistics. 20011, parameter estimator 148 may be responsible for statistically analyzing the
noise components and determining parameters for each spectral component, for example'
based on the noise comPonent.
The noise estimator 146 may,for example, be configured to search for local minima in the
spectrogram and the parameter estimator 148 may be configured to determine the noise
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statistics at these portions assuming that the minima in the spectrogram are primarily an
attribute of the background noise rather than foreground sound.
As an intermediate note it is emphasized that it may also be possible to perform the
estimation by noise estimator without the FDNS 742 as the minima do also occur in the
non-shaped spectrum. Most of the description of Fig. 5 would remain the same'
Parameter quantizer 152, in turn, may be colfigured to parameterize the parameters
estimated by parameter estimator 148. For example, the parameters may describe a mean
amplitude and a first or higher order momentum of a distribution of the spectral values
within the spectrogram of the input signal as far as the noise component is concerned' In
order to save bitrate, the parameters may be forwarded to the data stream for insertion into
the same within SID frames in a spectral resolution lower than the spectral resolution
provided by transformer 140'
The stationarity measurer 150 may be configured to derive a measure of stationarity for the
noise signal. The parameter estimator 148 in turn may use the measure of stationarity so as
to decide whether or not a parameter update should be initiated by sending another SID
frame such as frame 38 in Fig. 1 or to influence the way the parameters are estimated'
Module 152 quantizes the parameters calculated by parameter estimator 148 and LP
analysis 144 and signals this to the decoding side' In particular, prior to quantizing'
spectral components may be grouped into groups' Such grouping may be selected in
accordance with psychoacoustical aspects such as conforming to the bark scale or the like'
The detector 16 informs the quantizer 152 whether the quantization is needed to be
performed or not. In case of no quantizationis needed, zero frames should follow'
When transferring the description onto a concrete scenario of switching from an active
phase to an inactive phase, then the modules of Fig' 5 act as follows'
During an active phase, encoding engine 14 keeps on coding the audio signal via packager
into bitstream. The encoding may be performed frame-wise. Each frame of the data stream
may represent one time portion/interval of the audio signal. The audio encoder 14 may be
configured to encode all frames using LPC coding' The audio encoder 14 may be
configured to encode some frames as described with respect to Fig' 2, called TCX frame
coding mode, fbr example. Remaining ones may be encoded using code-excited linear
prediction (CELP) coding such as ACELP coding mode, for example' That is' portion 44
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of the data stream may comprise a continuous update of LPC coefficients using some LPC
transmission rate which rnay be equal to or greater than the frame rate'
In parallel, noise estim ator 146 inspects the LPC flattened (LPC analysis filtered) spectra
so as to identify the minima kn,in within the TCX sprectrogram represented by the sequence
of these spectra. Of course, these minima may vary in time t, i.e. k.;n(t). Nevertheless, the
minima may form traces in the spectrogram output by FDNS 142, arrd thus, for each
consecutive spectrum i at time ti, the minima may be associatable with the minima at the
preceding and succeeding spectrum, respectively'
The parameter estimator then derives background noise estimate pafameters therefrom
such as, for example, a central tendency (mean average, median or the like) m and/or
dispersion (standard deviation, variance or the like) d for different spectral components or
bands. The derivation may involve a statistical analysis of the consecutive spectral
coefficients of the spectra of the spectrogram at the minima, thereby yielding m and d for
each minimum at kn.'in. Interpolation along the spectral dimension between the
aforementioned spectrum minima may be performed so as to obtain m and d for other
predetermined spectral components or bands. The spectral resolution for the derivation
and/or interpolation of the central tendency (mean average) and the derivation of the
dispersion (standard deviation, variance or the like) may differ.
The just mentioned parameters are continuously updated per spectrum output by FDNS
l42,for example.
As soon as detector 16 detects the entrance of an inactive phase, detector 16 may inform
engine 14 accordingly so that no further active frames are forwarded to packager 154.
However, the quantizer 152 outputs the just-mentioned statistical noise parameters in a
f,rrst SID frame within the inactive phase, instead. The first SID frame may or may not
comprise an update of the LPCs. If an LPC update is present, same may be conveyed
within the data stream in the SID frame 32 tn the format used in portion 44, i.e' during
active phase, such as using quantization in the LSF/LSP domain, or differently, such as
using spectral weightings corresponding to the LPC analysis or LPC synthesis filter's
transfer function such as those which would have been applied by FDNS 142 within the
framework of encoding engine 14 in proceeding with an active phase.
During the inactive phase, noise estimator 146, parameter estimator 148 and stationarity
tneasurer 150 keep on co-operating so as to keep the decoding side updated on changes in
the background noise. In particular, measurer 150 checks the spectral weighting defined by
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the LPCs, so as to identify changes and inform the estimator 148 when an SID frame
should be sent to the decoder. For example, the measurer 150 could activate estimator
accordingly whenever the afore-mentioned measure of stationarity indicates a degree of
fluctuation in the LPCs which exceeds a certain amount. Additionally or alternatively,
estimator could be triggered to send the updated parameters an a regular basis. Between
these SID update frames 40, nothing would be send in the data streams, i.e. "zero frames".
At the decoder side, during the active phase., .the decoding engine 160 assumes
responsibility for reconstructing the audio signal. As soon as the inactive phase starts, the
adaptive parameter random generator 164 uses the dequantized random generator
parameters sent during the inactive phase within the data stream from parameter quantizer
150 to generate random spectral components, thereby forming a random spectrogram
which is spectrally formed within the spectral energy processor 166 with the synthesizer
168 then performing a retransformation from the spectral domain into the time domain. For
spectral formation within FDNS 166, either the most recent LPC coefficients from the
most recent active frames may be used or the spectral weighting to be applied by FDNS
166 may be derived therefrom by extrapolation, or the SID frame 32 itself may convey the
information. By this measure, at the beginning of the inactive phase, the FDNS 166
continues to spectrally weight the inbound spectrum in accordance with a transfer function
of an LPC synthesis filter, with the LPS defining the LPC synthesis filter being derived
from the active data portion 44 or SID frame 32. However, with the beginning of the
inactive phase, the spectrum to be shaped by FDNS 166 is the randomly generated
spectrum rather than a transform coded on as in case of TCX frame coding mode.
