The application is related to signal processing, and particularly, to audio signal
processing.
The perceptual coding of audio signals for the purpose of data reduction for efficient
storage or transmission of these signals is a widely used practice. In particular when
lowest bit rates are to be achieved, the employed coding leads to a reduction of audio
quality that often is primarily caused by a limitation at the encoder side of the audio signal
bandwidth to be transmitted In contemporary codecs well-known methods exist for the
decoder-side signal restoration through audio signal Band Width Extension (BWE), e.g.
Spectral Band Replication (SBR).
In low bit rate coding, often also so-called noise-filling is employed. Prominent spectral
regions that have been quantized to zero due to strict bitrate constraints are filled with
synthetic noise in the decoder.
Usually, both techniques are combined in low bitrate coding applications. Moreover,
integrated solutions such as Intelligent Gap Filling (IGF) exist that combine audio coding,
noise-filling and spectral gap filling.
However, all these methods have in common that in a first step the baseband or core
audio signal is reconstructed using waveform decoding and noise-filling, and in a second
step the BWE or the IGF processing is performed using the readily reconstructed signal.
This leads to the fact that the same noise values that have been filled in the baseband by
noise-filling during reconstruction are used for regenerating the missing parts in the
highband (in BWE) or for filling remaining spectral gaps (in IGF). Using highly correlated
noise for reconstructing multiple spectral regions in BWE or IGF may lead to perceptual
impairments.
Relevant topics in the state-of-art comprise
• SBR as a post processor to waveform decoding [1-3]
• AAC PNS [4]
• MPEG-D USAC noise-filling [5]
• G.719 and G.722.1C [6]
• MPEG-H 3D IGF [8]
The following papers and patent applications describe methods that are considered to be
relevant for the application:
[1] M. Dietz, L . Liljeryd, K . Kjorling and O. Kunz, "Spectral Band Replication, a novel
approach in audio coding," in 112th AES Convention, Munich, Germany, 2002.
[2] S. Meltzer, R. Bohm and F. Henn, "SBR enhanced audio codecs for digital
broadcasting such as "Digital Radio Mondiale" (DRM)," in 112th AES Convention,
Munich, Germany, 2002.
[3] T. Ziegler, A. Ehret, P. Ekstrand and M. Lutzky, "Enhancing mp3 with SBR:
Features and Capabilities of the new mp3PRO Algorithm," in 112th AES
Convention, Munich, Germany, 2002.
[4] J . Herre, D. Schulz, Extending the MPEG-4 AAC Codec by Perceptual Noise
Substitution, Audio Engineering Society 104th Convention, Preprint 4720,
Amsterdam, Netherlands, 1998
[5] European Patent application EP2304720 USAC noise-filling
[6] ITU-T Recommendations G.719 and G.221 C
[7] EP 2704142
[8] EP 13177350
Audio signals processed with these methods suffer from artifacts such as roughness,
modulation distortions and a timbre perceived as unpleasant, in particular at low bit rate
and consequently low bandwidth and/or the occurrence of spectral holes in the LF range.
The reason for this is, as will be explained below, primarily the fact that the reconstructed
components of the extended or gap filled spectrum are based on one or more direct
copies containing noise from the baseband. The temporal modulations resulting from said
unwanted correlation in reconstructed noise are audible in a disturbing manner as
perceptual roughness or objectionable distortion. All existing methods like mp3+SBR,
AAC+SBR, USAC, G.719 and G.722.1C, and also MPEG-H 3D IGF first do a complete
core decoding including noise-filling before filling spectral gaps or the highband with
copied or mirrored spectral data from the core.
It is an object of the present invention to provide an improved concept of generating an
enhanced signal.
This object is achieved by an apparatus for generating an enhanced signal of claim 1, a
method for generating an enhanced signal of claim 11, a system of encoding and
decoding of claim 13, a method of encoding and decoding of claim 14 or a computer
program of claim 15.
The present invention is based on the finding that a significant improvement of the audio
quality of an enhanced signal generated by bandwidth extension or intelligent gap filling or
any other way of generating an enhanced signal having spectral values for an
enhancement spectral region being not contained in an input signal is obtained by
generating first noise values for a noise-filling region in a source spectral region of the
input signal and by then generating second independent noise values for a noise region in
the destination or target region, i.e., in the enhancement region which now has noise
values, i.e., the second noise values that are independent from the first noise values.
Thus, the prior art problem with having dependent noise in the baseband and the
enhancement band due to the spectral values mapping is eliminated and the related
problems with artifacts such as roughness, modulation distortions and a timbre perceived
as unpleasant particularly at low bitrates are eliminated.
In other words, the noise-filling of second noise values being decorrelated from the first
noise values, i.e., noise values which are at least partly independent from the first noise
values makes sure that artifacts do not occur anymore or are at least reduced with respect
to the prior art. Hence, the prior art processing of noise-filling spectral values in the
baseband by a straightforward bandwidth extension or intelligent gap filling operation does
not decorrelate the noise from the baseband, but only changes the level, for example.
However, introducing decorrelated noise values in the source band on the one hand and
in the target band on the other hand, preferably derived from a separate noise process
provides the best results. However, even the introduction of noise values being not
completely decorrelated or not completely independent, but being at least partly
decorrelated such as by a decorrelation value of 0.5 or less when the decorrelation value
of zero indicates completely decorrelated, improves the full correlation problem of the prior
art.
Hence, embodiments relate a combination of waveform decoding, bandwidth extension or
gap filling and noise-filling in a perceptual decoder.
Further advantages are that, in contrast to already existing concepts, the occurrence of
signal distortions and perceptual roughness artifacts, which currently are typical for
calculating bandwidth extensions or gap filling subsequent to waveform decoding and
noise-filling are avoided.
This is due to, in some embodiments, a change in the order of the mentioned processing
steps. It is preferred to perform bandwidth extension or gap filling directly after waveform
decoding and it is furthermore preferred to compute the noise-filling subsequently on the
already reconstructed signal using uncorrelated noise.
In further embodiments, waveform decoding and noise-filling can be performed in a
traditional order and further downstream in the processing, the noise values can be
replaced by appropriately scaled uncorrelated noise.
Hence, the present invention addresses the problems that occur due to a copy operation
or a mirror operation on noise-filled spectra by shifting the noise-filling step to a very end
of a processing chain and using uncorrelated noise for the patching or gap filling.
Subsequently, preferred embodiments of the present invention are discussed with respect
to the accompanying drawings, in which:
Fig. 1a illustrates an apparatus for encoding an audio signal;
Fig. 1b illustrates a decoder for decoding an encoded audio signal matching with
the encoder of Fig. 1a;
Fig. 2a illustrates a preferred implementation of the decoder;
Fig. 2b illustrates a preferred implementation of the encoder;
Fig. 3a illustrates a schematic representation of a spectrum as generated by the
spectral domain decoder of Fig. 1b ;
Fig. 3b illustrates a table indicating the relation between scale factors for scale
factor bands and energies for reconstruction bands and noise-filling
information for a noise-filling band;
Fig. 4a illustrates the functionality of the spectral domain encoder for applying the
selection of spectral portions into the first and second sets of spectral
portions;
illustrates an implementation of the functionality of Fig. 4a;
illustrates a functionality of an MDCT encoder;
illustrates a functionality of the decoder with an MDCT technology;
illustrates an implementation of the frequency regenerator;
illustrates a block diagram of an apparatus for generating an enhanced
signal in accordance with the present invention;
illustrates a signal flow of independent noise-filling steered by a selection
information in a decoder in accordance with an embodiment of the present
invention;
illustrates a signal flow of an independent noise-filling implemented through
an exchanged order of gap filling or bandwidth extension and noise-filling in
a decoder;
illustrates a flowchart of a procedure in accordance with a further
embodiment of the present invention;
Fig. 10 illustrates a flowchart of a procedure in accordance with a further
embodiment of the present invention;
Fig. 11 illustrates a flowchart for explaining a scaling of random values;
Fig. 12 illustrates a flowchart illustrating an embedding of the present invention into
a general bandwidth extension or a gap filling procedure;
Fig. 13a illustrates an encoder with a bandwidth extension parameter calculation;
and
Fig. 13b illustrates a decoder with a bandwidth extension implemented as a post
processor rather than an integrated procedure as in Fig. 1a or 1b.
