Patent Description:
A state-of-the-art conversational codec can represent with a very good quality a clean speech signal with a bit rate of around <NUM> kbps and approach transparency at a bit rate of <NUM> kbps. To sustain this high speech quality even at low bit rate a multi modal coding scheme may be used. Usually the input sound signal is split among different categories reflecting its characteristics. For example, the different categories may include voiced, unvoiced and onset. The codec uses different coding modes optimized for all these categories.

However, some deployed speech codecs do not use this multi modal approach resulting in a suboptimal quality especially at low bit rates for a sound signal different from clean speech. When a codec is deployed, it is hard to modify the encoder due to the fact that the bitstream is standardized and any modification to the bitstream would break the interoperability of the codec. However modifications to the decoder can be implemented to improve the quality perceived on the receiver side.

United States Patent No. <CIT> discloses a speech codec employing noise classification for noise compensation. A multi-rate speech codec supports a plurality of encoding bit rate modes by adaptively selecting encoding bit rate modes to match communication channel restrictions. In higher bit rate encoding modes, an accurate representation of speech through CELP (code excited linear prediction) and other associated modeling parameters are generated for higher quality decoding and reproduction. For each bit rate mode selected, pluralities of fixed or innovation subcodebooks are selected for use in generating innovation vectors. The speech coder distinguishes various voice signals as a function of their voice content. For example, a Voice Activity Detection (VAD) algorithm selects an appropriate coding scheme depending on whether the speech signal comprises active or inactive speech. The encoder may consider varying characteristics of the speech signal including sharpness, a delay correlation, a zero-crossing rate, and a residual energy. In another embodiment of the present invention, code excited linear prediction is used for voice active signals whereas random excitation is used for voice inactive signals; the energy level and spectral content of the voice inactive signal may also be used for noise coding. The multi-rate speech codec may employ distributed detection and compensation processing the speech signal. For high quality perceptual speech reproduction, the speech codec may perform noise detection in both an encoder and a decoder. The noise detection may be coordinated between the encoder and decoder. Similarly, noise compensation may be performed in a distributed manner among both the decoder and the encoder.

According to a first aspect, the present invention relates to device for modifying a synthesis of a time-domain excitation decoded by a time-domain decoder according to claim <NUM>.

According to another aspect, the present invention relates to device for decoding a sound signal encoded by encoding parameters, comprising: a decoder of a time-domain excitation in response to the sound signal encoding parameters; a synthesis filter responsive to the decoded time-domain excitation to produce a synthesis of said time-domain excitation; and the above described device for modifying the synthesis of the time-domain excitation.

According to a third aspect, the present invention relates to a method for modifying a synthesis of a time-domain excitation decoded by a time-domain decoder according to claim <NUM>.

According to a further aspect, the present invention is concerned with a method for decoding a sound signal encoded by encoding parameters, comprising: decoding a time-domain excitation in response to the sound signal encoding parameters; synthesizing the decoded time-domain excitation to produce a synthesis of said time-domain excitation; and the above described method for modifying the synthesis of the time-domain excitation.

The foregoing and other features of the device and method for modifying the synthesis of a time-domain excitation will become more apparent upon reading of the following non restrictive description, given by way of non limitative example with reference to the accompanying drawings.

The present disclosure relates to an approach to implement on the decoder side a multimodal decoding such that interoperability is maintained and the perceived quality is increased. In the disclosure, although AMR-WB as described in reference [3GPP TS <NUM>, "Adaptive Multi-Rate - Wideband (AMR-WB) speech codec; Transcoding functions] is used as illustrative example, it should be kept in mind that this approach can be applied to other types of low bit rate speech decoders as well.

Referring to <FIG>, to achieve this multimodal decoding, a time-domain excitation decoder <NUM> first decodes entirely the received bitstream <NUM>, for example the AMR-WB bitstream, to get a complete time-domain Code-Excited Linear Prediction (CELP) decoded excitation. The decoded time-domain excitation is processed through a Linear Prediction (LP) synthesis filter <NUM> to obtain a speech/sound signal time-domain synthesis at the inner sampling frequency of the decoder. For AMR-WB, this inner sampling frequency is <NUM>, but for another codec it could be different.