Moreover, the spectral shaping applied at 166 is merely discontinuously updated by use of
the SID frames 38. An interpolation or fading could be performed to gradually switch from
one spectral shaping definition to the next during the intemrption phases 36.
As shown in Fig. 6, the adaptive parametric random generator as 164 may additionally,
optionally, use the dequantized transform coefficients as contained within the most recent
portions of the last active phase in the data stream, namely within data stream portion 44
immediately before the entrance of the inactive phase. For example, the usage may be thus
that a smooth transition is performed from the spectrogram within the active phase to the
random spectrogram within the inactive phase.
Briefly referring back to Fig. 1 and 3, it follows from the embodiments of Fig. 5 and 6 (and
the subsequently explained Fig. 7) that the parametric background noise estimate as
generated within encoder and/or decoder, may comprise statistical information on a
distribution of temporally consecutive spectral values for distinct spectral portions such as
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bark bands or different spectral components. For each such spectral portion, for example,
the statistical information may contain a dispersion measure. The dispersion measure
would, accordingly, be defined in the spectral information in a spectrally resolved manner,
namely sampled at/for the spectral portions. The spectral resolution, i.e. the number of
measures for dispersion and central tendency spread along the spectral axis, may differ
between, for example, dispersion measure and the optionally present mean or central
tendency measure. The statistical information is contained within the SID frames. It may
refer to a shaped spectrum such as the LPC analysis filtered (i.e. LPC flattened) spectrum
such as shaped MDCT spectrum which enables synthesis at by synthesizing a random
spectrum in accordance with the statistical spectrum and de-shaping same in accordance
with a LPC synthesis filter's transfer function. In that case, the spectral shaping
information may be present within the SID frames, although it may be left away in the first
SID frame 32, for example. However, as will be shown later, this statistical information
'
may alternatively refer to a non-shaped spectrum. Moreover, instead of using a real valued
spectrum representation such as an MDCT, a complex valued filterbank spectrum such as
QMF spectrum of the audio signal may be used. For example, the QMF spectrum of the
audio signal in non-shaped from may be used and statistically described by the statistical
information in which case there is no spectral shaping other than contained within the
statistical information itself.
Similar to the relationship between the embodiment of Fig. 3 relative to the embodiment of
Fig. 1, Frg.7 shows a possible implementation of the decoder of Fig. 3. As is shown by use
of the same reference signs as in Fig. 5, the decoder of Fig. 7 may comprise a noise
estimator 146, a parameter estimator 148 and a stationarity measurer 150, which operate
like the same elements in Fig. 5, with the noise estimator 146 of Fig. 7, however, operating
on the transmitted and dequantized spectrogram such as 120 or 122 in Fig. 4. The
parameter estimator 146 then operates like the one discussed in Fig.5. The same applies
with regard to the stationarity measurer 148, which operates on the energy and spectral
values or LPC data revealing the temporal development of the LPC analysis filter's (or
LPC synthesis filter's) spectrum as transmitted and dequantized via/from the data stream
during the active phase.
While elements 146, 148 and 150 act as the background noise estimator 90 of Fig. 3, the
decoder of Fig. 7 also comprises an adaptive parametric random generator 164 and an
FDNS 166 as well as an inverse transformer 168 and they are connected in series to each
other like in Fig. 6, so as to output the comfort noise at the output of synthesizer 168.
Modules 164,166, and 168 act as the backround noise generator 96 of Fig. 3 with module
164 assuming responsibility for the functionality of the parametric random generator 94.
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The adaptive parametric random generator 94 or 164 outputs randomly generated spectral
components of the spectrogram in accordance with the parameters determined by
parameter estimator 148 which, in turn, is triggered using the stationarity measure output
by statioparity measurer 150. Processor 166 then spectrally shapes the thus generated
spectrogram with the inverse transformer 168 then performing the transition from the
spectral domain to the time domain. Note that when during inactive phase 88 the decoder is
receiving the information 108, the background noise estimator 90 is performing an update
of the noise estimates followed by some means of interpolation. Otherwise, if zeto frames
are received, it will simply do processing such as interpolation and/or fading.
Summarizing Figs. 5 to 7, these embodiments show that it is technically possible to apply a
controlled random generator 164 to excite the TCX coefficients, which can be real values
such in MDCT or complex values as in FFT. It might also be advantageous to apply the
random generator 164 on groups of coefficients usually achieved through filterbanks.
The random generator 164 is preferably controlled such that same models the type of noise
as closely as possible. This could be accomplished if the target noise is known in advance.
Some applications may allow this. In many realistic applications where a subject may
encounter different types of noise, an adaptive method is required as shown in Figs. 5 to 7.
Accordingly, an adaptive parameter random generator 164 is used which could be briefly
defined as g : f (x), where x : (xr, xz, ...) is a set of random generator parameters as
provided by parameter estimators 146 and 150, respectively.
To make the parameter random generator adaptive, the random generator parameter
estimator 146 adequately controls the random generator. Bias compensation may be
included in order to compensate for the cases where the data is deemed to be statistically
insufficient. This is done to generate a statistically matched model of the noise based on
the past frames and it will always update the estimated parameters. An example is given
rvhere the random generator 164 is supposed to generate a Gaussian noise. In this case, for
example, only the mean and variance parameters may be needed and a bias can be
calculated and applied to those parameters. A more advanced rnethod can handle any type
of noise or distribution and the parameters are not necessarily the moments of a
distribution.
For the non-stationary noise,
pararnetric random generator
llleasurer 148 can be derived
it needs to have a stationarity measure and a less adaptive
can then be used. The stationarity measure determined by
from the spectral shape of the input signal using various
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methods like, for example, the ltakura distance measure, the Kullback-Leibler distance
measure, etc.
To handle the discontinuous nature of noise updates sent through SID frames such as
illustrated by 38 in Fig. 1, additional information is usually being sent such as the energy
and spectral shape of the noise. This information is useful for generating the noise in the
decoder having a smooth transition even during a period of discontinuity within the
inactive phase. Finalty, various smoothing or fi{tering techniques can be applied to help
improve the quality of the comfort noise emulator'
As already noted above, Figs. 5 and 6 on the one hand and Fig' 7 on the other hand belong
to different scenarios. In one scenario corresponding to Figs. 5 and 6, parametric
background noise estimation is done in the encoder based on the processed input signal and
later on the parameters are transmitted to the decoder. Fig. 7 corresponds to the other
scenario where the decoder can take care of the parametric background noise estimate
based on the past received frames within the active phase. The use of a voice/signal
activity detector or noise estimator can be benef,rcial to help extracting noise components
even during active speech, for example.