Fig. 6 illustrates an apparatus for generating an enhanced signal such as an audio signal
from an input signal which can also be an audio signal. The enhanced signal has spectral
values for an enhancement spectral region, wherein the spectral values for the
enhancement spectral region are not contained in the original input signal at an input
signal input 600. The apparatus comprises a mapper 602 for mapping a source spectral
region of the input signal to a target region in the enhancement spectral region, wherein
the source spectral region comprises a noise-filling region.
Furthermore, the apparatus comprises a noise filler 604 configured for generating first
noise values for the noise-filling region in the source spectral region of the input signal and
for generating second noise values for a noise region in the target region, wherein the
second noise values, i.e., the noise values in the target region are independent or
uncorrelated or decorrelated from the first noise values in the noise-filling region.
One embodiment relates to a situation, in which noise filling is actually performed in the
base band, i.e., in which the noise values in the source region have been generated by
noise filling. In a further alternative, it is assumed that a noise filling in the source region
has not been performed. Nevertheless the source region has a noise region actually filled
with noise like spectral values exemplarily encoded as spectral values by the source or
core encoder. Mapping this noise like source region to the enhancement region would
also generate dependent noise in source and target regions. In order to address this
issue, the noise filler only fills noise into the target region of the mapper, i.e. generates
second noise values for the noise region in the target region, wherein the second noise
values are decorrelated from first noise values in the source region. This replacement or
noise filling can also take place either in a source tile buffer or can take place in the target
itself. The noise region can be identified by the classifier either by analyzing the source
region or by analyzing the target region.
To this end, reference is made to Fig. 3A. Fig. 3A illustrates as filling region such as scale
factor band 301 in the input signal, and the noise filler generates the first noise spectral
values in this noise-filling band 301 in a decoding operation of the input signal.
Furthermore, this noise-filling band 301 is mapped to a target region, i.e., in accordance
with the prior art, the generated noise values are mapped to the target region and,
therefore, the target region would have dependent or correlated noise with the source
region.
In accordance with the present invention, however, the noise filler 604 of Fig. 6 generates
second noise values for a noise region in the destination or target region, where the
second noise values are decorrelated or uncorrelated or independent from the first noise
values in the noise-filling band 301 of Fig. 3A.
Generally, the noise-filling and the mapper for mapping the source spectral region to a
destination region may be included within a high frequency regenerator as illustrated in
the context of Figs. 1A to 5C exemplarily within an integrated gap filling or can be
implemented as a post-processor as illustrated in Fig. 13B and the corresponding encoder
in Fig. 13A.
Generally, an input signal is subjected to an inverse quantization 700 or any other or
additional predefined decoder processing 700 which means that, at the output of block
700, the input signal of Fig. 6 is obtained, so that the input into the core coder noise-filling
block or noise filler block 704 is the input 600 of Fig. 6 . The mapper in Fig. 6 corresponds
to the gap filling or bandwidth extension block 602 and the independent noise-filling block
702 is also included within the noise filler 604 of Fig. 6 . Thus, blocks 704 and 702 are both
included in the noise filler block 604 of Fig. 6 and block 704 generates the so-called first
noise values for a noise region in the noise-filling region and block 702 generates the
second noise values for a noise region in the destination or target region, which is derived
from the noise-filling region in the baseband by bandwidth extension performed by the
mapper or gap filling or bandwidth extension block 602. Furthermore, as discussed later
on, the independent noise-filling operation performed by block 702 is controlled by a
control vector PHI illustrated by a control line 706.
1. Step: Noise Identification
In a first step ail spectrai lines which represent noise in a transmitted audio frame are
identified. The identification process may be controlled by already existing, transmitted
knowledge of noise positions used by noise-filling [4][5] or may be identified with an
additional classifier. The result of noise line identification is a vector containing zeroes and
ones where a position with a one indicates a spectral line which represents noise.
In mathematical terms this procedure can be described as:
Let X e be a transmitted and re-quantized spectrum after noise-filling [4][5] of a
transform coded, windowed signal of length V . Let e .,0 < m < , be the
stop line of the whole decoding process.
The classifier determines spectral lines where noise-filling [4][5] in the core region is
used:
C £ ® {0,l} ,
< m £ N ' ' els e
and the result is a vector of length m.
An additional classifier C may identify further lines in X which represents noise. This
classifier can be described as:
C C x {0,1} ® {0,l} ,
,·, if ί ΐ = 1 V is classified as noise L , . , .
ί else
After the noise identification process the noise indication vector f ,ί is defined as:
,
, the spe ctral line X[i] is not identifie d as a nois e line
2 . Step: Independent Noise
In the second step a specific region of the transmitted spectrum is selected and copied to
a source tile. Within this source tile the identified noise is replaced by random noise. The
energy of the inserted random noise is adjusted to the same energy of the original noise in
the source tile.
In mathematical terms this procedure can be described as:
Let n, n < , be the start line for the copy up process, described in Step 3 . Let
X be a continuous part of a transmitted spectrum x , representing a source tile
of length v < n, which contains the spectral lines , fc+1 , ,.., i fc+ _, of , where k
is the index of the first spectral line in the source tile X , so that
s [i] = l < i v, Furthermore, let f ' c f , so that
f [ ] = n , 0 < ¾+ , c —i < m < N,
or, if the IGF scheme [8] is used:
r .
0 »' < v , c ³ n , 0 < k 4- < n, — i < m
3 . Step: Independent Noise-Filling
Perform legacy noise-filling up to n and calculate the energy of noise spectral lines in the
source region k, k - 1, ... , k ÷ v — :
v - l
Perform independent noise-filling in the gap filling or BWE spectral region:
where r[t],0 < < v again is a set of random numbers.
Calculate the energy E ' of the inserted random numbers:
25
v-l
=
Again, if E > 0 calculate
the factor g , else set g = Q:
E
With g , rescale the replaced noise:
flr , . i j - i], [ + i = i .
A C -r ί ] ; = , , O S i
{ X[c + i], p [ + ] = 0
The inventive independent noise-filling can be used in a stereo channel pair environment
as well. Therefore the encoder calculates the appropriate channel pair representation, L/R
or M/S, per frequency band and optional prediction coefficients. The decoder applies
independent noise-filling as described above to the appropriately chosen representation of
the channels prior to the subsequent computation of the final conversion of all frequency
bands into L/R representation.
The invention is applicable or suitable for all audio applications in which the full bandwidth
is not available or that use gap filling for filling spectral holes. The invention may find use
in the distribution or broadcasting of audio content such as, for example with digital radio,
Internet streaming and audio communication applications.
Subsequently, embodiments of the present invention are discussed with respect to Figs.
9-12. In step 900, noise regions are identified in the source range. This procedure, which
has been discussed before with respect to "Noise Identification" can rely on the noisefilling
side information received from an encoder-side fully or can also be configured to
alternatively or additionally rely on the signal analysis of the input signal already
generated, but without spectral values for the enhancement spectral region, i.e., without
the spectral values for this enhancement's spectral region.
Then, in step 902, the source range which has already been subjected to straightforward
noise-filling as known in the art, i.e., a complete source range is copied to a source tile
buffer.
Then, in step 904, the first noise values, i.e., the straightforward noise values generated
within the noise-filling region of the input signal are replaced in the source tile buffer by
random values. Then, in step 906, these random values are scaled in the source tile
buffer to obtain the second noise values for the target region. Then, in step 908, the
mapping operation is performed, i.e., their content of the source tile buffer available
subsequent to steps 904 and 906 is mapped to the destination range. Thus, by means of
the replacement operation 904, and subsequent to the mapping operation 908, the
independent noise-filling operation in the source range and in the target range have been
obtained.
