Apparatus and Method for Selecting One of a First Encoding Algorithm and a
Second Encoding Algorithm using harmonics reduction
Specification
The present invention relates to audio coding and. in particular, to switched audio coding,
where, for different portions of an audio signal, the encoded signal is generated using
different encoding algorithms
Switched audio coders which determine different encoding algorithms for different portions
of the audio signal are known Generally, switched audio coders provide for switching
between two different modes, i.e. algorithms, such as ACELP (Algebraic Code Excited
Linear Prediction) and TCX (Transform Coded Excitation).
The LPD mode of MPEG USAC (MPEG Unified Speech Audio Coding) is based on the
two different modes ACELP and TCX. ACELP provides betler quality for speech-like and
transient-like signals TCX provides better quality for music-like and noise-like signals
The encoder decides which mode to use on a frame-by-frame basis The decision made
by the encoder is critical for the codec quality A single wrong decision can produce a
strong artifact, particularly at low-bitrates
The most-straightforward approach for deciding which mode to use is a closed-loop mode
selection, i.e. to perform a complete encoding/decoding of both modes then compute a
selection criteria (eg segmental SNR) for both modes based on the audio signal and the
coded/decoded audio signals, and finally choose a mode based on the selection criteria.
This approach generally produces a stable and robust decision. However, it also requires
a significant amount of complexity, because both modes have to be run at each frame.
To reduce the complexity an alternative approach is the open-loop mode selection. Open-
loop selection consists of not performing a complete encoding/decoding of both modes
but instead choose one mode using a selection criteria computed with low-complexity.
The worst-case complexity is then reduced by the complexity of the least-complex mode
(usually TCX). minus the complexity needed to compute the selection criteria. The save in
complexity is usually significant which makes this kind of approach attractive when the
codec worst-case complexity is constrained

The AMR-WB+ standard (denned in the International Standard 3GPP TS 26.290 V6.1.0
2004-12) includes an open-loop mode selection, used to decide between all combinations
of ACELP/TCX20/TCX40/TCX80 in a 80ms frame. It is described in Section 5 24 of
3GPP TS 26 290 It is also described in the conference paper "Low Complex Audio
Encoding for Mobile, Multimedia. VTC 2006, Makmen et ai" and US 7,747,430 B2 and US
7,739.120 B2 going back to the author of this conference paper
US 7,747,430 B2 discloses an open-loop mode selection based on an analysis of long
term prediction parameters. US 7,739.120 B2 discloses an open-loop mode selection
based on signal characteristics indicating the type of audio content in respective sections
of an audio signal, wherein, if such a selection is not viable, the selection is further based
on a statistical evaluation carried out for respectively neighboring sections
The open-loop mode selection of AMR-WB+ can be described in two main steps In the
first mam step, several features are calculated on the audio signal, such as standard
deviation of energy levels, low-frequency/high-frequency energy relation, total energy, ISP
(immittance spectral pair) distance, pitch lags and gains, spectral tilt These features are
then used to make a choice between ACELP and TCX, using a simple threshold-based
classifier If TCX is selected in the first mam step, then the second main step decides
between the possible combinations of TCX20/TCX40/TCX80 in a closed-loop manner
WO 2012/110448 A1 discloses an approach for deciding between two encoding
algorithms having different characteristics based on a transient detection result and a
quality result of an audio signal In addition, applying a hysteresis is disclosed, wherein
the hysteresis relies on the selections made in the past. i.e. for the earlier portions of the
audio signal
In the conference paper "Low Complex Audio Encoding for Mobile. Multimedia. VTC 2006,
Makinen et al.". the closed-loop and open-loop mode selection of AMR-WB+ are
compared Subjective listening tests indicate that the open-loop mode selection performs
significantly worse than the closed-loop mode selection But it is also shown that the
open-loop mode selection reduces the worst-case complexity by 40%

It is the object of the invention to provide for an improved approach which permits for
selection between a first encoding algorithm and a second encoding algorithm with good
performance and reduced complexity.
This object is achieved by an apparatus according to claim 1, a method according to claim
18, and a computer program according to claim 19.
Embodiments of the invention provide an apparatus for selecting one of a first encoding
algorithm having a first characteristic and a second encoding algorithm having a second
characteristic for encoding a portion of an audio signal to obtain an encoded version of the
portion of the audio signal, comprising:
a filter configured to receive the audio signal, to reduce the amplitude of harmonics in the
audio signal and to output a filtered version of the audio signal;
a first estimator for using the filtered version of the audio signal in estimating a SNR
(signal to noise ratio) or a segmented SNR of the portion of the audio signal as a first
quality measure for the portion of the audio signal, which is associated with the first
encoding algorithm, without actually encoding and decoding the portion of the audio signal
using the first encoding algorithm;
a second estimator for estimating a SNR or a segmented SNR as a second quality
measure for the portion of the audio signal, which is associated with the second encoding
algorithm, without actually encoding and decoding the portion of the audio signal using the
second encoding algorithm; and
a controller for selecting the first encoding algorithm or the second encoding algorithm
based on a comparison between the first quality measure and the second quality
measure.
Embodiments of the invention provide a method for selecting one of a first encoding
algorithm having a first characteristic and a second encoding algorithm having a second
characteristic for encoding a portion of an audio signal to obtain an encoded version of the
portion of the audio signal, comprising:

filtering the audio signal to reduce the amplitude of harmonics in the audio signal and to
output a filtered version of the audio signal,
using the filtered version of the audio signal in estimating a SNR or a segmental SNR of
the portion of the audio signal as a first quality measure for the portion of the audio signal,
which is associated with the first encoding algorithm, without actually encoding and
decoding the portion of the audio signal using the first encoding algorithm;
estimating a second quality measure for the portion of the audio signal, which is
associated with the second encoding algorithm, without actually encoding and decoding
the portion of the audio signal using the second encoding algorithm, and
selecting the first encoding algorithm or the second encoding algorithm based on a
comparison between the first quality measure and the second quality measure.
Embodiments of the invention are based on the recognition that an open-loop selection
with improved performance can be implemented by estimating a quality measure for each
of first and second encoding algorithms and selecting one of the encoding algorithms
based on a comparison between the first and second quality measures. The quality
measures are estimated, i e the audio signal is not actually encoded and decoded to
obtain the quality measures. Thus, the quality measures can be obtained with reduced
complexity. The mode selection may then be performed using the estimated quality
measures comparable to a closed-loop mode selection. Moreover, the invention is based
on the recognition that an improved mode selection can be obtained if the estimation of
the first quality measure uses a filtered version of the portion of the audio signal, in which
harmonics are reduced when compared to the non-filtered version of the audio signal
In embodiments of the invention, an open-loop mode selection where the segmental SNR
of ACELP and TCX are first estimated with low complexity is implemented And then the
mode selection is performed using these estimated segmental SNR values, like in a
closed-loop mode selection.
Embodiments of the invention do not employ a classical features+classifier approach like
it is done in the open-loop mode selection of AMR-WB+. But instead, embodiments of the
invention try to estimate a quality measure of each mode and select the mode that gives
the best quality.