The time-domain synthesis of the current frame from the LP synthesis filter <NUM> is processed through a classifier <NUM>-<NUM>-<NUM>-<NUM> (<FIG>, <FIG> and <FIG>) supplied with voice activity detection (VAD) information <NUM> from the bitstream <NUM>. The classifier <NUM>-<NUM>-<NUM>-<NUM> analyses and categorizes the time-domain synthesis either as inactive speech, active voiced speech, active unvoiced speech, or generic audio. Inactive speech (detected at <NUM>) includes all background noises between speech burst, active voiced speech (detected at <NUM>) represents a frame during an active speech burst having voiced characteristics, active unvoiced speech (detected at <NUM>) represents a frame during a speech burst having unvoiced characteristics, and generic audio (detected at <NUM>) represents music or reverberant speech. Other categories can be added or derived from the above categories. The disclosed approach aims at improving in particular, but not exclusively, the perceived quality of the inactive speech, the active unvoiced speech and the generic audio.

Once the category of the time-domain synthesis is determined, a converter/modifier <NUM> converts the decoded excitation from the time-domain excitation decoder <NUM> into frequency domain using a non-overlap frequency transform. An overlap transform can be used as well, but it implies an increase of the end-to-end delay which is not desirable in most cases. The frequency representation of the excitation is then split into different frequency bands in the converter/modifier <NUM>. The frequency bands can have fixed size, can rely on critical bands [<NPL>], or any other combinations. Then the energy per band is computed and kept in memory in the converter/modifier <NUM> for use after the reshaping process to ensure the modification does not alter the global frame energy level.

The modification of the excitation in the frequency domain as performed by the converter/modifier <NUM> may differ with the classification of the synthesis. For inactive speech and active unvoiced speech, the reshaping may consist of a normalization of the low frequencies with an addition of noise and replacement of the high frequency content with noise only. A cut-off frequency of the decoded time-domain synthesis, the limit between low and high frequency, can be fixed at a value around <NUM> to <NUM>. Some of the low frequency content of the decoded time-domain synthesis is kept to prevent artifact when switching between a non-modified frame and a modified frame. It is also possible to make the cut-off frequency variable from frame to frame by choosing a frequency bin as a function of the decoded pitch from the time-domain excitation decoder <NUM>. The modification process has as effect of removing the kind of electrical noise associated with the low bit rate speech codec. After the modification process, a gain matching per frequency band is applied to get back the initial energy level per frequency band with a slight increase of the energy for the frequencies above <NUM> to compensate for an LP filter gain drop at those frequencies.

For a frame categorized as generic audio, the processing in the converter/modifier <NUM> is different. First the normalization is performed per frequency band for all the bands. In the normalization operation, all the bins inside a frequency band that are below a fraction of the maximum frequency value within the band are set to zero. For higher frequency bands, more bins are zeroed per band. This simulates a frequency quantification scheme with a high bit budget, but having more bits allocated to the lower frequencies. After the normalization process, a noise fill can be applied to replace the zeroed bins with random noise but, depending on the bit rate, the noise fill is not always used. After the modification process, a gain matching per frequency band is applied to get back the initial energy level per frequency band and a tilt correction depending on the bit rate is applied along the frequency band to compensate for the systematic under estimation of the LP filter in case of generic audio input. Another differentiation for the generic audio path comes from the fact that the gain matching is not applied over all frequency bins. Because the spectrum of generic audio is usually more peaky than speech, the perceived quality is improved when it is possible to identify spectral pulses and to put some emphasis thereon. To do so, full gain matching with tilt correction is applied only to the highest energy bins inside a frequency band. For the lowest energy bins, only a fraction of the gain matching is applied to those bins. This results in increasing the spectral dynamic.

After the excitation frequency reshaping and gain matching, the converter/modifier <NUM> applies an inverse frequency transform to obtain the modified time-domain excitation. This modified excitation is processed through the LP synthesis filter <NUM> to obtain a modified time-domain synthesis. An overwriter <NUM> simply overwrites the time-domain decoded synthesis from LP synthesis filter <NUM> with the modified time-domain synthesis from the LP synthesis filter <NUM> depending on the classification of the time-domain decoded synthesis before final de-emphasis and resampling to <NUM> (for the example of AMR-WB) in a deemphasizing filter and resampler <NUM>.

In case of inactive speech, the only difference compared to active unvoiced speech modification is the use of a smoother <NUM> for smoothing the LP synthesis filter <NUM> to give smoother noise variation. The remaining modifications are the same as for the active unvoiced path. In the following text a more detailed example of implementation of the disclosed approach is described with reference to <FIG>.