Fig. 10 illustrates a further embodiment of the present invention. Again, in step 900, the
noise in the source range is identified. However; the functionality of this step 900 is
different from the functionality of the step 900 in Fig. 9 , since step 900 in Fig. 9 may
operate on an input signal spectrum which has already received noise values, i.e., in
which the noise-filling operation has already been performed.
However, in Fig. 10, any noise-filling operation to the input signal has not been performed
and the input signal does not yet have any noise values in the noise-filling region at the
input in step 902. In step 902, the source range is mapped to the destination or target
range where the noise-filling values are not included in the source range.
Thus, the identification of the noise in the source range in step 900 can be, with respect to
the noise-filling region, performed by identifying zero spectral values in the signal and/or
by using this noise-filling side-information from the input signal, i.e., the encoder-side
generated noise-filling information. Then, in step 904, the noise-filling information and,
particularly, the energy information identifying the energy to be introduced into the
decoder-side input signal is read.
Then, as illustrated in step 1006, a noise-filling in the source range is performed and,
subsequently or concurrently, a step 1008 is performed, i.e., random values are inserted
in positions in the destination range which have been identified by step 900 over the full
band or which have been identified by using the baseband or input signal information
together with the mapping information, i.e., which (of a plurality of) source range is
mapped to which (of a plurality of) target range.
Finally, the inserted random values are scaled to obtain the second independent or
uncorrelated or decorrelated noise values.
Subsequently, Fig. 11 is discussed in order to illustrate further information on the scaling
of the noise-filling values in the enhancement spectral region, i.e., how, from the random
values, the second noise values are obtained.
In step 1100, an energy information on noise in the source range is obtained. Then, an
energy information is determined from the random values, i.e., from the values generated
by a random or pseudo-random process as illustrated in step 1102. Furthermore, step
104 illustrates the way how to calculate the scale factor, i.e., by using the energy
information on noise in the source range and by using the energy information on the
random values. Then, in step 1106, the random values, i.e., from which the energy has
been calculated in step 1102, are multiplied by the scale factor generated by step 1104.
Hence, the procedure illustrated in Fig. 11 corresponds to the calculation of the scale
factor g illustrated before in an embodiment. However, all these calculations can also be
performed in a logarithmic domain or in any other domain and the multiplication step 106
can be replaced by an addition or subtraction in the logarithmic range.
Further reference is made to Fig. 12 in order to illustrate the embedding of the present
invention within a general intelligent gap filling or bandwidth extension scheme. In step
1200, spectral envelope information is retrieved from the input signal. The spectral
envelope information can, for example, be generated by a parameter extractor 1306 of
Fig. 13A and can be provided by a parameter decoder 1324 of Fig. 13b. Then, the second
noise values and the other values in the destination range are scaled using this spectral
envelope information as illustrated in 1202. Subsequently, any further post-processing
1204 can be performed to obtain the final time domain enhanced signal having an
increased bandwidth in case of bandwidth extension or having a reduced number or no
spectral holes in the context of intelligent gap filling.
In this context it is outlined that, particularly for the embodiment of Fig. 9, several
alternatives can be applied. For an embodiment, step 902 is performed with the whole
spectrum of the input signal or at least with the portion of the spectrum of the input signal
which is above the noise-filling border frequency. This frequency assures that below a
certain frequency, i.e., below this frequency, any noise-filling is not performed at all.
Then, irrespective of any specific source range/target range mapping information, the
whole input signal spectrum, i.e., the complete potential source range is copied to the
source tile buffer 902 and is then processed with step 904 and 906 and step 908 then
selects the certain specifically required source region from this source tile buffer.
In other embodiments, however, only the specifically required source ranges which may
be only parts of the input signal are copied to the single source tile buffer or to several
individual source tile buffers based on the source range/target range information included
in the input signal, i.e., associated as side information to this audio input signal.
Depending on the situation, the second alternative, where only the specifically required
source ranges are processed by steps 902, 904, 906, the complexity or at least the
memory requirements may be reduced compared to the situation where always,
independent of the specific mapping situation, the whole source range at least above the
noise-filling border frequency is processed by steps 902, 904, 906.
Subsequently, reference is made to Figs. 1a - 5c in order to illustrate the specific
implementation of the present invention within a frequency regenerator 116, which is
placed before the spectrum-time converter 18.
Fig. 1a illustrates an apparatus for encoding an audio signal 99. The audio signal 99 is
input into a time spectrum converter 100 for converting an audio signal having a sampling
rate into a spectral representation 101 output by the time spectrum converter. The
spectrum 101 is input into a spectral analyzer 102 for analyzing the spectral
representation 101 . The spectral analyzer 10 1 is configured for determining a first set of
first spectral portions 103 to be encoded with a first spectral resolution and a different
second set of second spectral portions 105 to be encoded with a second spectral
resolution. The second spectral resolution is smaller than the first spectral resolution. The
second set of second spectral portions 105 is input into a parameter calculator or
parametric coder 104 for calculating spectral envelope information having the second
spectral resolution. Furthermore, a spectral domain audio coder 106 is provided for
generating a first encoded representation 107 of the first set of first spectral portions
having the first spectral resolution. Furthermore, the parameter calculator/parametric
coder 104 is configured for generating a second encoded representation 109 of the
second set of second spectral portions. The first encoded representation 107 and the
second encoded representation 109 are input into a bit stream multiplexer or bit stream
former 108 and block 108 finally outputs the encoded audio signal for transmission or
storage on a storage device.
Typically, a first spectral portion such as 306 of Fig. 3a will be surrounded by two second
spectral portions such as 307a, 307b. This is not the case in HE AAC, where the core
coder frequency range is band limited
Fig. 1b illustrates a decoder matching with the encoder of Fig. 1a. The first encoded
representation 107 is input into a spectral domain audio decoder 12 for generating a first
decoded representation of a first set of first spectral portions, the decoded representation
having a first spectral resolution. Furthermore, the second encoded representation 109 is
input into a parametric decoder 114 for generating a second decoded representation of a
second set of second spectral portions having a second spectral resolution being lower
than the first spectral resolution.
The decoder further comprises a frequency regenerator 116 for regenerating a
reconstructed second spectral portion having the first spectral resolution using a first
spectral portion. The frequency regenerator 116 performs a tile filling operation, i.e., uses
a tile or portion of the first set of first spectral portions and copies this first set of first
spectral portions into the reconstruction range or reconstruction band having the second
spectral portion and typically performs spectral envelope shaping or another operation as
indicated by the decoded second representation output by the parametric decoder 14,
i.e., by using the information on the second set of second spectral portions. The decoded
first set of first spectral portions and the reconstructed second set of spectral portions as
indicated at the output of the frequency regenerator 116 on line 117 is input into a
spectrum-time converter 118 configured for converting the first decoded representation
and the reconstructed second spectral portion into a time representation 119, the time
representation having a certain high sampling rate.
Fig. 2b illustrates an implementation of the Fig. 1a encoder. An audio input signal 99 is
input into an analysis filterbank 220 corresponding to the time spectrum converter 100 of
Fig. 1a. Then, a temporal noise shaping operation is performed in TNS block 222.
Therefore, the input into the spectral analyzer 102 of Fig. 1a corresponding to a block
tonal mask 226 of Fig. 2b can either be full spectral values, when the temporal noise
shaping/ temporal tile shaping operation is not applied or can be spectral residual values,
when the TNS operation as illustrated in Fig. 2b, block 222 is applied. For two-channel
signals or multi-channel signals, a joint channel coding 228 can additionally be performed,
so that the spectral domain encoder 106 of Fig. 1a may comprise the joint channel coding
block 228. Furthermore, an entropy coder 232 for performing a lossless data compression
is provided which is also a portion of the spectral domain encoder 106 of Fig. 1a.