Embodiments of the present invention will now be described in further detail with
reference to the accompanying drawings, in which:
Fig. 1 shows a schematic view of an embodiment of an apparatus for selecting one of a
first encoding algorithm and a second encoding algorithm;
Fig. 2 shows a schematic view of an embodiment of an apparatus for encoding an audio
signal;
Fig. 3 shows a schematic view of an embodiment of an apparatus for selecting one of a
first encoding algorithm and a second encoding algorithm;
Fig 4a and 4b possible representations of SNR and segmental SNR.
In the following description, similar elements/steps in the different drawings are referred to
by the same reference signs. It is to be noted that in the drawings features,such as signal
connections and the like, which are not necessary in understanding the invention have
been omitted
Fig. 1 shows an apparatus 10 for selecting one of a first encoding algorithm, such as a
TCX algorithm, and a second encoding algorithm, such as an ACELP algorithm, as the
encoder for encoding a portion of an audio signal. The apparatus 10 comprises a first
estimator 12 for estimating a SNR or a segmental SNR of the portion of the audio signal
as first quality measure for the signal portion is provided. The first quality measure is
associated with the first encoding algorithm. The apparatus 10 comprises a filter 2
configured to receive the audio signal, to reduce the amplitude of harmonics in the audio
signal and to output a filtered version of the audio signal. The filter 2 may be internal to the
first estimator 12 as shown in Fig. 1 or may be external to the first estimator 12 The first
estimator 12 uses the filtered version of the audio signal in estimating the first quality
measure. In other words, the first estimator 12 estimates a first quality measure which the
portion of the audio signal would have if encoded and decoded using the first encoding
algorithm, without actually encoding and decoding the portion of the audio signal using the
first encoding algorithm. The apparatus 10 comprises a second estimator 14 for
estimating a second quality measure for the signal portion. The second quality measure is
associated with the second encoding algorithm. In other words, the second estimator 14

estimates the second quality measure which the portion of the audio signal would have if
encoded and decoded using the second encoding algorithm, without actually encoding
and decoding the portion of the audio signal using the second encoding algorithm.
Moreover, the apparatus 10 comprises a controller 16 for selecting the first encoding
algorithm or the second encoding algorithm based on a comparison between the first
quality measure and the second quality measure. The controller may comprise an output
18 indicating the selected encoding algorithm.
In the following specification, the first estimator uses the filtered version of the audio
signal, i.e. the filtered version of the portion of the audio signal in estimating the first
quality measure if the filter 2 configured to reduce the amplitude of harmonics is provided
and is not disabled, even if not explicitly indicated.
In an embodiment, the first characteristic associated with the first encoding algorithm is
better suited for music-like and noise-like signals, and the second encoding characteristic
associated with the second encoding algorithm is better suited for speech-like and
transient-like signals. In embodiments of the invention, the first encoding algorithm is an
audio coding algorithm, such as a transform coding algorithm, eg. a MDCT (modified
discrete cosine transform) encoding algorithm, such as a TCX (transform coding
excitation) encoding algorithm. Other transform coding algorithms may be based on an
FFT transform or any other transform or filterbank. In embodiments of the invention, the
second encoding algorithm is a speech encoding algorithm, such as a CELP (code
excited linear prediction) coding algorithm, such as an ACELP (algebraic code excited
linear prediction) coding algorithm
In embodiments the quality measure represents a perceptual quality measure. A single
value which is an estimation of the subjective quality of the first coding algorithm and a
single value which is an estimation of the subjective quality of the second coding algorithm
may be computed. The encoding algorithm which gives the best estimated subjective
quality may be chosen just based on the comparison of these two values This is different
from what is done in the AMR-WB+ standard where many features representing different
characteristics of the signal are computed and, then, a classifier is applied to decide which
algorithm to choose
In embodiments, the respective quality measure is estimated based on a portion of the
weighted audio signal, i e a weighted version of the audio signal. In embodiments, the

weighted audio signal can be defined as an audio signal filtered by a weighting function;
where the weighting function is a weighted LPC filter A(z/g) with A(z) an LPC filter and g a
weight between 0 and 1 such as 0.68 It turned out that good measures of perceptual
quality can be obtained in this manner Note that the LPC filter A(z) and the weighted LPC
filter A(z/g) are determined m a pre-processing stage and that they are also used m both
encoding algorithms In other embodiments, the weighting function may be a linear filter, a
FIR filter or a linear prediction filter.
In embodiments, the quality measure is the segmental SNR (signal to noise ratio) in the
weighted signal domain. It turned out that the segmental SNR in the weighted signal
domain represents a good measure of the perceptual quality and, therefore, can be used
as the quality measure in a beneficial manner This is also the quality measure used m
both ACELP and TCX encoding algorithms to estimate the encoding parameters.
Another quality measure may be the SNR in the weighted signal domain. Other quality
measures may be the segmental SNR, the SNR of the corresponding portion of the audio
signal in the non-weighted signal domain, i.e not filtered by the (weighted) LPC
coefficients
Generally, SNR compares the original and processed audio signals (such as speech
signals) sample by sample Its goal is to measure the distortion of waveform coders that
reproduce the input waveform. SNR may be calculated as shown in Fig. 4a, where x(i)
and y(i) are the original and the processed samples indexed by i and N is the total number
of samples. Segmental SNR, instead of working on the whole signal, calculates the
average of the SNR values of short segments, such as 1 to 10 ms. such as 5ms SNR
may be calculated as shown in Fig. 4b, where N and M are the segment length and the
number of segments, respectively.
In embodiments of the invention, the portion of the audio signal represents a frame of the
audio signal which is obtained by windowing the audio signal and selection of an
appropriate encoding algorithm is performed for a plurality of successive frames obtained
by windowing an audio signal. In the following specification, in connection with the audio
signal, the terms "portion" and 'frame" are used in an exchangeable manner In
embodiments, each frame is divided into subframes and segmental SNR is estimated for
each frame by calculating SNR for each subframe, converted in dB and calculating the
average of the subframe SNRs in dB.

Thus, in embodiments, it is not the (segmental) SNR between the input audio signal and
the decoded audio signal that is estimated, but the (segmental) SNR between the
weighted input audio signal and the weighted decoded audio signal Is estimated As far as
this (segmental) SNR is concerned, reference can be made to chapter 5 2.3 of the AMR-
WB+ standard (International Standard 3GPP TS 26 290 V6 1 0 2004-12)
In embodiments of the invention, the respective quality measure is estimated based on
the energy of a portion of the weighted audio signal and based on an estimated distortion
introduced when encoding the signal portion by the respective algorithm, wherein the first
and second estimators are configured to determine the estimated distortions dependent
on the energy of a weighted audio signal.
In embodiments of the invention, an estimated quantizer distortion introduced by a
quantizer used in the first encoding algorithm when quantizing the portion of the audio
signal is determined and the first quality measure is determined based on the energy of
the portion of the weighted audio signal and the estimated quantizer distortion In such
embodiments, a global gain for the portion of the audio signal may be estimated such that
the portion of the audio signal would produce a given target bitrate when encoded with a
quantizer and an entropy encoder used in the first encoding algorithm, wherein the
estimated quantizer distortion is determined based on the estimated global gain In such
embodiments, the estimated quantizer distortion may be determined based on a power of
the estimated gain. When the quantizer used in the first encoding algorithm is a uniform
scalar quantizer, the first estimator may be configured to determine the estimated
quantizer distortion using the formula D = G*G/12, wherein D is the estimated quantizer
distortion and G is the estimated global gam In case the first encoding algorithm uses
another quantizer, the quantizer distortion may be determined form the globalgam in a
different manner.
The inventors recognized that a quality measure, such as a segmental SNR, which would
be obtained when encoding and decoding the portion of the audio signal using the first
encoding algorithm, such as the TCX algorithm, can be estimated in an appropriate
manner by using the above features in any combination thereof.
In embodiments of the invention, the first quality measure is a segmental SNR and the
segmental SNR is estimated by calculating an estimated SNR associated with each of a