Referring to <FIG>, the classifier <NUM>-<NUM>-<NUM>-<NUM> performs at the decoder a classification of the time-domain synthesis <NUM> of the speech/sound signal as described hereinabove for the bit rates where the modification is applied. For the purpose of simplification of the drawings, the LP synthesis filter <NUM> is not shown in <FIG>. Classification at the decoder is similar to that as described in references [<CIT>, "A method and device for efficient frame erasure concealment in linear predictive based speech codecs"] and [<CIT>, "Method and device for efficient frame erasure concealment in speech codecs"], plus some adaption for the generic audio detection. The following parameters are used for the classification of the frames at the decoder: a normalized correlation rx, a spectral tilt measure et, a pitch stability counter pc, a relative frame energy of the sound signal at the end of the current frame Es, and a zero-crossing counter zc. The computation of these parameters which are used to classify the signal is explained below.

The normalized correlation rx is computed at the end of the frame based on the speech/sound signal time-domain synthesis sout(n). The pitch lag of the last sub-frame from the time-domain excitation decoder <NUM> is used. More specifically, the normalized correlation rx is computed pitch synchronously as follows: <MAT> where x(n)= sout(n), T is the pitch lag of the last sub-frame, t=L-T, and L is the frame size. If the pitch lag of the last sub-frame is larger than 3N12 (N being the sub-frame size), T is set to the average pitch lag of the last two sub-frames.

Therefore, the normalized correlation rx is computed using the speech/sound signal time-domain synthesis sout(n). For pitch lags lower than the sub-frame size (<NUM> samples) the normalized correlation is computed twice at instants t=L-T and t=L-2T, and the normalized correlation rx is given as the average of these two computations.

The spectral tilt parameter et contains the information about the frequency distribution of energy. As a non limitative example, the spectral tilt at the decoder is estimated as the first normalized autocorrelation coefficient of the time-domain synthesis. It is computed based on the last <NUM> sub-frames as: <MAT> where x(n) = sout(n) is the time-domain synthesis signal, N is the sub-frame size, and L is the frame size (N=<NUM> and L=<NUM> in the example of AMR-WB).

The pitch stability counter pc assesses the variation of the pitch period. It is computed at the decoder as follows: <MAT>.

The values p<NUM>, p<NUM>, p<NUM> and p<NUM> correspond to the closed-loop pitch lag from the <NUM> sub-frames of the current frame (in the example of AMR-WB).

The relative frame energy Es is computed as a difference between the current frame energy Ef in dB and its long-term average Elt <MAT> where the current frame energy Ef is the energy of the time-domain synthesis sout(n) in dB computed pitch synchronously at the end of the frame as <MAT> where L=<NUM> (in the example of AMR-WB) is the frame length and T is the average pitch lag of the last two sub-frames. If T is less than the sub-frame size then T is set to <NUM>T (the energy computed using two pitch periods for short pitch lags).

The long-term averaged energy is updated on active speech frames using the following relation: <MAT>.

The last parameter is the zero-crossing counter zc computed on one frame of the time-domain synthesis sout(n). As a non limitative example, the zero-crossing counter zc counts the number of times the sign of the time-domain synthesis changes from positive to negative during that interval.

To make the classification more robust, the classification parameters are considered together forming a function of merit fm. For that purpose, the classification parameters are first scaled using a linear function. Let us consider a parameter px, its scaled version is obtained using: <MAT>.

The scaled pitch stability counter pc is clipped between <NUM> and <NUM>. The function coefficients kp and cp have been found experimentally for each of the parameters. The values used in this example of implementation are summarized in Table <NUM>:.

The function of merit is defined as: <MAT> where the superscript s indicates the scaled version of the parameters.

The classification of the frames is then done using the function of merit fm and following the rules summarized in Table <NUM>:.

In addition to this classification, the information <NUM> on the voice activity detection (VAD) by the encoder can be transmitted into the bitstream <NUM> (<FIG>) as it is the case with the example of AMR-WB. Thus, one bit is sent into the bitstream <NUM> to specify whether or not the encoder considers the current frame as active content (VAD = <NUM>) or inactive content (background noise, VAD = <NUM>). When the VAD information indicates that the content is inactive, the classifier portion <NUM>, <NUM>, <NUM> and <NUM> then overwrites the classification as UNVOICED.