The spectral analyzer/tonal mask 226 separates the output of TNS block 222 into the core
band and the tonal components corresponding to the first set of first spectral portions 103
and the residual components corresponding to the second set of second spectral portions
105 of Fig. 1a. The block 224 indicated as IGF parameter extraction encoding
corresponds to the parametric coder 104 of Fig. 1a and the bitstream multiplexer 230
corresponds to the bitstream multiplexer 108 of Fig. 1a.
Preferably, the analysis filterbank 222 is implemented as an MDCT (modified discrete
cosine transform filterbank) and the MDCT is used to transform the signal 99 into a timefrequency
domain with the modified discrete cosine transform acting as the frequency
analysis tool.
The spectral analyzer 226 preferably applies a tonality mask. This tonality mask
estimation stage is used to separate tonal components from the noise-like components in
the signal. This allows the core coder 228 to code all tonal components with a psychoacoustic
module. The tonality mask estimation stage can be implemented in numerous
different ways and is preferably implemented similar in its functionality to the sinusoidal
track estimation stage used in sine and noise-modeling for speech/audio coding [8, 9] or
an HILN model based audio coder described in [10]. Preferably, an implementation is
used which is easy to implement without the need to maintain birth-death trajectories, but
any other tonality or noise detector can be used as well.
The IGF module calculates the similarity that exists between a source region and a target
region. The target region will be represented by the spectrum from the source region. The
measure of similarity between the source and target regions is done using a crosscorrelation
approach. The target region is split into nTar non-overlapping frequency tiles.
For every tile in the target region, nSrc source tiles are created from a fixed start
frequency. These source tiles overlap by a factor between 0 and 1, where 0 means 0%
overlap and 1 means 100% overlap. Each of these source tiles is correlated with the
target tile at various lags to find the source tile that best matches the target tile. The best
matching tile number is stored in tileN m[idxjar] , the lag at which it best correlates
with the target is stored in xcorr_lag [idxjar] [idx_src] and the sign of the correlation is
stored in xcorr_sign[idxjar] [idx_src] . In case the correlation is highly negative, the
source tile needs to be multiplied by - 1 before the tile filling process at the decoder. The
IGF module also takes care of not overwriting the tonal components in the spectrum since
the tonal components are preserved using the tonality mask. A band-wise energy
parameter is used to store the energy of the target region enabling us to reconstruct the
spectrum accurately.
This method has certain advantages over the classical SBR [1] in that the harmonic grid of
a multi-tone signal is preserved by the core coder while only the gaps between the
sinusoids is filled with the best matching "shaped noise" from the source region. Another
advantage of this system compared to ASR (Accurate Spectral Replacement) [2-4] is the
absence of a signal synthesis stage which creates the important portions of the signal at
the decoder. Instead, this task is taken over by the core coder, enabling the preservation
of important components of the spectrum. Another advantage of the proposed system is
the continuous scalability that the features offer. Just using tile u [idx ar ] and
xcorrjag —0, for every tile is called gross granularity matching and can be used for low
bitrates while using variable xco-rrjag for every tile enables us to match the target and
source spectra better.
In addition, a tile choice stabilization technique is proposed which removes frequency
domain artifacts such as trilling and musical noise.
In case of stereo channel pairs an additional joint stereo processing is applied. This is
necessary, because for a certain destination range the signal can a highly correlated
panned sound source. In case the source regions chosen for this particular region are not
well correlated, although the energies are matched for the destination regions, the spatial
image can suffer due to the uncorrelated source regions. The encoder analyses each
destination region energy band, typically performing a cross-correlation of the spectral
values and if a certain threshold is exceeded, sets a joint flag for this energy band. In the
decoder the left and right channel energy bands are treated individually if this joint stereo
flag is not set. In case the joint stereo flag is set, both the energies and the patching are
performed in the joint stereo domain. The joint stereo information for the IGF regions is
signaled similar the joint stereo information for the core coding, including a flag indicating
in case of prediction if the direction of the prediction is from downmix to residual or vice
versa.
The energies can be calculated from the transmitted energies in the L/R-domain.
midNrg[k] = leftNrg[k] +
sideNrg[k] = leftNrg[k) —r g h Nrg [
with being the frequency index in the transform domain.
Another solution is to calculate and transmit the energies directly in the joint stereo
domain for bands where joint stereo is active, so no additional energy transformation is
needed at the decoder side.
The source tiles are always created according to the Mid/Side-Matrix:
midTile[k] =0.5 · (lefffile[k] +rightTUe[k])
sideTile[k] =0.5 · (leftTile[k] - ghtTil k])
Energy adjustment:
midTiie[k] = i i le[ ] *r d rg [k]
sideTile[k] = sideTile[k] *s ide rg [k
Joint stereo -> LR transformation:
If no additional prediction parameter is coded:
leftTile[k] - midTile[k] +sideTile[k]
righlTile[k] =midTil.e[k] - sideTile[k)
If an additional prediction parameter is coded and if the signaled direction is from mid to
side:
sideTile[k] =sideTile[k] - predictionCoeff midTile\k ]
leflTile[k] =m.idTile[k] +sideTile[k]
rightTile[k] =midlile[k] - sideTile[k]
If the signaled direction is from side to mid:
midTile\[k] =midTile[k] - predictionCoeff sideTile[k]
left.Tile[k] =midTile\[k] - sideTile[k]
righlTile[k] =midTile\[k] +sideTile[k ]
This processing ensures that from the tiles used for regenerating highly correlated
destination regions and panned destination regions, the resulting left and right channels
still represent a correlated and panned sound source even if the source regions are not
correlated, preserving the stereo image for such regions.
In other words, in the bitstream, joint stereo flags are transmitted that indicate whether L/R
or M/S as an example for the general joint stereo coding shall be used. In the decoder,
first, the core signal is decoded as indicated by the joint stereo flags for the core bands.
Second, the core signal is stored in both L/R and M/S representation. For the IGF tile
filling, the source tile representation is chosen to fit the target tile representation as
indicated by the joint stereo information for the IGF bands.
Temporal Noise Shaping (TNS) is a standard technique and part of AAC [11 - 13]. TNS
can be considered as an extension of the basic scheme of a perceptual coder, inserting
an optional processing step between the filterbank and the quantization stage. The main
task of the TNS module is to hide the produced quantization noise in the temporal
masking region of transient like signals and thus it leads to a more efficient coding
scheme. First, TNS calculates a set of prediction coefficients using "forward prediction" in
the transform domain, e.g. MDCT. These coefficients are then used for flattening the
temporal envelope of the signal. As the quantization affects the TNS filtered spectrum,
also the quantization noise is temporarily flat. By applying the invers TNS filtering on
decoder side, the quantization noise is shaped according to the temporal envelope of the
TNS filter and therefore the quantization noise gets masked by the transient.
IGF is based on an MDCT representation. For efficient coding, preferably long blocks of
approx. 20 ms have to be used. If the signal within such a long block contains transients,
audible pre- and post-echoes occur in the IGF spectral bands due to the tile filling. Fig. 7c
shows a typical pre-echo effect before the transient onset due to IGF. On the left side, the
spectrogram of the original signal is shown and on the right side the spectrogram of the
bandwidth extended signal without TNS filtering is shown.
This pre-echo effect is reduced by using TNS in the IGF context. Here, TNS is used as a
temporal tile shaping (TTS) tool as the spectral regeneration in the decoder is performed
on the TNS residual signal. The required TTS prediction coefficients are calculated and
applied using the full spectrum on encoder side as usual. The TNS/TTS start and stop
frequencies are not affected by the IGF start frequency fl F ta of the IGF tool. In
comparison to the legacy TNS, the TTS stop frequency is increased to the stop frequency
of the IGF tool, which is higher than f! ta . On decoder side the TNS/TTS coefficients
are applied on the full spectrum again, i.e. the core spectrum plus the regenerated
spectrum plus the tonal components from the tonality map (see Fig. 7e). The application
of TTS is necessary to form the temporal envelope of the regenerated spectrum to match
the envelope of the original signal again. So the shown pre-echoes are reduced. In
addition, it still shapes the quantization noise in the signal below f;GF t as usual with
TNS.