plurality of sub-portions of the portion of the audio signal based on an energy of the
corresponding sub-portion of the weighted audio signal and the estimated quantizer
distortion and by calculating an average of the SNRs associated with the sub-portions of
the portion of the weighted audio signal to obtain the estimated segmental SNR for the
portion of the weighted audio signal
In embodiments of the invention, an estimated adaptive codebook distortion introduced by
an adaptive codebook used in the second encoding algorithm when using the adaptive
codebook to encode the portion of the audio signal is determined, and the second quality
measure is estimated based on an energy of the portion of the weighted audio signal and
the estimated adaptive codebook distortion.
In such embodiments, for each of a plurality of sub-portions of the portion of the audio
signal, the adaptive codebook may be approximated based on a version of the sub-portion
of the weighted audio signal shifted to the past by a pitch-lag determined in a pre-
processing stage, an adaptive codebook gain may be estimated such that an error
between the sub-portion of the portion of the weighted audio signal and the approximated
adaptive codebook is minimized, and an estimated adaptive codebook distortion may be
determined based on the energy of an error between the sub-portion of the portion of the
weighted audio signal and the approximated adaptive codebook scaled by the adaptive
codebook gain
In embodiments of the invention, the estimated adaptive codebook distortion determined
for each sub-portion of the portion of the audio signal may be reduced by a constant factor
in order to take into consideration a reduction of the distortion which is achieved by an
innovative codebook in the second encoding algorithm
In embodiments of the invention, the second quality measure is a segmental SNR and the
segmental SNR is estimated by calculating an estimated SNR associated with each sub-
portion based on the energy the corresponding sub-portion of the weighted audio signal
and the estimated adaptive codebook distortion and by calculating an average of the
SNRs associated with the sub-portions to obtain the estimated segmental SNR
In embodiments of the invention, the adaptive codebook is approximated based on a
version of the portion of the weighted audio signal shifted to the past by a pitch-lag
determined in a pre-processing stage, an adaptive codebook gain is estimated such that

an error between the portion of the weighted audio signal and the approximated adaptive
codebook is minimized, and the estimated adaptive codebook distortion is determined
based on the energy between the portion of the weighted audio signal and the
approximated adaptive codebook scaled by the adaptive codebook gam Thus, the
estimated adaptive codebook distortion can be determined with low complexity.
The inventors recognized that the quality measure, such as a segmental SNR, which
would be obtained when encoding and decoding the portion of the audio signal using the
second encoding algorithm, such as an ACELP algorithm, can be estimated in an
appropriate manner by using the above features in any combination thereof
In embodiments of the invention, a hysteresis mechanism is used in comparing the
estimated quality measures This can make the decision which algorithm is to be used
more stable The hysteresis mechanism can depend on the estimated quality measures
(such as the difference therebetween) and other parameters, such as statistics about
previous decisions, the number of temporally stationary frames, transients in the frames.
As far as such hysteresis mechanisms are concerned, reference can be made to WO
2012/110448 A1, for example
In embodiments of the invention, an encoder for encoding an audio signal comprises the
apparatus 10, a stage for performing the first encoding algorithm and a stage for
performing the second encoding algorithm, wherein the encoder is configured to encode
the portion of the audio signal using the first encoding algorithm or the second encoding
algorithm depending on the selection by the controller 16. In embodiments of the
invention, a system for encoding and decoding comprises the encoder and a decoder
configured to receive the encoded version of the portion of the audio signal and an
indication of the algorithm used to encode the portion of the audio signal and to decode
the encoded version of the portion of the audio signal using the indicated algorithm
Such an open-loop mode selection algorithm as shown in Fig. 1 and described above
(except for filter 2) is described in an earlier application PCT/EP2014/051557. This
algorithm is used to make a selection between two modes, such as ACELP and TCX, on a
frame-by-frame basis. The selection may be based on an estimation of the segmental
SNR of both ACELP and TCX. The mode with the highest estimated segmented SNR is
selected Optionally, a hysteresis mechanism can be used to provide a more robust
selection The segmental SNR of ACELP may be estimated using an approximation of the

adaptive codebook distortion and an approximation of the innovative codebook distortion
The adaptive codebook may be approximated in the weighted signal domain using a
pitch-lag estimated by a pitch analysis algorithm The distortion may be computed in the
weighted signal domain assuming an optimal gam. The distortion may then be reduced by
a constant factor, approximating the innovative codebook distortion The segmental SNR
of TCX may be estimated using a simplified version of the real TCX encoder The input
signal may first be transformed with an MDCT, and then shaped using a weighted LPC
filter Finally, the distortion may be estimated in the weighted MDCT domain, using a
global gain and a global gain estimator
It turned out that this open-loop mode selection algorithm as described in the earlier
application provides the expected decision most of the time, selecting ACELP on speech-
like and transient-like signals and TCX on music-like and noise-like signals However, the
inventors recognized that it might happen that ACELP is sometimes selected on some
harmonic music signals On such signals, the adaptive codebook generally has a high
prediction gain, due to the high predictability of harmonic signals, producing low distortion
and then higher segmental SNR than TCX. However, TCX sounds better on most
harmonic music signals, so TCX should be preferred in these cases.
Thus, the present invention suggests to perform the estimation of the SNR or the
segmental SNR as the first quality measure using a version of the input signal, which is
filtered to reduce harmonics thereof. Thus, an improved mode selection on harmonic
music signals can be obtained
Generally, any suitable filter for reducing harmonics could be used In embodiments of the
invention, the filter is a long-term prediction filter. One simple example of a long-term
prediction filter is

where the filter parameters are the gain 'g" and the pitch-lag "T", which are determined
from the audio signal
Embodiments of the invention are based on a long-term prediction filter that is applied to
the audio signal before the MDCT analysis in the TCX segmental SNR estimation The
long-term prediction filter reduces the amplitude of the harmonics in the input signal
before the MDCT analysis. The consequence is that the distortion in the weighted MDCT

domain is reduced the estimated segmental SNR of TCX is increased, and finally TCX is
selected more often on harmonics music signals
In embodiments of the invention, a transfer function of the long-term prediction filter
comprises an Integer part of a pitch lag and a multi tap filter depending on a fractional part
of the pitch lag. This permits for an efficient implementation since the integer part is used
m the normal sampling rate framework (z-Tint) only. At same time, high accuracy due to
the usage of the fractional part in the multi tap filter can be achieved By considering the
fractional part in the multi tap filter removal of the energy of the harmonics can be
achieved while removal of energy of portions near the harmonics is avoided.
In embodiments of the invention, the long-term prediction filter is described as follows:

wherein T,„t and T,. are the integer and fractional part of a pitch-lag, g is a gain, (3 is a
weight, and B(z,Tf, ) is a FIR low-pass filter whose coefficients depend on the fractional
part of the pitch lag Further details on embodiments of such a long-term prediction filter
will be set-forth below
The pitch-lag and the gain may be estimated on a frame-by-frame basis
The prediction filter can be disabled (gam=0) based on a combination of one or more
harmonicity measure(s) (e.g. normalized correlation or prediction gain) and/or one or more
temporal structure measure(s) (e g temporal flatness measure or energy change).
The filter may be applied to the input audio signal on a frame-by-frame basis If the filter
parameters change from one frame to the next, a discontinuity can be introduced at the
border between two frames In embodiments, the apparatus further comprises a unit for
removing discontinuities m the audio signal caused by the filter. To remove the possible
discontinuities, any technique can be used, such as techniques comparable to those
described in US5012517. EP0732687A2. US5999899A. or US7353168B2 Another
technique for removing possible discontinuities is described below.
Before describing an embodiment of the first estimator 12 and the second estimator 14 in
detail referring to Fig 3. an embodiment of an encoder 20 is described referring to Fig. 2.