Among the scenarios shown in Figs. 5 to 7, the scenario of Fig' 7 may be preferred as this
scenario results in a lower bitrate being transmitted. The scenario of Figs' 5 and 6,
however, has the advantage of having a more accurate noise estimate available'
All of the above embodiments could be combined with bandwidth extension techniques
such as spectral band replication (SBR), although bandwidth extension in general may be
used.
To illustrate this, see Fig. 8. Fig. 8 shows modules by which the encoders of Figs' I and 5
could be extended to perform parametric coding with regard to a higher frequency portion
of the input signal. In particular, in accordance with Fig. 8 a time domain input audio
signal is spectrally decomposed by an analysis filterbank 200 such as a QMF analysis
filterbank as shown in Fig. 8. The above embodiments of Figs. I and 5 would then be
applied only onto a lower frequency portion of the spectral decomposition generated by
filterbank 200. In order to convey information on the higher frequency portion to the
decoder side, parametric coding is also used. To this end, a regular spectral band
replication encoder ZO2 is configured to parameterize the higher frequency portion during
active phases and feed infornration thereon in the form of spectral band replication
information w,ithin the data stream to the decoding side. A switch 204 may be provided
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between the output of QMF filterbank 200 and the input of spectral band replication
encoder 202 to connect the output of filterbank 200 with an input of a spectral band
replication encoder 206 connected in parallel to encoder 202 so as to assume responsibility
for the bandwidth extension during inactive phases. That is, switch 204 may be controlled
like switch 22 rnFig. 1. As will be outlined in more detail below, the spectral band
replication encoder module 206 may be configured to operate similar to spectral band
replication encoder 202: both may be configured to parameterize the spectral envelope of
the input audio signal within the higher frequency portion, i.e. the remaining higher
frequency portion not subject to core coding by the encoding engine, for example'
However, the spectral band replication encoder module 206 may use a minimum
time/frequency resolution at which the spectral envelope is parameterized and conveyed
within the data stream, whereas spectral band replication encoder 202 may be configured
to adapt the time/frequency resolution to the input audio signal such as depending on the
occurrences of transients within the audio signal.
Fig. 9 shows a possible implementation of the bandwidth extension encoding module 206.
A time/frequency grid setter 208, an energy calculator 210 and an energy encoder 212 are
serially connected to each other between an input and an output of encoding module 206.
The time/frequency grid setter 208 may be configured to set the time/frequency resolution
at which the envelope of the higher frequency portion is determined. For example, a
minimum allowed time/frequency resolution is continuously used by encoding module
206. The energy calculator 210 may then determine the energy of the higher frequency
portion of the spectrogram output by filter bank 200 within the higher frequency portion in
time/frequency tiles corresponding to'the time/frequency resolution, and the energy
encoder 212 may use entropy coding, for example, in order to insert the energies calculated
by calculator 210 into the data stream 40 (see Fig. 1) during the inactive phases such as
within SID frames, such as SID frame 38.
It should be noted that the bandwidth extension information generated in accordance with
the embodiments of Figs. 8 and 9 may also be used in connection with using a decoder in
accordance with any of the embodiments outlined above, such as Figs. 3, 4 and7.
Thus, Figs. 8 and 9 make it clear that the comfort noise generation as explained with
respect to Figs. 1 to 7 may also be used in connection with spectral band replication. For
example, the audio encoders and decoders described above may operate in different
operating modes, among which some may comprise spectral band replication and some
may not. Super wideband operating modes could, for example, involve spectral band
replication. In any case, the above embodiments of Figs. 1 to 7 showing examples for
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generating comfort noise may be combined with bandwidth extension techniques in the
manner described with respect to Figs. 8 and 9. The spectral band replication encoding
module 206 being responsible for bandwidth extension during inactive phases may be
configured to operate on a very low time and frequency resolution. Compared to the
regular spectral band replication processing, encoder 206 may operate at a different
frequency resolution which entails an additional frequency band table with very low
frequency resolution along with IIR smoothing filters in the decoder for every comfort
noise generating scale factor band which interpolates the energy scale factors applied in the
envelope adjuster during the inactive phases. As just mentioned, the time/frequency grid
may be configured to correspond to a lowest possible time resolution.
That is, the bandwidth extension coding may be performed differently in the QMF or
spectral domain depending on the silence or active phase being present. In the active phase,
i.e. during active frames, regular SBR encoding is carried out by the encodet 202, resulting
in a normal SBR data stream which accompanies data streams 44 and 102, respectively. In
inactive phases or during frames classified as SID frames, only information about the
spectral envelope, represented as energy scale factors, may be extracted by application of a
time/frequency grid which exhibits a very low frequency resolution, and for example the
lowest possible time resolution. The resulting scale factors might be efficiently coded by
encoder 212 and written to the data stream. In zero frames or during intemrption phases
36, no side information may be written to the data stream by the spectral band replication
encoding module 206, andtherefore no energy calculation may be carried out by calculator
2t0.
In conformity with Fig. 8, Fig. 10 shows a possible extension of the decoder embodiments
of Figs. 3 and 7 to bandwidth extension coding techniques. To be more precise, Fig. l0
shows a possible embodiment of an audio decoder in accordance with the present
application. A core decoder 92 is connected in parallel to a comfort noise generator, the
comfort noise generator being indicated with reference sign 220 and comprising, for
example, the noise generation module 162 or modules 90,94 and 96 of Fig. 3. A switch
222 is shown as distributing the frames within data streams 104 and 30, respectively, onto
the core decoder 92 or comfort noise generator 220 depending on the frame type, namely
whether the frame concerns or belongs to an active phase, or concems or belongs to an
inactive phase such as SID frames or zero frames concerning intemrption phases. The
outputs of core decoder 92 and comfort noise generator 220 are connected to an input of a
spectral bandwidth extension decoder 224, the output of which reveals the reconstructed
audio signal.
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Fig. 1 I shows a more detailed embodiment of a possible implementation of the bandwidth
extension decoder224.