The classification scheme also includes a generic audio detection (see classifier portion <NUM> of <FIG>). The generic audio category includes music, reverberant speech and can also include background music. A second step of classification allows the classifier <NUM>-<NUM>-<NUM>-<NUM> to determine with good confidence that the current frame can be categorized as generic audio. Two parameters are used to realize this second classification step. One of the parameters is the total frame energy Ef as formulated in Equation (<NUM>).

First, a mean of the past forty (<NUM>) total frame energy variations E df is calculated using the following relation: <MAT>.

Then, a statistical deviation of the energy variation history σE over the last fifteen (<NUM>) frames is determined using the following relation: <MAT>.

The resulting deviation σE gives an indication on the energy stability of the decoded synthesis. Typically, music has a higher energy stability (lower statistical deviation of the energy variation history) than speech.

Additionally, the first step classification is used to evaluate the interval between two frames classified as unvoiced NUV when the frame energy Ef, as formulated in equation (<NUM>) is higher than -12dB. When a frame is classified as unvoiced and the frame energy Ef is greater than -9dB, meaning that the signal is unvoiced but not silence, if the long term active speech energy Elt, as formulated in Equation (<NUM>), is below 40dB the unvoiced interval counter is set to <NUM>, otherwise the unvoiced interval counter NUV is decreased by <NUM>. The counter NUV is also limited between <NUM> and <NUM> for active speech signal and between <NUM> and <NUM> for inactive speech signal. It is reminded that, in the illustrative example, the difference between active and inactive speech signal may be deduced from the voice activity detection VAD information included in the bitstream <NUM>.

A long term average is derived from this unvoiced frame counter as follow for active speech signal:
<MAT>.

And as follows for inactive speech signal:
<MAT>.

Furthermore, when the long term average is very high and the deviation σE is high, for example when NUVlt > <NUM> and σE > <NUM> in the current example of implementation, the long term average is modified as follow:
<MAT>.

This parameter on long term average of the number of frames between frames classified as unvoiced is used by the classifier <NUM>-<NUM>-<NUM>-<NUM> to determine if the frame should be considered as generic audio or not. The more the unvoiced frames are close in time, the more likely the frame has speech characteristics (less probably generic audio). In the illustrative example, the threshold to decide if a frame is considered as generic audio GA is defined as follows:
A frame is GA if : <MAT>.

The parameter <MAT>, defined in equation (<NUM>), is added to not classify large energy variation as generic audio, but to keep it as active speech.

The modification performed on the excitation depends on the classification of the frame and for some type of frames there is no modification at all. The next table <NUM> summarizes the case where a modification can be performed or not.

During the frequency-domain modification phase, the excitation needs to be represented into the transform-domain. For example, the time-to-frequency conversion is achieved by a time-to-frequency domain converter <NUM> of the converter/modifier <NUM> using a type II DCT (Discrete Cosine Transform) giving a frequency resolution of <NUM> but any other suitable transform can be used. In case another transform is used the frequency resolution (defined above), the number of frequency bands and the number of frequency bins per bands (defined further below) may need to be revised accordingly. The frequency representation of the time-domain CELP excitation fe calculated in the time-to-frequency domain converter <NUM> is given below: <MAT> where etd(n) is the time-domain CELP excitation, and L is the frame length. In the example of AMR-WB, the frame length is <NUM> samples for a corresponding inner sampling frequency of <NUM>.

In a time-domain CELP decoder such as <NUM>, the time-domain excitation signal is given by <MAT> where v(n) is the adaptive codebook contribution, b is the adaptive codebook gain, c(n) is the fixed codebook contribution, g is the fixed codebook gain.

Before any modification to the time-domain excitation, the converter/modifier <NUM> comprises a gain calculator <NUM>-<NUM>-<NUM> itself including a sub-calculator <NUM> to compute the energy per band Eb of the frequency-domain excitation and keeps the computed energy per band Eb in memory for energy adjustment after the excitation spectrum reshaping. For a <NUM> sampling frequency, the energy can be computed by the sub-calculator <NUM> as follow : <MAT> where CBb represents the cumulative frequency bins per band and Bb the number of bins per frequency band defined as: <MAT> <MAT>.