The encoder 20 comprises the first estimator 12, the second estimator 14. the controller
16, a pre-processing unit 22, a switch 24, a first encoder stage 26 configured to perform a
TCX algorithm, a second encoder stage 28 configured to perform an ACELP algorithm,
and an output interface 30 The pre-processing unit 22 may be part of a common USAC
encoder and may be configured to output the LPC coefficients, the weighted LPC
coefficients, the weighted audio signal, and a set of pitch lags. It is to be noted that all
these parameters are used in both encoding algorithms, i.e. the TCX algorithm and the
ACELP algorithm Thus, such parameters have not to be computed for the open-loop
mode decision additionally The advantage of using already computed parameters in the
open-loop mode decision is complexity saving
As shown in Fig 2, the apparatus comprises the harmonics reduction filter 2. The
apparatus further comprises an optional disabling unit 4 for disabling the harmonics
reduction filter 2 based on a combination of one or more harmomcity measure(s) (e g
normalized correlation or prediction gain) and/or one or more temporal structure
measure(s) (e g. temporal flatness measure or energy change). The apparatus comprises
an optional discontinuity removal unit 6 for removing discontinuities from the filtered
version of the audio signal. In addition, the apparatus optionally comprises a unit 8 for
estimating the filter parameters of the harmonics reduction filter 2. In Fig. 2. these
components (2, 4, 6, and 8) are shown as being part of the first estimator 12 It goes
without saying that these components may be implemented external or separate from the
first estimator and may be configured to provide the filtered version of the audio signal to
the first estimator
An input audio signal 40 is provided on an input line The input audio signal 40 is applied
to the first estimator 12, the pre-processing unit 22 and both encoder stages 26. 28 In the
first estimator 12, the input audio signal 40 is applied to the filter 2 and the filtered version
of the input audio signal is used in estimating the first quality measure In case the filter is
disabled by disabling unit 4, the input audio signal 40 is used in estimating the first quality
measure, rather than the filtered version of the input audio signal. The pre-processing unit
22 processes the input audio signal in a conventional manner to derive LPC coefficients
and weighted LPC coefficients 42 and to filter the audio signal 40 with the weighted LPC
coefficients 42 to obtain the weighted audio signal 44 The pre-processing unit 22 outputs
the weighted LPC coefficients 42. the weighted audio signal 44 and a set of pitch-lags 48
As understood by those skilled in the art, the weighted LPC coefficients 42 and the

weighted audio signal 44 may be segmented into frames or sub-frames The
segmentation may be obtained by windowing the audio signal in an appropriate manner
In alternative embodiments, a preprocessor may be provided, which is configured to
generate weighted LPC coefficients and a weighted audio signal based on the filtered
version of the audio signal The weighted LPC coefficients and the weighted audio signal,
which are based on the filtered version of the audio signal are then applied to the first
estimator to estimate the first quality measure, rather than the weighted LPC coefficients
42 and the weighted audio signal 44.
In embodiments of the invention, quantized LPC coefficients or quantized weighted LPC
coefficients may be used Thus, it should be understood that the term "LPC coefficients" Is
intended to encompass 'quantized LPC coefficients" as well, and the term "weighted LPC
coefficients" is intended to encompass "weighted quantized LPC coefficients" as well In
this regard, it is worthwhile to note that the TCX algorithm of USAC uses the quantized
weighted LPC coefficients to shape the MCDT spectrum
The first estimator 12 receives the audio signal 40, the weighted LPC coefficients 42 and
the weighted audio signal 44. estimates the first quality measure 46 based thereon and
outputs the first quality measure to the controller 16 The second estimator 16 receives
the weighted audio signal 44 and the set of pitch lags 48. estimates the second quality
measure 50 based thereon and outputs the second quality measure 50 to the controller
16. As known to those skilled in the art, the weighted LPC coefficients 42, the weighted
audio signal 44 and the set of pitch lags 48 are already computed in a previous module
(i.e. the pre-processing unit 22) and, therefore, are available for no cost
The controller takes a decision to select either the TCX algorithm or the ACELP algorithm
based on a comparison of the received quality measures As indicated above, the
controller may use a hysteresis mechanism in deciding which algorithm to be used
Selection of the first encoder stage 26 or the second encoder stage 28 is schematically
shown m Fig 2 by means of switch 24 which is controlled by a control signal 52 output by
the controller 16 The control signal 52 indicates whether the first encoder stage 26 or the
second encoder stage 28 is to be used Based on the control signal 52, the required
signals schematically indicated by arrow 54 In Fig 2 and at least including the LPC
coefficients, the weighted LPC coefficients, the audio signal, the weighted audio signal,
the set of pitch lags are applied to either the first encoder stage 26 or the second encoder

stage 28 The selected encoder stage applies the associated encoding algorithm and
outputs the encoded representation 56 or 58 to the output interface 30 The output
interface 30 may be configured to output an encoded audio signal 60 which may comprise
among other data the encoded representation 56 or 58, the LPC coefficients or weighted
LPC coefficients, parameters for the selected encoding algorithm and information about
the selected encoding algorithm
Specific embodiments for estimating the first and second quality measures, wherein the
first and second quality measures are segmental SNRs in the weighted signal domain are
now described referring to Fig. 3 Fig 3 shows the first estimator 12 and the second
estimator 14 and the functionalities thereof in the fomn of flowcharts showing the
respective estimation step-by-step
Estimation of the TCX segmental SNR
The first (TCX) estimator receives the audio signal 40 (input signal), the weighted LPC
coefficients 42 and the weighted audio signal 44 as inputs The filtered version of the
audio signal 40 is generated, step 98. In the filtered version of the audio signal 40
harmonics are reduced or suppressed.
The audio signal 40 may be analysed to determine one or more harmomcity measure(s)
(e g normalized correlation or prediction gain) and/or one or more temporal structure
measure(s) (eg temporal flatness measure or energy change). Based on one of these
measures or a combination of these measures, filter 2 and, therefore, filtering 98 may be
disabled If filtering 98 is disabled, estimation of the first quality measure is performed
using the audio signal 40 rather than the filtered version thereof
In embodiments of the invention, a step of removing discontinuities (not shown in Fig 3)
may follow filtering 98 in order to remove discontinuities in the audio signal, which may
result from filtering 98
In step 100. the filtered version of the audio signal 40 is windowed. Windowing may take
place with a 10ms low-overlap sine window When the past-frame is ACELP. the block-
size may be increased by 5ms, the left-side of the window may be rectangular and the
windowed zero impulse response of the ACELP synthesis filter may be removed from the
windowed input signal. This is similar as what is done in the TCX algorithm A frame of the