In legacy decoders, spectral patching on an audio signal corrupts spectral correlation at
the patch borders and thereby impairs the temporal envelope of the audio signal by
introducing dispersion. Hence, another benefit of performing the IGF tile filling on the
residual signal is that, after application of the shaping filter, tile borders are seamlessly
correlated, resulting in a more faithful temporal reproduction of the signal.
In an inventive encoder, the spectrum having undergone TNS/TTS filtering, tonality mask
processing and IGF parameter estimation is devoid of any signal above the IGF start
frequency except for tonal components. This sparse spectrum is now coded by the core
coder using principles of arithmetic coding and predictive coding. These coded
components along with the signaling bits form the bitstream of the audio.
Fig. 2a illustrates the corresponding decoder implementation. The bitstream in Fig. 2a
corresponding to the encoded audio signal is input into the demultiplexer/decoder which
would be connected, with respect to Fig. 1b, to the blocks 112 and 114. The bitstream
demultiplexer separates the input audio signal into the first encoded representation 107 of
Fig. 1b and the second encoded representation 109 of Fig. 1b. The first encoded
representation having the first set of first spectral portions is input into the joint channel
decoding block 204 corresponding to the spectral domain decoder 112 of Fig. 1b. The
second encoded representation is input into the parametric decoder 114 not illustrated in
Fig. 2a and then input into the IGF block 202 corresponding to the frequency regenerator
116 of Fig. 1b. The first set of first spectral portions required for frequency regeneration
are input into IGF block 202 via line 203. Furthermore, subsequent to joint channel
decoding 204 the specific core decoding is applied in the tonal mask block 206 so that the
output of tonal mask 206 corresponds to the output of the spectral domain decoder 112.
Then, a combination by combiner 208 is performed, i.e., a frame building where the output
of combiner 208 now has the full range spectrum, but still in the TNS/TTS filtered domain.
Then, in block 210, an inverse TNS/TTS operation is performed using TNS/TTS filter
information provided via line 109, i.e., the TTS side information is preferably included in
the first encoded representation generated by the spectral domain encoder 106 which
can, for example, be a straightforward AAC or USAC core encoder, or can also be
included in the second encoded representation. At the output of block 210, a complete
spectrum until the maximum frequency is provided which is the full range frequency
defined by the sampling rate of the original input signal. Then, a spectrum/time conversion
is performed in the synthesis filterbank 212 to finally obtain the audio output signal.
Fig. 3a illustrates a schematic representation of the spectrum. The spectrum is subdivided
in scale factor bands SCB where there are seven scale factor bands SCB1 to SCB7 in the
illustrated example of Fig. 3a. The scale factor bands can be AAC scale factor bands
which are defined in the AAC standard and have an increasing bandwidth to upper
frequencies as illustrated in Fig. 3a schematically. It is preferred to perform intelligent gap
filling not from the very beginning of the spectrum, i.e., at low frequencies, but to start the
IGF operation at an IGF start frequency illustrated at 309. Therefore, the core frequency
band extends from the lowest frequency to the iGF start frequency. Above the iGF start
frequency, the spectrum analysis is applied to separate high resolution spectral
components 304, 305, 306, 307 (the first set of first spectral portions) from low resolution
components represented by the second set of second spectral portions. Fig. 3a illustrates
a spectrum which is exemplarily input into the spectral domain encoder 106 or the joint
channel coder 228, i.e., the core encoder operates in the full range, but encodes a
significant amount of zero spectral values, i.e., these zero spectral values are quantized to
zero or are set to zero before quantizing or subsequent to quantizing. Anyway, the core
encoder operates in full range, i.e., as if the spectrum would be as illustrated, i.e., the core
decoder does not necessarily have to be aware of any intelligent gap filling or encoding of
the second set of second spectral portions with a lower spectral resolution.
Preferably, the high resolution is defined by a line-wise coding of spectral lines such as
MDCT lines, while the second resolution or low resolution is defined by, for example,
calculating only a single spectral value per scale factor band, where a scale factor band
covers several frequency lines. Thus, the second low resolution is, with respect to its
spectral resolution, much lower than the first or high resolution defined by the line-wise
coding typically applied by the core encoder such as an AAC or USAC core encoder.
Regarding scale factor or energy calculation, the situation is illustrated in Fig. 3b. Due to
the fact that the encoder is a core encoder and due to the fact that there can, but does not
necessarily have to be, components of the first set of spectral portions in each band, the
core encoder calculates a scale factor for each band not only in the core range below the
IGF start frequency 309, but also above the IGF start frequency until the maximum
frequency f , F, which is smaller or equal to the half of the sampling frequency, i.e., fs 2 .
Thus, the encoded tonal portions 302, 304, 305, 306, 307 of Fig. 3a and, in this
embodiment together with the scale factors SCB1 to SCB7 correspond to the high
resolution spectral data. The low resolution spectral data are calculated starting from the
IGF start frequency and correspond to the energy information values E , E2, E3, E , which
are transmitted together with the scale factors SF4 to SF7.
Particularly, when the core encoder is under a low bitrate condition, an additional noisefilling
operation in the core band, i.e., lower in frequency than the IGF start frequency, i.e.,
in scale factor bands SCB1 to SCB3 can be applied in addition. In noise-filling, there exist
several adjacent spectral lines which have been quantized to zero. On the decoder-side,
these quantized to zero spectral values are re-synthesized and the re-synthesized
spectral values are adjusted in their magnitude using a noise-fiiiing energy such as NF2
illustrated at 308 in Fig . 3b. The noise-filling energy, which can be given in absolute terms
or in relative terms particularly with respect to the scale factor as in USAC corresponds to
the energy of the set of spectral values quantized to zero. These noise-filling spectral lines
can also be considered to be a third set of third spectral portions which are regenerated
by straightforward noise-filling synthesis without any IGF operation relying on frequency
regeneration using frequency tiles from other frequencies for reconstructing frequency
tiles using spectral values from a source range and the energy information E , E2, E3, E4.
Preferably, the bands, for which energy information is calculated coincide with the scale
factor bands. In other embodiments, an energy information value grouping is applied so
that, for example, for scale factor bands 4 and 5 , only a single energy information value is
transmitted, but even in this embodiment, the borders of the grouped reconstruction bands
coincide with borders of the scale factor bands. If different band separations are applied ,
then certain re-calculations or synchronization calculations may be applied, and this can
make sense depending on the certain implementation .
Preferably, the spectral domain encoder 106 of Fig . 1a is a psycho-acoustically driven
encoder as illustrated in Fig . 4a. Typically, as for example illustrated in the MPEG2/4 AAC
standard or MPEG1/2, Layer 3 standard, the to be encoded audio signal after having been
transformed into the spectral range (40 1 in Fig. 4a) is forwarded to a scale factor
calculator 400. The scale factor calculator is controlled by a psycho-acoustic model
additionally receiving the to be quantized audio signal or receiving , as in the MPEG 1/2
Layer 3 or MPEG AAC standard, a complex spectral representation of the audio signal.
The psycho-acoustic model calculates, for each scale factor band, a scale factor
representing the psycho-acoustic threshold . Additionally, the scale factors are then , by
cooperation of the well-known inner and outer iteration loops or by any other suitable
encoding procedure adjusted so that certain bitrate conditions are fulfilled . Then, the to be
quantized spectral values on the one hand and the calculated scale factors on the other
hand are input into a quantizer processor 404. In the straightforward audio encoder
operation, the to be quantized spectral values are weighted by the scale factors and, the
weighted spectral values are then input into a fixed quantizer typically having a
compression functionality to upper amplitude ranges. Then, at the output of the quantizer
processor there do exist quantization indices which are then forwarded into an entropy
encoder typically having specific and very efficient coding for a set of zero-quantization
indices for adjacent frequency values or, as also called in the art, a "run" of zero values.