The low frequency bands may correspond to the critical audio bands as described in [<CIT>, "A method and device for efficient frame erasure concealment in linear predictive based speech codecs"], but the frequency bands above <NUM> may be a little shorter to better match the possible spectral energy variation in those bands. Any other configuration of spectral bands is also possible.

To achieve a transparent switching between the non-modified excitation and the modified excitation for inactive frames and active unvoiced frames, at least the lower frequencies of the time-domain excitation contribution are kept. The converter/modifier <NUM> comprises a cut-off frequency calculator <NUM> to determine a frequency where the time-domain contribution stop to be used, the cut-off frequency fc, having a minimum value of <NUM>. This means that the first <NUM> of the decoded excitation is always kept and depending on the decoded pitch value from the time-domain excitation decoder <NUM>, this cut-off frequency can be higher. The <NUM>th harmonic is computed from the lowest pitch of all sub-frames and the time-domain contribution is kept up to this <NUM>th harmonic. An estimate of the <NUM>th harmonic is calculated as follows: <MAT> where Fs = <NUM> Hz, Nsub is the number of sub-frames and T is the decoded sub-frame pitch. For all i < Nb where Nb is the maximum frequency band included in frequency range Lf, a verification is made to find the band in which the <NUM>th harmonic is located by searching for the highest band for which the following inequality is still verified: <MAT> where Lf is defined as: <MAT>.

The index of that frequency band in Lf will be called i<NUM>th and it indicates the frequency band where the <NUM>th harmonic is likely to be located. The calculator cut-off frequency calculator <NUM> computes the final cut-off frequency ftc as the higher frequency between <NUM> and the last frequency of the frequency band in which the <NUM>th harmonic is likely to be located (Lf (i<NUM>th)), using the following relation: <MAT>.

The converter/modifier <NUM> further comprises a zeroer <NUM> that zeroes the frequency bins of the frequency bands above the cut-off frequency fc.

For inactive frames and active unvoiced frames, a normalizer <NUM> of the converter/modifier <NUM> normalizes the frequency bins below fc of the frequency bands of the frequency representation of the time-domain CELP excitation fe between [<NUM>, <NUM>] using the following relation: <MAT>.

Then, the converter/modifier <NUM> comprises a random noise generator <NUM> to generate random noise and a simple noise fill is performed through an adder <NUM> to add noise over all the frequency bins at a constant level. The function describing the noise addition is defined below as:
for <MAT> where rand is a random number generator which is limited between -<NUM> to <NUM>.

Sub-calculator <NUM> of the gain calculator <NUM>-<NUM>-<NUM> determines the energy per band after the spectrum reshaping Eb' using the same method as described in above section <NUM>.

For inactive frames and active unvoiced frames, the energy matching consists only in adjusting the energy per band after the excitation spectrum modification to its initial value. For each band i, sub-calculator <NUM> of the gain calculator <NUM>-<NUM>-<NUM> determines a matching gain Gb to apply to all bins in the frequency band for matching the energy as follows: <MAT> where Eb(i) is the energy per band before excitation spectrum modification as determined in sub-calculator <NUM> using the method of above section <NUM> and E'b(i) is the energy per band after excitation spectrum modification as calculated in sub-calculator <NUM>. For a specific band i, the modified (de-normalized) frequency-domain excitation <MAT> as determined in sub-calculator <NUM> can be written as:
for <MAT> where CBb and Bb are defined in above section <NUM>.

Reference will now be made to <FIG>. For generic audio frames as determined by the classifier portion <NUM>, the normalization is slightly different and performed by a normalizer <NUM>. First the normalization factor Nf changes from band to band, using a higher value for low frequency bands and a lower value for high frequency bands. The idea is to allow for higher amplitude in the low frequency bands where the location of the pulses is more accurate and lower amplitude in the higher frequency bands where the location of the pulses is less accurate. In this illustrative example, the varying normalization factor Nf by frequency band is defined as : <MAT>.

For a specific frequency band i, the normalization of the frequency representation of the time-domain excitation (frequency-domain excitation) fe of generic audio frames can be described as follow: <MAT> where Bb is the number of bins per frequency band, the cumulative frequency bins per bands is CBb and feN(j) is the normalized frequency-domain excitation. Bb and CBb are described in the above section <NUM>.

Furthermore, the normalizer <NUM> comprises a zeroer (not shown) to zero all the frequency bins below a fraction Zf of the maximum value of feN(j) in each frequency band to obtain f'eN(j) : <MAT> where Zf can be represented as: <MAT>.