filtered version of the audio signal 40. which represents a portion of the audio signal, is
output from step 100
In step 102, the windowed audio signal, i.e. the resulting frame, is transformed with a
MDCT (modified discrete cosine transform). In step 104 spectrum shaping is performed by
shaping the MDCT spectrum with the weighted LPC coefficients
In step 106 a global gain G is estimated such that the weighted spectrum quantized with
gain G would produce a given target R. when encoded with an entropy coder, e.g an
arithmetic coder The term "global gam" is used since one gain is determined for the whole
frame
An example of an implementation of the global gain estimation is now explained It is to be
noted that this global gain estimation is appropriate for embodiments in which the TCX
encoding algorithm uses a scalar quantizer with an arithmetic encoder. Such a scalar
quantizer with an arithmetic encoder is assumed in the MPEG USAC standard.
Initialization
Firstly, variables used in gam estimation are initialized by
1 Set en[i] = 9.0 + 10 0*log10(c[4*i+0] + c[4*i+1] + c[4*i+2] + c[4*i+3]),
where 0<=KL/4, C[] is the vector of coefficients to quantize, and L is the length of c[].
2 Set fac = 128, offset = fac and target = any value (eg 1000)
Iteration
Then, the following block of operations is performed NITER times (e.g here, NITER = 10)
1. fac = fac/2
2 offset = offset - fac
3 ener= 0
4 for every i where 0<=i 3 0, then ener = ener + en[i]-offset
5 if ener > target, then offset = offset + fac
The result of the iteration is the offset value After the iteration, the global gam is
estimated as G = 10^(offset/20)

The specific manner in which the global gain is estimated may vary dependent on the
quantizer and the entropy coder used. In the MPEG USAC standard a scalar quantizer
with an arithmetic encoder is assumed. Other TCX approaches may use a different
quantizer and it is understood by those skilled in the art how to estimate the global gam for
such different quantizers. For example, the AMR-WB+ standard assumes that a RE8
lattice quantizer is used For such a quantizer, estimation of the global gain could be
estimated as described in chapter 5 3 5.7 on page 34 of 3GPP TS 26 290 V6 1 0 2004-12.
wherein a fixed target bitrate is assumed
After having estimated the global gain in step 106, distortion estimation takes place in step
108 To be more specific, the quantizer distortion is approximated based on the estimated
global gain. In the present embodiment it is assumed that a uniform scalar quantizer is
used. Thus, the quantizer distortion is determined with the simple formula D=G*G/12, m
which D represents the determined quantizer distortion and G represents the estimated
global gain This corresponds to the high-rate approximation of a uniform scalar quantizer
distortion.
Based on the determined quantizer distortion, segmental SNR calculation is performed in
step 110. The SNR in each sub-frame of the frame is calculated as the ratio of the
weighted audio signal energy and the distortion D which is assumed to be constant in the
subframes For example the frame is split into four consecutive sub-frames (see Fig 4).
The segmental SNR is then the average of the SNRs of the four sub-frames and may be
indicated in dB.
This approach permits estimation of the first segmental SNR which would be obtained
when actually encoding and decoding the subject frame using the TCX algorithm,
however without having to actually encode and decode the audio signal and. therefore,
with a strongly reduced complexity and reduced computing time.
Estimation of the ACELP segmental SNR
The second estimator 14 receives the weighted audio signal 44 and the set of pitch lags
48 which is already computed in the pre-processing unit 22.

As shown in step 112, in each sub-frame, the adaptive codebook is approximated by
simply using the weighted audio signal and the pitch-lag T. The adaptive codebook is
approximated by
xw(n-T), n = 0 N
wherein xw is the weighted audio signal, T is the pitch-lag of the corresponding subframe
and N is the sub-frame length. Accordingly, the adaptive codebook is approximated by
using a version of the sub-frame shifted to the past by T Thus, in embodiments of the
invention, the adaptive codebook is approximated in a very simple manner.
In step 114, an adaptive codebook gain for each sub-frame is determined. To be more
specific, in each sub-frame, the codebook gam G is estimated such that it minimizes the
error between the weighted audio signal and the approximated adaptive-codebook This
can be done by simply comparing the differences between both signals for each sample
and finding a gam such that the sum of these differences is minimal
In step 116, the adaptive codebook distortion for each sub-frame is determined. In each
sub-frame, the distortion D introduced by the adaptive codebook is simply the energy of
the error between the weighted audio signal and the approximated adaptive-codebook
scaled by the gam G.
The distortions determined in step 116 may be adjusted in an optional step 118 m order to
take the innovative codebook into consideration The distortion of the innovative codebook
used m ACELP algorithms may be simply estimated as a constant value. In the described
embodiment of the invention, it is simply assumed that the innovative codebook reduces
the distortion D by a constant factor. Thus, the distortions obtained in step 116 for each
sub-frame may be multiplied in step 118 by a constant factor, such as a constant factor in
the order of 0 to 1, such as 0 055
In step 120 calculation of the segmental SNR takes place. In each sub-frame, the SNR is
calculated as the ratio of the weighted audio signal energy and the distortion D. The
segmental SNR is then the mean of the SNR of the four sub-frames and may be indicated
in dB.

This approach permits estimation of the second SNR which would be obtained when
actually encoding and decoding the subject frame using the ACELP algorithm, however
without having to actually encode and decode the audio signal and, therefore, with a
strongly reduced complexity and reduced computing time.
The first and second estimators 12 and 14 output the estimated segmental SNRs 46, 50
to the controller 16 and the controller 16 takes a decision which algorithm is to be used for
the associated portion of the audio signal based on the estimated segmental SNRs 46,
50. The controller may optionally use a hysteresis mechanism in order to make the
decision more stable For example, the same hysteresis mechanism as m the closed-loop
decision may be used with slightly different tuning parameters. Such a hysteresis
mechanism may compute a value "dsnr" which can depend on the estimated segmental
SNRs (such as the difference therebetween) and other parameters, such as statistics
about previous decisions, the number of temporally stationary frames, and transients in
the frames
Without a hysteresis mechanism, the controller may select the encoding algorithm having
the higher estimated SNR, i.e ACELP is selected if the second estimated SNR is higher
less than the first estimated SNR and TCX is selected if the first estimated SNR is higher
than the second estimated SNR. With a hysteresis mechanism, the controller may select
the encoding algorithm according to the following decision rule, wherein acelp_snr is the
second estimated SNR and tcx_snr is the first estimated SNR:
if acelp_snr + dsnr > tcx_snr then select ACELP, otherwise select TCX
Determination of the parameters of the filter for reducing the amplitude of the
harmonics
An embodiment for determining the parameters of the filter for reducing the amplitude of
the harmonics is now described. The filter parameters may be estimated at the encoder-
side, such as in unit 8.
Pitch estimation
One pitch lag (integer part + fractional part) per frame is estimated (frame size e.g. 20ms).
This is done in three steps to reduce complexity and to improve estimation accuracy.

a) First Estimation of the integer part of the pitch lag
A pitch analysis algorithm that produces a smooth pitch evolution contour is used
(eg Open-loop pitch analysis described in Rec ITU-T G.718, sec 6 6) This
analysis is generally done on a subframe basis (subframe size e.g. 10ms). and
produces one pitch lag estimate per subframe. Note that these pitch lag estimates
do not have any fractional part and are generally estimated on a downsampled
signal (sampling rate e.g 6400Hz) The signal used can be any audio signal, eg a
LPC weighted audio signal as described in Rec ITU-T G.718. sec. 6.5
b) Refinement of the integer part T„,t of the pitch lag
The final integer part of the pitch lag is estimated on an audio signal x[n] running at
the core encoder sampling rate, which is generally higher than the sampling rate of
the downsampled signal used in a) (eg. 12 8kHz, 16kHz, 32kHz. ). The signal
x[n] can be any audio signal e g. a LPC weighted audio signal.
The integer part T,r, of the pitch lag is then the lag that maximizes the
autocorrelation function

with d around a pitch lag T estimated m a).