In the audio encoder of Fig. 1a, however, the quantizer processor typically receives
information on the second spectral portions from the spectral analyzer. Thus, the
quantizer processor 404 makes sure that, in the output of the quantizer processor 404, the
second spectral portions as identified by the spectral analyzer 102 are zero or have a
representation acknowledged by an encoder or a decoder as a zero representation which
can be very efficiently coded, specifically when there exist "runs" of zero values in the
spectrum.
Fig. 4b illustrates an implementation of the quantizer processor. The MDCT spectral
values can be input into a set to zero block 410. Then, the second spectral portions are
already set to zero before a weighting by the scale factors in block 4 12 is performed. In an
additional implementation, block 410 is not provided, but the set to zero cooperation is
performed in block 418 subsequent to the weighting block 412. In an even further
implementation, the set to zero operation can also be performed in a set to zero block 422
subsequent to a quantization in the quantizer block 420. In this implementation, blocks
410 and 418 would not be present. Generally, at least one of the blocks 410, 418, 422 are
provided depending on the specific implementation.
Then, at the output of block 422, a quantized spectrum is obtained corresponding to what
is illustrated in Fig. 3a. This quantized spectrum is then input into an entropy coder such
as 232 in Fig. 2b which can be a Huffman coder or an arithmetic coder as, for example,
defined in the USAC standard.
The set to zero blocks 410, 418, 422, which are provided alternatively to each other or in
parallel are controlled by the spectral analyzer 424. The spectral analyzer preferably
comprises any implementation of a well-known tonality detector or comprises any different
kind of detector operative for separating a spectrum into components to be encoded with
a high resolution and components to be encoded with a low resolution. Other such
algorithms implemented in the spectral analyzer can be a voice activity detector, a noise
detector, a speech detector or any other detector deciding, depending on spectral
information or associated metadata on the resolution requirements for different spectral
portions.
Fig. 5a illustrates a preferred implementation of the time spectrum converter 100 of Fig.
1a as, for example, implemented in AAC or USAC. The time spectrum converter 100
comprises a windower 502 controlled by a transient detector 504. When the transient
detector 504 detects a transient, then a switchover from long windows to short windows is
signaled to the windower. The windower 502 then calculates, for overlapping blocks,
windowed frames, where each windowed frame typically has two N values such as 2048
values. Then, a transformation within a block transformer 506 is performed, and this block
transformer typically additionally provides a decimation, so that a combined
decimation/transform is performed to obtain a spectral frame with N values such as MDCT
spectral values. Thus, for a long window operation, the frame at the input of block 506
comprises two N values such as 2048 values and a spectral frame then has 1024 values.
Then, however, a switch is performed to short blocks, when eight short blocks are
performed where each short block has 1/8 windowed time domain values compared to a
long window and each spectral block has 1/8 spectral values compared to a long block.
Thus, when this decimation is combined with a 50% overlap operation of the windower,
the spectrum is a critically sampled version of the time domain audio signal 99.
Subsequently, reference is made to Fig. 5b illustrating a specific implementation of
frequency regenerator 116 and the spectrum-time converter 118 of Fig. 1b, or of the
combined operation of blocks 208, 212 of Fig. 2a. In Fig. 5b, a specific reconstruction
band is considered such as scale factor band 6 of Fig. 3a. The first spectral portion in this
reconstruction band, i.e., the first spectral portion 306 of Fig. 3a is input into the frame
builder/adjustor block 510. Furthermore, a reconstructed second spectral portion for the
scale factor band 6 is input into the frame builder/adjuster 510 as well. Furthermore,
energy information such as E3 of Fig. 3b for a scale factor band 6 is also input into block
510. The reconstructed second spectral portion in the reconstruction band has already
been generated by frequency tile filling using a source range and the reconstruction band
then corresponds to the target range. Now, an energy adjustment of the frame is
performed to then finally obtain the complete reconstructed frame having the N values as,
for example, obtained at the output of combiner 208 of Fig. 2a. Then, in block 512, an
inverse block transform/interpolation is performed to obtain 248 time domain values for
the for example 124 spectral values at the input of block 512. Then, a synthesis
windowing operation is performed in block 514 which is again controlled by a long
window/short window indication transmitted as side information in the encoded audio
signal. Then, in block 516, an overlap/add operation with a previous time frame is
performed. Preferably, MDCT applies a 50% overlap so that, for each new time frame of
2N values, N time domain values are finally output. A 50% overlap is heavily preferred
due to the fact that it provides critical sampling and a continuous crossover from one
frame to the next frame due to the overlap/add operation in block 516.
As illustrated at 301 in Fig. 3a, a noise-filling operation can additionally be applied not only
below the IGF start frequency, but also above the IGF start frequency such as for the
contemplated reconstruction band coinciding with scale factor band 6 of Fig. 3a. Then,
noise-filling spectral values can also be input into the frame builder/adjuster 510 and the
adjustment of the noise-filling spectral values can also be applied within this block or the
noise-filling spectral values can already be adjusted using the noise-filling energy before
being input into the frame builder/adjuster 510.
Preferably, an IGF operation, i.e., a frequency tile filling operation using spectral values
from other portions can be applied in the complete spectrum. Thus, a spectral tile filling
operation can not only be applied in the high band above an IGF start frequency but can
also be applied in the low band. Furthermore, the noise-filling without frequency tile filling
can also be applied not only below the IGF start frequency but also above the IGF start
frequency. It has, however, been found that high quality and high efficient audio encoding
can be obtained when the noise-filling operation is limited to the frequency range below
the IGF start frequency and when the frequency tile filling operation is restricted to the
frequency range above the IGF start frequency as illustrated in Fig. 3a.
Preferably, the target tiles (TT) (having frequencies greater than the IGF start frequency)
are bound to scale factor band borders of the full rate coder. Source tiles (ST), from which
information is taken, i.e., for frequencies lower than the IGF start frequency are not bound
by scale factor band borders. The size of the ST should correspond to the size of the
associated TT. This is illustrated using the following example. TT[0] has a length of 10
MDCT Bins. This exactly corresponds to the length of two subsequent SCBs (such as 4 +
6). Then, all possible ST that are to be correlated with TT[0], have a length of 10 bins, too.
A second target tile TT[1] being adjacent to TT[0] has a length of 15 bins I (SCB having a
length of 7 + 8). Then, the ST for that have a length of 15 bins rather than 10 bins as for
TT[0].
Should the case arise that one cannot find a TT for an ST with the length of the target tile
(when e.g. the length of TT is greater than the available source range), then a correlation
is not calculated and the source range is copied a number of times into this TT (the
copying is done one after the other so that a frequency line for the lowest frequency of the
second copy immediately follows - in frequency - the frequency line for the highest
frequency of the first copy), until the target tile TT is completely filled up.
Subsequently, reference is made to Fig. 5c illustrating a further preferred embodiment of
the frequency regenerator 116 of Fig. 1b or the IGF block 202 of Fig. 2a. Block 522 is a
frequency tile generator receiving, not only a target band ID, but additionally receiving a
source band ID. Exemplarily, it has been determined on the encoder-side that the scale
factor band 3 of Fig. 3a is very well suited for reconstructing scale factor band 7 . Thus, the
source band ID would be 2 and the target band ID would be 7 . Based on this information,
the frequency tile generator 522 applies a copy up or harmonic tile filling operation or any
other tile filling operation to generate the raw second portion of spectral components 523.
The raw second portion of spectral components has a frequency resolution identical to the
frequency resolution included in the first set of first spectral portions.
Then, the first spectral portion of the reconstruction band such as 307 of Fig. 3a is input
into a frame builder 524 and the raw second portion 523 is also input into the frame
builder 524. Then, the reconstructed frame is adjusted by the adjuster 526 using a gain
factor for the reconstruction band calculated by the gain factor calculator 528. Importantly,
however, the first spectral portion in the frame is not influenced by the adjuster 526, but
only the raw second portion for the reconstruction frame is influenced by the adjuster 526.