As shown in Fig. I l, the bandwidth extension decoder 224 in accordance with the
embodiment of Fig. 11 comprises an input 226 for receiving the time domain
reconstruction of the low frequency portion of the complete audio signal to be
reconstructed. It is input226 which connects the bandwidth extension decoder 224 w:ththe
outputs of the core decod er 92 and the comfort noise generator 220 so that the time domain
input at input 226 may either be the reconstructed lower frequency portion of an audio
signal comprising both noise and useful component, or the comfort noise generated for
bridging the time between the active phases.
As in accordance with the embodiment of Fig. I I the bandwidth extension decoder 224 ts
constructed to perform a spectral bandwidth replication, the decoder 224 is called SBR
decoder in the following. With respect to Figs. 8 to 10, however, it is emphasized that these
embodiments are not restricted to spectral bandwidth replication. Rather, a more general,
alternative way of bandwidth extension may be used with regard to these embodiments as
well.
Further, the SBR decoder 224 of Fig. I I comprises a time-domain output 228 for
outputting the finally reconstructed audio signal, i.e. either in active phases or inactive
phases. Between inpttt 226 and output 228, the SBR decoder 224 comprises - serially
connected in the order of their mentioning - a spectral decomposer 230 which may be, as
shown in Fig. 11, an analysis filterbank such as a QMF analysis filterbank, an HF
generator 232, an envelope adjuster 234 and a spectral-to-time domain converter 236
which may be, as shown in Fig. 11, embodied as a synthesis filterbank such as a QMF
sy'nthesis filterbank.
Modules 230 to 236 operate as follows. Spectral decomposer 230 spectrally decomposes
the time domain input signal so as to obtain a reconstructed low frequency portion. The HF
generator 232 generates a high frequency replica portion based on the reconstructed low
frequency portion and the envelope adjuster 234 spectrally forms or shapes the high
frequency replica using a representation of a spectral envelope of the high frequency
porlion as conveyed via the SBR data stream portion and provided by modules not yet
discussed but shown in Fig. 11 above the envelope adjuster 234. Thus, envelope adjuster
234 adjusts the envelope of the high frequency replica portion in accordance with the
tirne/frequency grid representation of the transmitted high frequency envelope, and
fbrrvards the thus obtained high frequency portion to the spectral-to-temporal domain
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converter 236 for a conversion of the whole frequency spectrum, i'e' spectrally formed
high frequency portion along with the reconstructed low frequency portion, to a
reconstructed time domain signal at output 228.
As already mentioned above with respect to Figs. 8 to 10, the high frequency portion
spectral envelope may be conveyed within the data stream in the form of energy scale
f-actors and the SBR deco der 224 comprises an input 238 in order to receive this
infonnation on the high frequency portions spectral-envelope. As shown in Fig' 11, in the
case of active phases, i.e. active frames present in the data stream during active phases'
inputs 238 may be directly connected to the spectral envelope input of the envelope
adjuster 234 via a respective switch 240. However, the SBR decoder 224 additionally
comprises a scale factor combiner 242, a scale factor data storc 244, an interpolation
filtering unit246 such as an IIR filtering unit, and a gain adjuster 248. Modules242,244,
246 and248 are serially connected to each other between 238 and the spectral envelope
input of envelope adjuster 234 with switch 240 being connected between gain adjuster 248
and envelope adjuster 234 and a further switch 250 being connected between scale factor
data store 244 and filtering unit246. Switch 250 is configured to either connect this scale
factor data store 244 withthe input of filtering unit246, or a scale factor data restoret 252.
ln case of SID frames during inactive phases - and optionally in cases of active frames for
which a very coarse representation of the high frequency portion spectral envelope is
acceptable - switches 250 and 240 connect the sequence of modules 242 to 248 between
input 238 and envelope adjuster 234. The scale factor combiner 242 adapts the frequency
resolution at which the high frequency portions spectral envelope has been transmitted via
the data srream to the resolution, which envelope adjuster 234 expects receiving and a
scale factor data store 244 stores the resulting spectral envelope until a next update. The
filtering unit 246 filters the spectral envelope in time and/or spectral dimension and the
gain adj ust er 248 adapts the gain of the high frequency portion's spectral envelope. To that
end, gain adjuster may combine the envelope data as obtained by unit 246 with the actual
envelope as derivable from the QMF filterbank output. The scale factor data restorer 252
reproduces the scale factor data representing the spectral envelope within intemrption
phases or zero frames as stored by the scale factor stote244.
Thus, at the decoder side the following processing may be carried out. In active frames or
during active phases, regular spectral band replication processing may be applied. During
these active periods, the scale factors from the data stream, which are typically available
for a higher number of scale factor bands as compared to comfort noise generating
processing, are converted to the comfort noise generating frequency resolution by the scale
t-actor combiner 242. The scale factor combiner combines the scale factors for the higher
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frequency resolution to result in a number of scale factors compliant to CNG by exploiting
common frequency band borders of the different frequency band tables. The resulting scale
factor values at the output of the scale factor combining unit242 are stored fbr the reuse in
zero frames and later reproduction by restorer 252 and are subsequently used for updating
the filtering unit 246 for the CNG operating mode. In SID frames, a modified SBR data
stream reader is applied which extracts the scale factor information from the data stream.
The remaining configuration of the SBR processing is initialized with predefined values,
the time/frequency grid is initialized to the same.time/frequency resolution used in the
encoder. The extracted scale factors are fed into filtering unit 246, where, for example, one
IIR smoothing filter interpolates the progression of the energy for one low resolution scale
factor band over time. In case of zero frames, no payload is read from the bitstream and the
SBR configuration including the time/frequency grid is the same as is used in SID frames.
In zero frames, the smoothing filters in filtering wit 246 are fed with a scale factor value
output from the scale factor combining unit 242 whichhave been stored in the last frame
containing valid scale factor information. In case the current frame is classihed as an
inactive frame or SID frame, the comfort noise is generated in TCX domain and
transformed back to the time domain. Subsequently, the time domain signal containing the
comfort noise is fed into the QMF analysis filterbank 230 of the SBR modu\e2}4.In QMF
domain, bandwidth extension of the comfort noise is performed by means of copy-up
transposition within HF generator 232 and finally the spectral envelope of the artificially
created high frequency part is adjusted by application of energy scale factor information in
the envelope adjuster 234. These energy scale factors are obtained by the output of the
filtering unit 246 and are scaled by the gain adjustment unit 248 prior to application in the
envelope adjuster 234.In this gain adjustment wit248, a gain value for scaling the scale
factors is calculated and applied in order to compensate for huge energy differences at the
border between the low frequency portion and the high frequency content of the signal.