c) Estimation of the fractional part T„ of the pitch lag
The fractional part T„ is found by interpolating the autocorrelation function C(d)
computed in step b) and selecting the fractional pitch lag which maximizes the
interpolated autocorrelation function The interpolation can be performed using a
low-pass FIR filter as described in eg Rec. ITU-T G.718, sec. 6.6 7
Gain estimation and quantization

The gam is generally estimated on the input audio signal at the core encoder sampling
rate, but it can also be any audio signal like the LPC weighted audio signal. This signal is
noted y[n] and can be the same or different than x[n].
The prediction yP[n] of y[n] is first found by filtering y[n] with the following filter

with Tmt the integer part of the pitch lag (estimated in b)) and B(z.Tfr) a low-pass FIR
filter whose coefficients depend on the fractional part of the pitch lag Tfr (estimated in c))
One example of B(z) when the pitch lag resolution is %•

and limited between 0 and 1
Finally, the gain g is quantized e.g. on 2 bits, using e g. uniform quantization. • •
P is used to control the strength of the filter p equal to 1 produces full effects /? equal to 0
disables the filter Thus, in embodiments of the invention, the filter may be disabled by
setting ft to a value of 0 In embodiments of the invention, if the filter is enabled, U may be
set to a value between 0,5 and 0.75 In embodiments of the invention, if the filter is
enabled, li may be set to a value of 0,625 An example of B{z,Tfr) is given above The
order and the coefficients of B{z,Tfr) can also depend on the bitrate and the output
sampling rate. A different frequency response can be designed and tuned for each
combination of bitrate and output sampling rate

Disabling the filter
The filter may be disabled based on a combination of one or more harmonicity measure(s)
and/or one or more temporal structure measure(s). Examples of such a'measures are
described below
i) Harmonicity measure like the normalized correlation at the integer pitch-lag
estimated in step b)

The normalized correlation is 1 if the input signal is perfectly predictable by
the integer pitch-lag, and 0 if it is not predictable at all A high value (close
to 1) would then indicate a harmonic signal. For a more robust decision, the
normalized correlation of the past frame can also be used in the decision,
e.g •
If (norm corr(curr.)*norm.corr.(prev )) > 0.25, then the filter is not
disabled
II) Temporal structure measures computed, for example, on the basis of energy
sampules also used by a transient detector for transient detection (e.g.
temporal flatness measure, energy change), e g
if (temporal flatness measure > 3.5 or energy change > 3.5) then the filter is
disabled.
More details concerning determination of one or more harmonicity measures are set forth
below.
The measure of harmonicity is, for example, computed by a normalized correlation of the
audio signal or a pre-modified version thereof at or around the pitch-lag The pitch-lag
could even be determined in stages comprising a first stage and a second stage, wherein,
within the first stage, a preliminary estimation of the pitch-lag is determined at a down-
sampled domain of a first sample rate and, within the second stage, the preliminary
estimation of the pitch-lag is refined at a second sample rate, higher than the first sample
rate. The pitch-lag is, for example, determined using autocorrelation. The at least one

temporal structure measure is, for example, determined within a temporal region
temporally placed depending on the pitch information. A temporally past-heading end of
the temporal region is, for example, placed depending on the pitch information. The
temporal past-heading end of the temporal region may be placed such that the temporally
past-heading end of the temporal region is displaced into past direction by a temporal
amount monotomcally increasing with an increase of the pitch information The temporally
future-heading end of the temporal region may be positioned depending on the temporal
structure of the audio signal within a temporal candidate region extending from the
temporally past-heading end of the temporal region or, of the region of higher influence
onto the determination of the temporal structure measure, to a temporally future-heading
end of a current frame The amplitude or ratio between maximum and minimum energy
samples within the temporal candidate region may be used to this end. For example, the
at least one temporal structure measure may measure an average or maximum energy
variation of the audio signal within the temporal region and a condition of disablement
may be met if both the at least one temporal structure measure is smaller than a
predetermined first threshold and the measure of harmonicity is, for a current frame and/or
a previous frame, above a second threshold The condition is also by met if the measure
of harmonicity is, for a current frame, above a third threshold and the measure of
harmonicity is. for a current frame and/or a previous frame, above a fourth threshold which
decreases with an increase of the pitch lag
A step-by-step description of a concrete embodiment for determining the measures is
presented now
Step 1 Transient detection and temporal measures
The input signal s„r(n) is input to the time-domain transient detector The input signal
*,„.(*) is high-pass filtered The transfer function of the transient detection's HP filter is
given by

The signal, filtered by the transient detection's HP filter, is denoted as sw(n). The HP-
filtered signal sTD{n) is segmented into 8 consecutive segments of the same length The
energy of the HP-filtered signal sTD(n) for each segment is calculated as


where is the number of samples in 2 5 milliseconds segment at the input
sampling frequency
An accumulated energy is calculated using:

An attack is detected if the energy of a segment ETD{i) exceeds the accumulated energy
by a constant factor and the attacklndex is set to/

If no attack is detected based on the criteria above, but a strong energy increase is
detected in segment /. the attacklndex is set toi without indicating the presence of an
attack. The attacklndex is basically set to the position of the last attack in a frame with
some additional restrictions.
The energy change for each segment is calculated as:

The temporal flatness measure is calculated as

The maximum energy change is calculated as


If index of Ech„g{i) or E1D{>) is negative then it indicates a value from the previous
segment, with segment indexing relative to the current frame.
Npa:t, is the number of the segments from the past frames. It is equal to 0 if the temporal
flatness measure is calculated for the usage in ACELP/TCX decision if the temporal
flatness measure is calculate for the TCX LTP decision then it is equal to.

N,„,wis the number of segments from the current frame. It is equal to 8 for non-transient
frames For transient frames first the locations of the segments with the maximum and the
minimum energy are found:

Step 2 Transform block length switching
The overlap length and the transform block length of the TCX are dependent on the
existence of a transient and its location.
Table 1 Coding of the overlap and the transform length based on the transient
position


The transient detector described above basically returns the index of the last attack with
the restriction that if there are multiple transients then MINIMAL overlap is preferred over
HALF overlap which is preferred over FULL overlap. If an attack at position 2 or 6 is not
strong enough then HALF overlap is chosen instead of the MINIMAL overlap.
Step 3. Pitch estimation
One pitch lag (integer part + fractional part) per frame is estimated (frame size eg. 20ms)
as set forth above in 3 steps a) to c) to reduce complexity and improves estimation
accuracy.
Step 4. Decision bit
If the input audio signal does not contain any harmonic content or if a prediction based
technique would introduce distortions in time structure (e.g repetition of a short transient),
then a decision that the filter is disabled is taken.