To this end, the gain factor calculator 528 analyzes the source band or the raw second
portion 523 and additionally analyzes the first spectral portion in the reconstruction band
to finally find the correct gain factor 527 so that the energy of the adjusted frame output by
the adjuster 526 has the energy E when a scale factor band 7 is contemplated.
In this context, it is very important to evaluate the high frequency reconstruction accuracy
of the present invention compared to HE-AAC. This is explained with respect to scale
factor band 7 in Fig. 3a. It is assumed that a prior art encoder such as illustrated in Fig.
13a would detect the spectral portion 307 to be encoded with a high resolution as a
"missing harmonics". Then, the energy of this spectral component would be transmitted
together with a spectral envelope information for the reconstruction band such as scale
factor band 7 to the decoder. Then, the decoder would recreate the missing harmonic.
However, the spectral value, at which the missing harmonic 307 would be reconstructed
by the prior art decoder of Fig. 13b would be in the middle of band 7 at a frequency
indicated by reconstruction frequency 390. Thus, the present invention avoids a frequency
error 391 which would be introduced by the prior art decoder of Fig. 13d.
In an implementation, the spectral analyzer is also implemented to calculating similarities
between first spectral portions and second spectral portions and to determine, based on
the calculated similarities, for a second spectral portion in a reconstruction range a first
spectral portion matching with the second spectral portion as far as possible. Then, in this
variable source range/destination range implementation, the parametric coder will
additionally introduce into the second encoded representation a matching information
indicating for each destination range a matching source range. On the decoder-side, this
information would then be used by a frequency tile generator 522 of Fig. 5c illustrating a
generation of a raw second portion 523 based on a source band D and a target band ID.
Furthermore, as illustrated in Fig. 3a, the spectral analyzer is configured to analyze the
spectral representation up to a maximum analysis frequency being only a small amount
below half of the sampling frequency and preferably being at least one quarter of the
sampling frequency or typically higher.
As illustrated, the encoder operates without downsampling and the decoder operates
without upsampling. In other words, the spectral domain audio coder is configured to
generate a spectral representation having a Nyquist frequency defined by the sampling
rate of the originally input audio signal.
Furthermore, as illustrated in Fig. 3a, the spectral analyzer is configured to analyze the
spectral representation starting with a gap filling start frequency and ending with a
maximum frequency represented by a maximum frequency included in the spectral
representation, wherein a spectral portion extending from a minimum frequency up to the
gap filling start frequency belongs to the first set of spectral portions and wherein a further
spectral portion such as 304, 305, 306, 307 having frequency values above the gap filling
frequency additionally is included in the first set of first spectral portions.
As outlined, the spectral domain audio decoder 112 is configured so that a maximum
frequency represented by a spectral value in the first decoded representation is equal to a
maximum frequency included in the time representation having the sampling rate wherein
the spectral value for the maximum frequency in the first set of first spectral portions is
zero or different from zero. Anyway, for this maximum frequency in the first set of spectral
components a scale factor for the scale factor band exists, which is generated and
transmitted irrespective of whether all spectral values in this scale factor band are set to
zero or not as discussed in the context of Figs. 3a and 3b.
The invention is, therefore, advantageous that with respect to other parametric techniques
to increase compression efficiency, e.g. noise substitution and noise-filling (these
techniques are exclusively for efficient representation of noise like local signal content) the
invention allows an accurate frequency reproduction of tonal components. To date, no
state-of-the-art technique addresses the efficient parametric representation of arbitrary
signal content by spectral gap filling without the restriction of a fixed a-priory division in
low band (LF) and high band (HF).
Embodiments of the inventive system improve the state-of-the-art approaches and
thereby provides high compression efficiency, no or only a small perceptual annoyance
and full audio bandwidth even for low bitrates.
The general system consists of
• full band core coding
• intelligent gap filling (tile filling or noise-filling)
· sparse tonal parts in core selected by tonal mask
• joint stereo pair coding for full band, including tile filling
• TNS on tile
• spectral whitening in IGF range
A first step towards a more efficient system is to remove the need for transforming spectral
data into a second transform domain different from the one of the core coder. As the
majority of audio codecs, such as AAC for instance, use the MDCT as basic transform, it
is useful to perform the BWE in the MDCT domain also. A second requirement for the
B E system would be the need to preserve the tonal grid whereby even HF tonal
components are preserved and the quality of the coded audio is thus superior to the
existing systems. To take care of both the above mentioned requirements a system has
been proposed called Intelligent Gap Filling (IGF). Fig. 2b shows the block diagram of the
proposed system on the encoder-side and Fig. 2a shows the system on the decoder-side.
Subsequently, a post-processing framework is described with respect to Fig 13A and Fig.
13B in order to illustrate that the present invention can also be implemented in the high
frequency reconstructer 1330 in this post-processing embodiment.
Fig. 13a illustrates a schematic diagram of an audio encoder for a bandwidth extension
technology as, for example, used in High Efficiency Advanced Audio Coding (HE-AAC).
An audio signal at line 1300 is input into a filter system comprising of a low pass 1302 and
a high pass 1304. The signal output by the high pass filter 1304 is input into a parameter
extractor/coder 1306. The parameter extractor/coder 1306 is configured for calculating
and coding parameters such as a spectral envelope parameter, a noise addition
parameter, a missing harmonics parameter, or an inverse filtering parameter, for example.
These extracted parameters are input into a bit stream multiplexer 1308. The low pass
output signal is input into a processor typically comprising the functionality of a down
sampler 1310 and a core coder 1312. The low pass 1302 restricts the bandwidth to be
encoded to a significantly smaller bandwidth than occurring in the original input audio
signal on line 1300. This provides a significant coding gain due to the fact that the whole
functionalities occurring in the core coder only have to operate on a signal with a reduced
bandwidth. When, for example, the bandwidth of the audio signal on line 1300 is 20 kHz
and when the low pass filter 302 exemplarily has a bandwidth of 4 kHz, in order to fulfill
the sampling theorem, it is theoretically sufficient that the signal subsequent to the down
sampler has a sampling frequency of 8 kHz, which is a substantial reduction to the
sampling rate required for the audio signal 1300 which has to be at least 40 kHz.
Fig. 13b illustrates a schematic diagram of a corresponding bandwidth extension decoder.
The decoder comprises a bitstream multiplexer 1320. The bitstream demultiplexer 1320
extracts an input signal for a core decoder 1322 and an input signal for a parameter
decoder 1324. A core decoder output signal has, in the above example, a sampling rate of
8 kHz and, therefore, a bandwidth of 4 kHz while, for a complete bandwidth
reconstruction, the output signal of a high frequency reconstructor 1330 must be at 20 kHz
requiring a sampling rate of at least 40 kHz. In order to make this possible, a decoder
processor having the functionality of an upsampler 1325 and a filterbank 1326 is required.
The high frequency reconstructor 1330 then receives the frequency-analyzed low
frequency signal output by the filterbank 1326 and reconstructs the frequency range
defined by the high pass filter 1304 of Fig. 13a using the parametric representation of the
high frequency band. The high frequency reconstructor 1330 has several functionalities
such as the regeneration of the upper frequency range using the source range in the low
frequency range, a spectral envelope adjustment, a noise addition functionality and a
functionality to introduce missing harmonics in the upper frequency range and, if applied
and calculated in the encoder of Fig. 13a, an inverse filtering operation in order to account
for the fact that the higher frequency range is typically not as tonal as the lower frequency
range. In HE-AAC, missing harmonics are re-synthesized on the decoder-side and are
placed exactly in the middie of a reconstruction band. Hence, aii missing harmonic lines
that have been determined in a certain reconstruction band are not placed at the
frequency values where they were located in the original signal. Instead, those missing
harmonic lines are placed at frequencies in the center of the certain band. Thus, when a
missing harmonic line in the original signal was placed very close to the reconstruction
band border in the original signal, the error in frequency introduced by placing this missing
harmonics line in the reconstructed signal at the center of the band is close to 50% of the
individual reconstruction band, for which parameters have been generated and
transmitted.