The embodiments described above are commonly used in the embodiments of Figs. 12 and
13. Fig. 12 shows an embodiment of an audio encoder according to an embodiment of the
present application, and Fig. 13 shows an embodiment of an audio decoder. Details
disclosed with regard to these figures shall equally apply to the previously mentioned
elements individually.
The audio encoder of Fig. 12 comprises a QMF analysis filterbank 200 for spectrally
decomposing an input audio signal. A detector 270 and a noise estimator 262 are
connected to an output of QMF analysis filterbank 200. Noise estimator 262 assumes
responsibility for the functionality of background noise estimator 12. During active phases,
the QMF spectra from QMF analysis filterbank are processed by a parallel connection of a
spectral band replication parameter estimator 260 followed by some SBR encoder 264 on
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the one hand, and a concatenation of a QMF synthesis filterbank 272 followed by a core
encoder 14 on the other hapd. Both parallel paths are connected to a respective input of
bitstream packager 266. Incase of outputting SID frames, SID frame encoder 274 rcceives
the data fiom the noise estim atot 262 and outputs the SID frames to bitstream packager
266.
The spectral bandwidth extension data output by estimator 260 describe the spectral
envelope of the high frequency portion of the spectrogram or spectrum output by the QMF
analysis filterbank 200, which is then encoded, such as by entropy coding' by SBR encoder
264 . Datastream multiplexet 266 inserts the spectral bandwidth extension data in active
phases into the data stream output at an output 268 of the multiplexet 266'
Detector 270 detects whether currently an active or inactive phase is active' Based on this
detection, an active frame, an SID frame or a zeto frame, i'e' inactive frame' is to currently
be output. In other words, module 270 decides whether an active phase or an inactive
phase is active and if the inactive phase is active, whether or not an SID frame is to be
output. The decisions are indicated in Fig. 12 using I for zero frames' A for active frames'
and S for SID frames. A frames which correspond to time intervals of the input signal
where the active phase is present are also forwarded to the concatenation of the QMF
synthesis filterbank 272 andthe core encoder 14' The QMF synthesis filterbank 272has a
lower frequency resolution or operates at a lower number of QMF subbands when
compared to QMF analysis filterbank 200 so as to achieve by way of the subband number
ratio a corresponding downsampling rate in transferring the active frame portions of the
input signal to the time domain again. In particular, the QMF synthesis filterbank 272 ts
applied to the lower frequency portions or lower frequency subbands of the QMF analysis
filterbank spectrogram within the active frames' The core coder 14 thus receives a
downsampled version of the input signal, which thus covers merely a lower frequency
portion of the original input signal input into QMF analysis filterbank 200' The remaining
higherfrequencyportionisparametricallycodedbymodules260afi264,
SIDframes(or,tobemoreprecise,theinformationtobeconveyedbysame)areforwarded
to SID encoder 274, which assumes responsibility for the functionalities of module 152 of
Fig.5,forexample.Theonlydifference:module262operatesonthespectrumofinput
signal directly - without LPC shaping' Moreover, as the QMF analysis filtering is used' the
operation of module 262 isindependent from the frame mode chosen by the core coder or
the spectral bandwidth extension option being applied or not' The functionalities of module
148 and 150 of Fig' 5 may be implemented within module 274'
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Multiplexer 266 multiplexes the respective encoded information into the data stream at
output 268.
The audio decoder of Fig. 13 is able to operate on a data stream as output by the encoder of
Fig. 12. That is, a module 280 is conf,rgured to receive the data stream and to classify the
frames within the data stream into active frames, SID frames and zeto frames, i.e. a lack of
any frame in the data stream, for example. Active frames are forwarded to a concatenation
of a core decoder 92, a QMF analysis filterbank-2&2 and a spectral bandwidth extension
module 284. Optionally, a noise estimator 286 is connected to QMF analysis filterbank's
output. The noise estimator 286 may operate like, and may assume responsibility for the
functionalities of, the background noise estimator 90 of Fig. 3, for example, with the
exception that the noise estimator operates on the un-shaped spectra rather than the
excitation spectra. The concatenation of modules 92,282 and 284 is connected to an input
of a eMF synthesis filterbank 288. SID frames are forwarded to an SID frame decoder 290
which assumes responsibility for the functionality of the background noise generator 96,of
Fig. 3, for example. A comfort noise generating parameter updater 292 is fed by the
information from decod er 290 and noise estimator 286 with this updater 292 steering the
random generator 294, which assumes responsibility for the parametric random generators
functionality of Fig. 3. As inactive of zero frames are missing, they do not have to be
forwarded anywhere, but they trigger another random generation cycle of random
generator 294. The output of random generator 294 is connected to QMF synthesis
filterbank 288, the output of which reveals the reconstructed audio signal in silence and
active phases in time domain.
Thus, during active phases, the core decoder 92 reconstructs the low-frequency portion of
the audio signal including both noise and useful signal components' The QMF analysis
filterbank 282 spectrally decomposes the reconstructed signal and the spectral bandwidth
extension module 284 uses spectral bandwidth extension information within the data
stream and active frames, respectively, in order to add the high frequency portion' The
noise estimator 286, if present, performs the noise estimation based on a spectrum portion
as reconstructed by the core decoder, i.e. the low frequency portion. In inactive phases, the
SID frames convey information parametrically describing the background noise estimate
derived by the noise estim ation 262 at the encoder side. The parameter updater 292 may
primarily use the encoder information in order to update its parametric background noise
estimate, using the information provided by the noise estimator 286 primarily as a fallback
position in case of transmission loss concerning SID frames' The QMF synthesis filterbank
288 converts the spectrally decomposed signal as output by the spectral band replication
rnodule 284 inactive phases and the comfort noise generated signal spectrum in the time
a
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clomain. Thus, Figs. l2 and l3 make it clear that a QMF filterbank framework may be used
as a basis for QMF-based comfort noise generation' The QMF framework provides a
convenient way to resample the input signal down to a core-coder sampling rate in the
encoder, or to upsample the core-decoder output signal of core decoder 92 at the decoder
side using the QMF synthesis hlterbank 288. At the same time, the QMF framework can
also be used in combination with bandwidth extension to extract and process the high
fiequency components of the signal which are left over by the core coder and core decoder
rnodules 14 and 92. Accordingly, the QMF hlterbank can offer a common framework for
various signal processing tools. In accordance with the embodiments of Figs' 12 and 13'
comfort noise generation is successfully included into this framework'
In particular, in accordance with the embodiments of Figs' 12 and 13' it may be seen that it
is possible to generate comfort noise at the decoder side after the QMF analysis' but before
the QMF synthesis by applying a random generator 294 to excite the real and imaginary
parts of each QMF coefficient of the QMF synthesis filterbank 288, for example' The
amplitude of the random sequences are, for example, individually computed in each QMF
band such that the spectrum of the generated comfort noise resembles the spectrum of the
actual input background noise signal. This can be achieved in each QMF band using a
noise estimator after the eMF analysis at the encoding side. These parameters can then be
transmitted through the SID frames to update the amplitude of the random sequences
applied in each QMF band at the decoder side'
Ideally, note that the noise estimation 262 app]lied at the encoder side should be able to
operateduringbothinactive(i.e.,noise-only)andactiveperiods(typicallycontainingnoisy
25 speech) so that the comfort noise parameters can be updated immediately at the end of
eachactiveperiod.tnaddition,noiseestimationmightbeusedatthedecodersideaswell.