The decision is made based on several parameters such as the normalized correlation at
the integer pitch-lag and the temporal structure measures
The normalized correlation at the integer pitch-lag norm_corr is estimated as set forth
above. The normalized correlation is 1 if the input signal is perfectly predictable by the
integer pitch-lag, and 0 if it is not predictable at an A high value (dose to 1) would then
indicate a harmonic signal For a more robust decision, beside the normalized correlation
for the current frame (norm_corr(curr)) the normalized correlation of the past frame
(norm_corr(prev)) can also be used in the deosion . e.g.'
If (norm_corr(curr)*norm_corr(prev)) > 0.25
or
If max(norm_corr(curr) norm_corr(prev)) > 0.5.
then the current frame contains some harmomc content.
The temporal structure measures may be computed by a transient detector (e g temporal
flatness measure (equation (6)) and maximal energy change equation (7)), to avoid
activating the filter on a signal containing a strong transient or big temporal changes The
temporal features are calculated on the signal containing the current frame (Nnru.
segments) and the past frame up to the pitch lag (npas, segments) For step like
transients that are slowly decaying, all or some of the features are calculated only up to
the location of the transient (iwl^ - 3) because the distortions in the non-harmonic part of
the spectrum introduced by the LTP filtering would be suppressed by the masking of the
strong long lasting transient (e.g crash cymbal)
Pulse trains for low pitched signals can be detected as a transient by a transient detector.
For the signals with low pitch the features from the transient detector are thus ignored and
there is instead additional threshold for the normalized correlation that depends on the
pitch lag, e g :
if norm_corr <- 1 2-Tm[/L . then disable the filter.

One example decision is shown below where b1 is some bitrate. for example 48 kbps,
where TCX_20 Indicates that the frame is coded using single long block, where TCX_10
indicates that the frame is coded using 2,3,4 or more short blocks, where
TCX_207TCX_10 decision is based on the output of the transient detector described
above. tempFlatness is the Temporal Flatness Measure as defined In (6) (&).
maxEnergyChange is the Maximum Energy Change as defined in (7)fZ4. The condition
norm_corr(curr) > 1.2-Tint/L could also be written as (1 2-norm_corr(curr))*L < Tmt.

It is obvious from the examples above that the detection of a transient affects which
decision mechanism for the long term prediction will be used and what part of the signal
will be used for the measurements used in the decision, and not that it directly triggers
disabling of the long term prediction filter
The temporal measures used for the transform length decision may be completely
different from the temporal measures used for the LTP filter decision or they may overlap
or be exactly the same but calculated in different regions. For low pitched signals the
detection of transients may be ignored completely if the threshold for the normalized
correlation that depends on the pitch lag is reached
Technique for removing possible discontinuities
A possible technique for removing discontinuities caused by applying a linear filter H(z)
frame by frame is now described The linear filter may be the LTP filter described The
linear filter may be a FIR (finite impulse response) filter or an IIR (infinite impulse
response) filter. The proposed approach does not filter a portion of the current frame with
the filter parameters of the past frame, and thus avoids possible problems of known

approaches The proposed approach uses a LPC filter to remove the discontinuity This
LPC filter is estimated on the audio signal (filtered by a linear time-invariant filter H(z) or
not) and is thus a good model of the spectral shape of the audio signal (filtered by H(z) or
not). The LPC filter is then used such that the spectral shape of the audio signal masks
the discontinuity.
The LPC filter can be estimated in different ways It can be estimated eg. using the audio
signal (current and/or past frame) and the Levinson-Durbin algorithm. It can also be
computed on the past filtered frame signal, using the Levinson-Durbin algorithm.
If H(z) is used in an audio codec and the audio codec already uses a LPC filter (quantized
or not) to eg shape the quantization noise in a transform-based audio codec, then this
LPC filter can be directly used for smoothing the discontinuity, without the additional
complexity needed to estimate a new LPC filter
Below is described the processing of the current frame for the FIR case filter case and the
MR filter case. The past frame is assumed to be already processed.
FIR filter case
1 Filter the current frame with the filter parameters of the current frame, producing a
filtered current frame.
2 Considering a LPC filter (quantized or not) with order M, estimated on the audio
signal (filtered or not).
3. The M last samples of the past frame are filtered with the filter H(z) and the
coefficients of the current frame, producing a first portion of filtered signal
4 The M last samples of the filtered past frame are then subtracted from the first
portion of filtered signal, producing a second portion of filtered signal
5 A Zero Impulse Response (ZIR) of the LPC filter is then generated by filtering a
frame of zero samples with the LPC filter and initial states equal to the second
portion of filtered signal
6 The ZIR can be optionally windowed such that its amplitude goes faster to 0.
7 A beginning portion of the ZIR is subtracted from a corresponding beginning
portion of the filtered current frame.
IIR filter case:

1 Considering a LPC filter (quantized or not) with order M, estimated on the audio
signal (filtered or not)
2. The M last samples of the past frame are filtered with the filter H(z) and the
coefficients of the current frame, producing a first portion of filtered signal.
3 The M last samples of the filtered past frame are then subtracted from the first
portion of filtered signal, producing a second portion of filtered signal
4. A Zero Impulse Response (ZIR) of the LPC filter is then generated by filtering a
frame of zero samples with the LPC filter and initial states equal to the second
portion of filtered signal
5. The ZIR can be optionally windowed such that its amplitude goes faster to 0
6. A beginning portion of the current frame is then processed sample-by-sample
starting with the first sample of the current frame.

7 The sample is filtered with the filter H(z) and the current frame parameters,
producing a first filtered sample.
8 The corresponding sample of the ZIR is then subtracted from the first filtered
sample, producing the corresponding sample of the filtered current frame.
9 Move to the next sample.
10 Repeat 9 to 12 until the last sample of the beginning portion of the current frame is
processed
11. Filter the remaining samples of the current frame with the filter parameters of the
current frame.
Accordingly, embodiments of the invention permit for estimating segmental SNRs and
selection of an appropriate encoding algorithm in a simple and accurate manner In
particular, embodiments of the invention permit for an open-loop selection of an
appropriate coding algorithm, wherein inappropriate selection of a coding algorithm in
case of an audio signal having harmonics is avoided.
In the above embodiments, the segmental SNRs are estimated by calculating an average
of SNRs estimated for respective sub-frames. In alternative embodiments, the SNR of a
whole frame could be estimated without dividing the frame into sub-frames
Embodiments of the invention permit for a strong reduction in computing time when
compared to a closed-loop selection since a number of steps required in the closed-loop
selection are omitted.

Accordingly, a large number of steps and the computing time associated therewith can be
saved by the inventive approach while still permitting selection of an appropriate encoding
algorithm with good performance
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 represent a description of a corresponding
block or item or feature of a corresponding apparatus
Embodiments of the apparatuses described herein and the features thereof may be
implemented by a computer, one or more processors, one or more micro-processors,
field-programmable gate arrays (FPGAs). application specific integrated circuits (ASICs)
and the like or combinations thereof, which are configured or programmed in order to
provide the described functionalities
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 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 earner
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 herem. 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 non-
transitionary.
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 programmed 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