Furthermore, even though the typical audio core coders operate in the spectral domain,
the core decoder nevertheless generates a time domain signal which is then, again,
converted into a spectral domain by the filter bank 1326 functionality. This introduces
additional processing delays, may introduce artifacts due to tandem processing of firstly
transforming from the spectral domain into the frequency domain and again transforming
into typically a different frequency domain and, of course, this also requires a substantial
amount of computation complexity and thereby electric power, which is specifically an
issue when the bandwidth extension technology is applied in mobile devices such as
mobile phones, tablet or laptop computers, etc.
Although some aspects have been described in the context of an apparatus for encoding
or decoding, 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
represent 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 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
non-transitory storage medium such as a digital storage medium, for example a floppy
disc, a Hard Disk Drive (HDD), a DVD, a Blu-Ray, a CD, a ROM, a PROM, and 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 code may, for example, be stored on a machine readable carrier.
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.
A further embodiment of the inventive method is, therefore, a data carrier (or a digital
storage medium, or a computer-readable medium) comprising, recorded thereon, the
computer program for performing one of the methods described herein. The data carrier,
the digital storage medium or the recorded medium are typically tangible and/or nontransitory.
A further embodiment of the invention method is, therefore, a data stream or a sequence
of signals representing the computer 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.
A further embodiment comprises a processing means, for example, a computer or a
programmable logic 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 programmable 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 array 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.

Claims
1. Apparatus for generating an enhanced signal from an input signal (600), wherein
the enhanced signal has spectral values for an enhancement spectral region, the
spectral values for the enhancement spectral regions not being contained in the
input signal (600), comprising:
a mapper (602) for mapping a source spectral region of the input signal to a target
region in the enhancement spectral region, the source spectral region comprising a
noise-filling region (302); and
a noise filler (604) configured for generating first noise values for the noise-filling
region (302) in the source spectral region of the input signal and for generating
second noise values for a noise region in the target region, wherein the second
noise values are decorrelated from the first noise values or for generating second
noise values for a noise region in the target region, wherein the second noise
values are decorrelated from first noise values in the source region,
wherein the noise filler (604) is configured to identify noise positions using an
identification vector (706) having entries for spectral positions in the source
spectral region only, or having entries for spectral positions in the source spectral
region and in the target spectral region,
wherein the noise filler (604) is configured for calculating an energy information on
noise values indicated by the identification vector (706),
wherein the noise filler (604) is configured to calculate an energy information on
inserted random values intended for the target region,
wherein the noise filler is configured to calculate a gain factor for scaling the
random values, and
wherein the noise filler is configured to apply the gain factor to the inserted random
values.
2 . Apparatus of claim 1
wherein the input signal is an encoded signal comprising noise-filling parameters
for the source spectral region of the input signal,
wherein the noise filler is configured for generating the first noise values using the
noise-filling parameters and for generating the second noise values using an
energy information on the first noise values.
3 . Apparatus of claim 1 or 2 ,
wherein the noise filler is configured to calculate a gain factor for scaling the
random values using energy information on noise in the source range and the
energy information on the random values intended for the target region.
4 . Apparatus of one of the preceding claims,
wherein the noise filler (604) is configured for generating the second noise value
subsequent to an operation of the mapper (602) or for generating (604) the first
and the second noise values subsequent to an operation of the mapper (602).
5 . Apparatus of one of the preceding claims,
wherein the mapper (602) is configured to map the source spectral region to the
target region, and
wherein the noise filler (604) is configured to perform noise-filling in spectral
regions by generating the first noise values using noise-filling and noise-filling
parameters transmitted in the input signal as side information, and to perform
noise-filling in the target region to generate the second spectral values using
energy information on the first noise values.
6 . Apparatus of one of the preceding claims, further comprising:
an envelope adjuster for adjusting (1202) the second noise values in the
enhancement spectral region using spectral envelope information included in the
input signal as side information.
Apparatus of one of the preceding claims,
wherein the noise filler (604) is configured to only use side information of the input
signal to identify spectral positions for noise-filling, or
wherein the noise filler (604) is configured to analyze a time or spectral
characteristic of the input signal with or without spectral values in the noise-filling
region to identify spectral positions for noise-filling.
Apparatus of one of the preceding claims,
wherein the mapper (602) is configured to perform a gap filling operation for
generating the target spectral region, the apparatus comprising:
a spectral domain audio decoder ( 1 12) for generating a first decoded
representation of a first set of first spectral portions, the decoded representation
having a first spectral resolution;
a parametric decoder ( 1 14) for generating a second decoded representation of a
second set of second spectral portions having a second spectral resolution being
lower than the first spectral resolution;
a frequency regenerator ( 1 16) for regenerating a reconstructed second spectral
portion having the first spectral resolution using a first spectral portion and spectral
envelope information for the second spectral portion; and
a spectrum time converter ( 1 18) for converting the first decoded representation in
the reconstructed second spectral portion into a time representation,
wherein the mapper (602) and the noise filler (604) are at least partly included in
the frequency regenerator ( 16).
9 . Apparatus of claim 8 ,
wherein the spectral domain audio decoder is configured to output a sequence of
decoded frames of spectral values, a decoded frame being the first decoded
representation, wherein the frame comprises spectrai values for the first sei of
spectral portions and zero indications for the second set of second spectral
portions,
wherein the apparatus for decoding further comprises a combiner (208) for
combining spectral values generated by the frequency regenerator for the second
set of second spectral portions and spectral values of the first set of first spectral
portions in a reconstruction band to obtain a reconstructed spectral frame
comprising spectral values for the first set of the first spectral portions and the
second set of second spectral portions; and
wherein the spectrum-time converter ( 1 18) is configured to convert the
reconstructed spectral frame into the time representation.
Apparatus of one of the preceding claims, further comprising:
for each target frequency range, a source region identification, and
wherein the mapper (602) is configured for selecting the source region using the
source region identification and for mapping the selected source region to the
target region.
Method of generating an enhanced signal from an input signal (600), wherein the
enhanced signal has spectral values for an enhancement spectral region, the
spectral values for the enhancement spectral regions not being contained in the
input signal (600), comprising:
mapping (602) a source spectral region of the input signal to a target region in the
enhancement spectral region, the source spectral region comprising a noise-filling
region (302); and
generating (604) first noise values for the noise-filling region (302) in the source
spectral region of the input signal and for generating second noise values for a
noise region in the target region, wherein the second noise values are decorrelated
from the first noise values or for generating second noise values for a noise region
in the target region, wherein the second noise values are decorrelated from first
noise values in the source region,
wherein the generating (604) comprises:
identifying noise positions using an identification vector (706) having entries
for spectral positions in the source spectral region only, or having entries
for spectral positions in the source spectral region and in the target spectral
region,
calculating an energy information on noise values indicated by the
identification vector (706),
calculating an energy information on inserted random values intended for
the target region,
calculating a gain factor for scaling the random values, and
applying the gain factor to the inserted random values.
12. Method of claim 11, wherein the generating (604) comprises calculating a gain
factor for scaling the random values using energy information on noise in the
source range and the energy information on the random values intended for the
target region.
13. System for processing an audio signal, comprising:
an encoder for generating an encoded signal; and
the apparatus for generating an enhanced signal in accordance with any one of
claims 1 to 10, wherein the encoded signal is subjected to a processing (700) in
order to generate the input signal (600) into the apparatus for generating the
enhanced signal.
14. Method for processing an audio signal, comprising:
generating an encoded signal from an input signal; and
a method of generating an enhanced signal in accordance with claim 1, wherein
the encoded signal is subjected to a predefined processing (700) in order to
generate the input signal (600) into the apparatus for generating the enhanced
signal.
15.Computer program for performing, when running on a computer, the method of
claim 11 or claim 14.