Since noise-only frames are discarded in a DTX-based coding/decoding system' the noise
estimationatthedecodersideisfavorablyabletooperateonnoisyspeechcontents.The
advantageofperformingthenoiseestimationatthedecoderside,inadditiontotheencoder
30side,isthatthespectralshapeofthecomfortnoisecanbeupdatedevenwhenthepacket
transmissionfromtheencodertothedecoderfailsforthefirstSlDframe(s)followinga
period of activitY'
Thenoiseestimationshouldbeabletoaccuratelyandrapidlyfollowvariationsofthe
35 background noise,s spectral content and idearty it shourd be able to perform during both
activeandinactiveframes,asstatedabove.onewaytoachievethesegoalsistotrackthe
minima taken in each band by the power spectrum using a sliding window of finite length'
as proposed in [R. Martin, Noise Power Spectral Density Estimation Based on optimal
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Smoothing and Minimum Statistics, 2001 l. The idea behind it is that the power of a noisyspeech
spectrum frequently decays to the power of the background noise, e'g., between
words or syllables. Tracking the minimum of the power spectrum provides therefore an
estimate of the noise floor in each band, even'during speech activity. However, these noise
floors are underestimated in general. Furthermore, they do not allow to capture quick
fluctuations of the spectral powers, especially sudden energy increases.
Nevertheless, the noise floor computed as desc_rib.ed above in each band provides very
useful side-information to apply a second stage of noise estimation. In fact, we can expect
the porver of a noisy spectrum to be close to the estimated noise floor during inactivity,
whereas the spectral power will be far above the noise floor during activity. The noise
floors computed separately in each band can hence be used as rough activity detectors for
each band. Based on this knowledge, the background noise power can be easily estimated
as a recursively smoothed version of the power spectrum as follows:
o,ur{nt,ir) = fi{rn, k) ' o're(na - [,li) + (1- f(m, k]] 'ox?(rn,lcJ ,
where a*a{tn,J*) denotes the power spectral density of the input signal at the frame m and
band Ic, rs3{nr,ftJ refers the noise power estimate, and f(rru,lc} is a forgetting factor
(necessarily between 0 and 1) controlling the amount of smoothing for each band and each
frame separately. Using the noise floor information to reflect the activity status, it should
take a small value during inactive periods (i.e., when the power spectrum is close to the
noise floor), whereas a high value should be chosen to appty more smoothing (ideally
keeping as!(tn,k) constant) during active frames. To achieve this, a soft decision may be
made by computing the forgetting factors as follows:
r'ax3{lrlk) .F(m,lc)-1-e-ntffim-'J.
where a,.ns: is the noise floor power and a is a control parameter" A higher value for a
results in larger forgetting factors and hence causes overall more smoothing.
Thus, a Comfort Noise Generation (CNG) concept has been described where the artificial
noise is produced at the decoder side in a transform domain. The above embodiments can
35 be applied in combination with virtually any type of spectro-temporal analysis tool (i.e', a
transform or filterbank) decomposing a time-domain signal into multiple spectral bands.
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Again, it should be noted that the use of the spectral domain alone provides a more precise
estimate of the background noise and achieves advantages without using the above
possibility of continuously updating the estimate during active phases' Accordingly' some
further embodiments differ from the above embodiments by not using this feature of
continuous update of the parametric background noise estimate. But these alternative
embodiments use the spectral domain so as to parametrically determine the noise estimate'
Accordingly, in a further embodiment, the bu"kg.ound noise estimator 12 may be
configured to determine a parametric background noise estimate based on a spectral
decomposition representation of an input audio signal so that the parametric background
noise estimate spectrally describes a spectral envelope of a background noise of the input
audio signal. The determination may be commenced upon entering the inactive phase' or
the above advantages may be co-used, and the determination may continuously performed
during the active phases to update the estimate for immediate use upon entering the
inactive phase. The encoder 14 encodes the input audio signal into a data stream during the
active phase and a detector 16 may be configured to detect an entrance of an inactive phase
following the active phase based on the input signal' The encoder may be further
configured to encode into the data stream the parametric background noise estimate' The
background noise estimator may be configured to perform the determining the parametric
background noise estimate in the active phase and with distinguishing between a noise
component and a useful signal component within the spectral decomposition representation
of the input audio signal and to determine the parametric background noise estimate
merely from the noise component. [n another embodiment the encoder may be configured
to, in encoding the input audio signal, predictively code the input audio signal into linear
prediction coefficients and an excitation signal, and transform code a spectral
decomposition of the excitation signal, and code the linear prediction coefficients into the
data stream, wherein the background noise estimator is configured to use the spectral
decomposition of the excitation signal as the spectral decomposition representation of the
input audio signal in determining the parametric background noise estimate'
Further, the background noise estimator may be configured to identify local minima in the
spectral representation of the excitation signal and to estimate the spectral envelope of a
background noise of the input audio signal using interpolation between the identified local
minima as suPPorting Points'
In a further embodiment, an audio decoder for decoding a data stream so as to reconstruct
therefrom an audio signal. the data stream comprising at least an active phase followed by
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an inactive phase. The audio decoder comprises a background noise estimator 90 which
may be configured to determine a parametric background noise estimate based on a
spectral decomposition representation of the input audio signal obtained from the data
stream so that the parametric background noise estimate spectrally describes a spectral
envelope a background noise of the input audio signal. A decoder 92 may be configured to
reconstruct the audio signal from the data stream during the active phase' A parametric
random generator 94 and a background noise generator 96 may be configured to
reconstruct the audio signal during the inactive phase by controlling the parametric random
generator during the inactive phase with the parametric background noise estimate'
According to another embodiment, the background noise estimator may be configured to
perform the determining the parametric background noise estimate in the active phase and
with distinguishing between a noise component and a useful signal component within the
spectral decomposition representation of the input audio signal and to determine the
parametric background noise estimate merely from the noise component'
In a further embodiment, the decoder may be configured to, in reconstructing the audio
signal from the data stream, apply shaping a spectral decomposition of an excitation signal
transform coded into the data stream according to linear prediction coefficients also coded
into the data. The background noise estimator may be further configured to use the spectral
decomposition of the excitation signal as the spectral decomposition representation of the
input audio signal in determining the parametric background noise estimate'
According to a further embodiment, the background noise estimator may be configured to
identify local minima in the spectral representation of the excitation signal and to estimate
the spectral envelope of a background noise of the input audio signal using interpolation
between the identified local minima as supporting points.