W
e Claim:
1 Apparatus (10) for selecting one of a first encoding algorithm having a first
characteristic and a second encoding algorithm having a second characteristic for
encoding a portion of an audio signal (40) to obtain an encoded version of the
portion of the audio signal (40), comprising:
a long-term prediction filter configured to receive the audio signal to reduce the
amplitude of harmonics in the audio signal and to output a filtered version of the
audio signal;
a first estimator (12) for using the filtered version of the audio signal in estimating a
SNR (signal to noise ratio) or a segmental SNR of the portion of the audio signal
as a first quality measure for the portion of the audio signal, the first quality
measure being associated with the first encoding algorithm, wherein estimating
said first quality measure comprises performing an approximation of the first
encoding algorithm to obtain a distortion estimate of the first encoding algorithm
and to estimate the first quality measure based on the portion of the audio signal
and the distortion estimate of the first encoding algorithm without actually encoding
and decoding the portion of the audio signal using the first encoding algorithm;
a second estimator (14) for estimating a SNR or a segmental SNR as a second
quality measure for the portion of the audio signal, the second quality measure
being associated with the second encoding algorithm, wherein estimating said
second quality measure comprises performing an approximation of the second
encoding algorithm to obtain a distortion estimate of the second encoding
algorithm and to estimate the second quality measure using the portion of the
audio signal and the distortion estimate of the second encoding algorithm without
actually encoding and decoding the portion of the audio signal using the second
encoding algorithm; and
a controller (16) for selecting the first encoding algorithm or the second encoding
algorithm based on a comparison between the first quality measure and the
second quality measure,

wherein the first encoding algorithm is a transform coding algorithm, a MDCT
(modified discrete cosine transform) based coding algorithm or a TCX (transform
coding excitation) coding algorithm and wherein the second encoding algorithm is
a CELP (code excited linear prediction) coding algorithm or an ACELP (algebraic
code excited linear prediction) coding algorithm.
2 Apparatus (10) of claim 1, wherem a transfer function of the long-term prediction
filter comprises an integer part of a pitch lag and a multi tap filter depending on a
fractional part of the pitch lag.
3 Apparatus (10) of claim 1, wherein the long-term prediction filter has the transfer
function

with T,nt and T„ are the integer and fractional part of a pitch-lag, g is a gain. (3 is a
weight and B(z,Tlr) is a FIR low-pass filter whose coefficients depend on the
fractional part of the pitch
4. Apparatus of one of claims 1 to 3, further comprising a disabling unit for disabling
the filter based on a combination of one or more harmonicity measures and/or one
or more temporal structure measures.
5 Apparatus of claim 4, wherein the one or more harmonicity measures comprise at
least one of a normalized correlation or a prediction gain and wherein the one or
more temporal structure measures comprise at least one of a temporal flatness
measure and an energy change
6 Apparatus of one of claims 1 to 5, wherein the filter is applied to the audio signal
on a frame-by-frame basis, said apparatus further comprising a unit for removing
discontinuities in the audio signal caused by the filter
7. Apparatus (10) of one of claims 1 to 6, wherein the first and second estimators are
configured to estimate a SNR or segmental SNR of a portion of a weighted version
of the audio signal

8 Apparatus (10) of one of claims 1 to 7, wherein the first estimator (12) is configured
to determine an estimated quantizer distortion which a quantizer used in the first
encoding algorithm would introduce when quantizing the portion of the audio signal
and to estimate the first quality measure based on an energy of a portion of a
weighted version of the audio signal and the estimated quantizer distortion,
wherein the first estimator (12) is configured to estimate a global gain for the
portion of the audio signal such that the portion of the audio signal would produce
a given target bitrate when encoded with a quantizer and an entropy coder used in
the first encoding algorithm, wherein the first estimator (12) is further configured to
determine the estimated quantizer distortion based on the estimated global gam
9 Apparatus (10) of one of claims 1 to 8, wherein the second estimator (14) is
configured to determine an estimated adaptive codebook distortion which an
adaptive codebook used in the second encoding algorithm would introduce when
using the adaptive codebook to encode the portion of the audio signal, and
wherein the second estimator (14) is configured to estimate the second quality
measure based on an energy of a portion of a weighted version of the audio signal
and the estimated adaptive codebook distortion, wherein, for each of a plurality of
sub-portions of the portion of the audio signal, the second estimator (14) is
configured to approximate the adaptive codebook based on a version of the sub-
portion of the weighted audio signal shifted to the past by a pitch-lag determined in
a pre-processing stage, to estimate an adaptive codebook gain such that an error
between the sub-portion of the portion of the weighted audio signal and the
approximated adaptive codebook is minimized, and to determine the estimated
adaptive codebook distortion based on the energy of an error between the sub-
portion of the portion of the weighted audio signal and the approximated adaptive
codebook scaled by the adaptive codebook gain
10 Apparatus (10) of claim 9, wherein the second estimator (14) is further configured
to reduce the estimated adaptive codebook distortion determined for each sub-
portion of the portion of the audio signal by a constant factor
11. Apparatus (10) of one of claims 1 to 8, wherein the second estimator (14) is
configured to determine an estimated adaptive codebook distortion which an
adaptive codebook used in the second encoding algorithm would introduce when
using the adaptive codebook to encode the portion of the audio signal, and

wherein the second estimator (14) is configured to estimate the second quality
measure based on an energy of a portion of a weighted version of the audio signal
and the estimated adaptive codebook distortion, wherein the second estimator (14)
is configured to approximate the adaptive codebook based on a version of the
portion of the weighted audio signal shifted to the past by a pitch-lag determined in
a pre-processing stage, to estimate an adaptive codebook gain such that an error
between the portion of the weighted audio signal and the approximated adaptive
codebook is minimized, and to determine the estimated adaptive codebook
distortion based on the energy of an error between the portion of the weighted
audio signal and the approximated adaptive codebook scaled by the adaptive
codebook gain
12. Apparatus (20) for encoding a portion of an audio signal, comprising the apparatus
(10) according to one of claims 1 to 11, a first encoder stage (26) for performing
the first encoding algorithm and a second encoder stage (28) for performing the
second encoding algorithm, wherein the apparatus for encoding (20) is configured
to encode the portion of the audio signal using the first encoding algorithm or the
second encoding algorithm depending on the selection by the controller (16).
13. System for encoding and decoding comprising an apparatus (20) for encoding
according to claim 12 and a decoder configured to receive the encoded version of
the portion of the audio signal and an indication of the algorithm used to encode
the portion of the audio signal and to decode the encoded version of the portion of
the audio signal using the indicated algorithm
14 Method for selecting one of a first encoding algorithm having a first characteristic
and a second encoding algorithm having a second characteristic for encoding a
portion of an audio signal to obtain an encoded version of the portion of the audio
signal, comprising
filtering the audio signal using a long-term prediction filter to reduce the amplitude
of harmonics in the audio signal and to output a filtered version of the audio signal;
using the filtered version of the audio signal in estimating a SNR or a segmented
SNR of the portion of the audio signal as a first quality measure for the portion of
the audio signal, the first quality measure being associated with the first encoding

algorithm, wherein estimating said first quality measure comprises performing an
approximation of the first encoding algorithm to obtain a distortion estimate of the
first encoding algorithm and to estimate the first quality measure based on the
portion of the first audio signal and the distortion estimate of the first encoding
algorithm without actually encoding and decoding the portion of the audio signal
using the first encoding algorithm;
estimating a SNR or a segmented SNR as a second quality measure for the
portion of the audio signal, the second quality measure being associated with the
second encoding algorithm, wherein estimating said second quality measure
comprises performing an approximation of the second encoding algorithm to obtain
a distortion estimate of the second encoding algorithm and to estimate the second
quality measure using the portion of the audio signal and the distortion estimate of
the second encoding algorithm without actually encoding and decoding the portion
of the audio signal using the second coding algorithm, and
selecting the first encoding algorithm or the second encoding algorithm based on a
comparison between the first quality measure and the second quality measure,
wherein the first encoding algorithm is a transform coding algorithm, a MDCT
(modified discrete cosine transform) based coding algorithm or a TCX (transform
coding excitation) coding algorithm and wherein the second encoding algorithm is
a CELP (code excited linear prediction) coding algorithm or an ACELP (algebraic
code excited linear prediction) coding algorithm
15 Computer program having a program code for performing, when running on a
computer, the method of one of claims 14