Thus, the above embodiments, inter alias, described a TCX-based CNG where a basic
comfort noise generator employs random pulses to model the residual'
Although some aspects have been described in the context of an apparatus' it is clear that
these aspects also represent a description of the corresponding method, where a block or
device corresponds to a method step or a feature of a method step' Analogously' aspects
described in the context of a method step also represeht a description of a corresponding
block or item or feature of a corresponding apparatus. Some or all of the method steps may
be executed by (or using) a hardware apparatus, like for example, a microprocessor' a
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JJ
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programmable computer or an electronic circuit. In some embodiments' Some one or more
of the most important method steps may be executed by such an apparatus'
Depending on certain implementation requirements, embodiments of the invention can be
implemented in hardware or in software. The implementation can be performed using a
digitalstoragemedium,forexampleafloppydisk'aDVD'aBIu-Ray'aCD'aROM'a
PROM, an EPROM, an EEPROM or a FLASH memory' having electronically readable
control signals stored thereon, which cooperate '(or are capable of cooperating) with a
programmable computer system such that the respective method is performed' Therefore'
the digital storage medium may be computer readable'
some embodiments according to the invention comprise a data carrier having
electronically readable control signals, which are capable of cooperating with a
programmable computer system, such that one of the methods described herein is
performed.
Generally, embodiments of the present invention can be implemented as a computer
program product with a program code, the program code being operative for performing
one of the methods when the computer program product runs on a computer' The program
codemayforexamplebestoredonamachinereadablecarrier.
other embodiments comprise the computer program for performing one of the methods
described herein, stored on a machine readable carrier'
In other words, an embodiment of the inventive method is' therefore' a computer program
having a program code for performing one of the methods described herein' when the
computer program runs on a computer"
Afurtherembodimentoftheinventivemethodsis,therefore'adatacarrier(oradigital
storagemedium,oracomputer-readablemedium)comprising,recordedthereon,the
computelprogramforperformingoneofthemethodsdescribedherein.Thedatacarrier,
the digital storage medium or the recorded medium are typically tangible and/or nontransitionary.
Afurtherembodimentoftheinventivemethodis,therefore,adatastreamorasequenceof
signals representing the computef program for performing one of the methods described
herein. The data stream or the sequence of signals may for example be configured to be
transferred via a data communication connection, for example via the Internet'
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A further embodiment comprises a processing means, for example a computer' or a
programmable togic device, configured to or adapted to perform one of the methods
described herein.
A further embodiment comprises a computer having installed thereon the computer
program for performing one of the methods described herein.
A further embodiment according to the invention comprises an apparatus or a system
configured to transfer (for example, electronically or optically) a computer program for
performing one of the methods described herein to a receiver' The receiver may' for
example, be a computer, a mobile device, a memory device or the like' The apparatus or
system may, for example, comprise a file server for transferring the computer program to
the receiver .
In some embodiments, a programmabte logic device (for example a field programmable
gate array) may be used to perform some or all of the functionalities of the methods
described herein. In some embodiments, a field programmable gate atray may cooperate
with a microprocessor in order to perform one of the methods described herein. Generally,
the methods are preferably performed by any hardware apparatus.
The above described embodiments are merely illustrative for the principles of the present
invention. It is understood that modifications and variations of the arrangements and the
details described herein will be apparent to others skilled in the art' It is the intent,
therefore, to be limited only by the scope of the impending patent claims and not by the
specific details presented by way of description and explanation of the embodiments
herein.
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l.
N* .-\** ''
r€+ahrm
Audio encoder comPrising
a background noise estimator (12) configured to determine a parametric
background noise estimate based on a spectral decomposition representation of an
input audio signal so that the parametric background noise estimate spectrally
describes a spectral envelope ofa background noise ofthe input audio signal;
an encoder (14) for encoding the input audio signal into a data stream during the
active phase; and
a detector (16) configured to detect an entrance of an inactive phase following the
active phase based on the input signal,
wherein the audio encoder is configured to encode into the data stream the
parametric background noise estimate in the inactive phase.
Audio encoder according to claim 1, wherein the background noise estimator is
configured to perform the determining the parametric background noise estimate in
the active phase with distinguishing between a noise component and a useful signal
component within the spectral decomposition representation of the input audio
signal and to determine the parametric background noise estimate merely from the
noise component.
Audio encoder according to claim I or 2, wherein the encoder is configured to' in
encoding the input audio signal, predictively code the input audio signal into linear
prediction coefficients and an excitation signal, and transform code a spectral
decomposition of the excitation signal, and code the linear prediction coefficients
into the data stream, wherein the background noise estimator is configured to use
the spectral decomposition of the excitation signal as the spectral decomposition
representation of the input audio signal in determining the parametric background
noise estimate.
Audio encoder according to any of claims I to 3, wherein the background noise
estimator is configured to identify local minima in the spectral representation of the
excitation signal and to estimate the spectral envelope of a background noise of the
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2.
3.
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5.
input audio signal using interpolation between the identified local minima as
supporting points.
Audio encoder according to any of the previous claims, wherein the encoder is
configured to, in encoding the input audio signal, use predictive and/or transform
coding to encode a lower frequency portion of the spectral decomposition
representation of the input audio signal, and to use parametric coding to encode a
spectral envelope of a higher frequency portion of the spectral decomposition
representation of the input audio signal.
Audio encoder according to any of the previous claims, wherein the encoder is
configured to, in encoding the input audio signal, use predictive and/or transform
coding to encode a lower frequency portion of the spectral decomposition
representation of the input audio signal, and to choose between using parametric
coding to encode a spectral envelope of a higher frequency portion of the spectral
decomposition representation of the input audio signal or leaving the higher
frequency portion of the input audio signal un-coded.
Audio encoder according to claim 5 or 6, wherein the encoder is configured to
intemrpt the predictive and/or transform coding and the parametric coding in
inactive phases or to intemrpt the predictive and/or transform coding and perform
the parametric coding of the spectral envelope of the higher frequency portion of
the spectral decomposition representation of the input audio signal at a lower
time/frequency resolution compared to the use of the parametric coding in the
active phase.
Audio encoder according to claim 5, 6, or 7, wherein the encoder uses a filterbank
in order to spectrally decompose the input audio signal into a set of subbands
forming the lower frequency portion, and a set of subbands forming the higher
frequency portion.
Audio encoder according to claim 8, wherein the background noise estimator is
configured to update the parametric background noise estimate in the active phase
based on the lower and higher frequency portions of the spectral decomposition
representation of the input audio signal.
Audio encoder according to claim 9, wherein the background noise estimator is
configured to, in updating the parametric background noise estimate, identify local
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6.
7.
8.
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11.
minima in the lower and higher frequency portions of the spectral decomposition
representation of the input audio signal and to perform statistical analysis of the
lower and higher frequency portions of the spectral decomposition representation of
the input audio signal at the local minima so as to derive the parametric background
noise estimate.
Audio encoder according to any of the previous claims, wherein the noise estimator
is configured to continue continuously qpdating the background noise estimate
during the inactive phase, wherein the audio encodet is configured to intermittently
encode updates of the parametric background noise estimate as continuously
updated during the inactive phase'
Audio encoder according to claim ll, wherein the audio encoder is configured to
intermittently encode the updates of the parametric background noise estimate in a
fixed or variable interval of time'
Audio decoder for decodin g a data stream so as to reconstruct therefrom an audio
signal, the data stream comprising at least an active phase (86) followed by an
inactive phase (88), wherein the data stream has encoded therein a parametric
background noise estimate which spectrally describes a spectral envelope of a
background noise, the audio decoder comprising
a decoder (92) configured
during the active Phase;
to reconstruct the audio signal from the data stream
a parametric random generator (94); and
a background noise generator (96) configured to synthesize the audio signal during
the inactive phase (88) by controlling the parametric random generator (94) during
the inactive phase (88) depending on the parametric background noise estimate'
Audio decoder according to claim 13, wherein the background noise generator (96)
is configured to reconstruct a spectrum from the parametric background noise
estimate and re-transform the spectrum into a time-domain'
Audio decoder for decodin g a data stream so as to reconstruct therefrom an audio
signal, the data stream comprising at least an active phase followed by an inactive
phase, the audio decoder comprising
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t2.
13.
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14.
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16.
fl.
a background noise estimator (90) configured to determine a parametric
background noise estimate based on a spectral decomposition representation of the
ipput audio signal obtained from the data stream so that the parametric background
noise estimate spectrally describes a spectral envelope a background noise of the
input audio signal;
a decoder (92) configured to reconstruct-.the audio signal from the data stream
during the active phase;
a parametric random generator (94); and
a background noise generator (96) configured to reconstruct the audio signal during
the inactive phase by controlling the parametric random generator during the
inactive phase with the parametric background noise estimate.
Audio decoder according to claim 15, wherein the background noise estimator is
configured to perform the determining the parametric background noise estimate in
the active phase and with distinguishing between a noise component and a useful
signal component within the spectral decomposition representation of the input
audio signal and to determine the parametric background noise estimate merely
from the noise component.
Audio decoder according to claim 15 or 16, wherein the decoder is configured to, in
reconstructing the audio signal from the data stream, apply shaping a spectral
decomposition of an excitation signal transform coded into the data stream
according to linear prediction coefficients also coded into the data, wherein the
background noise estimator is configured to use the spectral decomposition of the
excitation signal as the spectral decomposition representation of the input audio
signal in determining the parametric background noise estimate.
Audio decoder according to claim 17, wherein the background noise estimator is
configured to identiff local minima in the spectral representation of the excitation
signal and to estimate the spectral envelope of a background noise of the input
audio signal using interpolation between the identified local minima as supporting
points.
Audio encoding method comPrising
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determining a parametric background noise estimate based on a spectral
decomposition representation of an input audio signal so that the parametric
background noise estimate spectrally describes a spectral envelope of a background
noise of the input audio signal;
encoding the input audio signal into a data stream during the active phase; and
detecting an entrance of an inactive phase following the active phase based on the
input signal, and
encoding into the data stream the parametric background noise estimate in the
inactive phase.
Method for decodin g a datastream so as to reconstruct therefrom an audio signal'
the data stream comprising at least an active phase (86) followed by an inactive
phase (88), wherein the data stream has encoded therein a parametric background
noise estimate which spectrally describes a spectral envelope of a background
noise, the audio decoder comprising
reconstructing the audio signal from the data stream during the active phase;
synthesizing the audio signal during the inactive phase (88) by controlling a
parametric random generator (94) during the inactive phase (88) depending on the
parametric background noise estimate'
Method for decodin g a data stream so as to reconstruct therefrom an audio signal'
the data stream comprising at least an active phase followed by an inactive phase'
the audio decoder comPrising
determining a parametric background noise estimate based on a spectral
decomposition representation of the input audio signal obtained from the data
stream so that the parametric background noise estimate spectrally describes a
spectral envelope a background noise of the input audio signal;
reconstructing the audio signal from the data stream during the active phase;
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21.
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reconstructing the audio signal during the inactive phase by controlling a
parametric random generator during the inactive phase with the parametric
background noise estimate.
5 22. Computer program having a program code for performing, when running on a
computer, a method according to any of claims 19 to 21.