A more aggressive zeroing can be performed by increasing the value of the vector Zf, if it is desired to increase the peakyness of the spectrum.

Calculator portion <NUM> of a gain calculator <NUM>-<NUM>-<NUM> determines the energy per band after spectrum reshaping Eb' using the same method as described in above section <NUM>.

<FIG> shows the gain calculator <NUM>-<NUM>-<NUM> and <FIG> describes in more detail calculator portion <NUM> of this gain calculator.

For generic audio frames, the energy matching is trickier since it aims at increasing the spectral dynamic as well. For each frequency band i, a sub-calculator <NUM> of calculator portion <NUM> of the gain calculator <NUM>-<NUM>-<NUM> computes an estimated gain Ge defined similarly as in equation (<NUM>): <MAT> where Eb(i) is the energy per band before excitation spectrum modification as determined in calculator portion <NUM> using the method as described in above section <NUM>, and E'b(i) is the energy per band after excitation spectrum modification as calculated in calculator portion <NUM>.

A sub-calculator <NUM> of the calculator portion <NUM> applies the gain Ge to the first <NUM> (or first <NUM> bands) of the normalized frequency-domain excitation f'eN from the normalizer <NUM> and spectrum splitter <NUM>-<NUM> to provide a modified (de-normalized) frequency-domain excitation f'edN using the following relation: <MAT>.

A finder <NUM> determines the maximum value max <MAT> per band i above <NUM>, where a = CBb(i) and b = CBb(i) + Bb(i) are defined in above section <NUM>.

For the frequency bands comprised between <NUM> and <NUM> (bands <NUM> to <NUM>) of the normalized frequency-domain excitation (see module <NUM> and <NUM>), if the normalized frequency-domain excitation in a frequency bin <MAT> (see module <NUM>), an amplifier <NUM> amplifies the gain Go from the sub-calculator <NUM> by a factor <NUM> as shown in the upper line of Equation (<NUM>). A sub-calculator <NUM> applies the amplified gain from amplifier <NUM> to the normalized spectral excitation f'eN in the frequency bin according to the first line of Equation (<NUM>) to obtain the modified (de-normalized) frequency-domain excitation f'edN.

Again for the frequency bands comprised between <NUM> and <NUM> (bands <NUM> to <NUM>) of the normalized frequency-domain excitation (see module <NUM> and <NUM>), if the normalized frequency-domain excitation in a frequency bin <MAT> (see module <NUM>), an attenuator <NUM> attenuates the gain Ge from the sub-calculator <NUM> by a factor <NUM> as shown in the lower line of Equation (<NUM>). A sub-calculator <NUM> applies the attenuated gain from attenuator <NUM> to the normalized spectral excitation f'eN in the frequency bin according to the lower line of Equation (<NUM>) to obtain the modified (de-normalized) frequency-domain excitation f'edN.

To summarize, the modified (de-normalized) spectral excitation f'edN is given as follows: <MAT>.

Finally for higher parts of the spectrum, in this example the frequency bands above <NUM> (bands > <NUM>) of the normalized frequency-domain excitation (see module <NUM> and <NUM>), if the normalized frequency-domain excitation in a frequency bin <MAT> (see module <NUM>), a tilt which is a function of the frequency band i and which can also be a function of the bit rate is added to the gain Ge to compensate for the too low energy estimation of the LPC filter. The value of the tilt per frequency band δ(i) is formulated as: <MAT>.

The tilt is calculated by tilt calculator <NUM>-<NUM> and is applied to the normalized frequency-domain excitation f'eN by frequency bin according to the upper line of Equation (<NUM>) by a sub-calculator <NUM> to obtain the modified (denormalized) frequency-domain excitation f'odN.

Again for higher parts of the spectrum, in this illustrative example the frequency bands above <NUM> (bands > <NUM>) of the normalized frequency-domain excitation (see module <NUM> and <NUM>), if the normalized frequency-domain excitation in a frequency bin <MAT> (see module <NUM>), an attenuator <NUM> calculates an attenuation gain <MAT> applied to the normalized spectral excitation f'eN by frequency bin according to the lower line of Equation (<NUM>) by a sub-calculator <NUM> to obtain the modified (de-normalized) frequency-domain excitation f'edN.

To summarize, the denormalized spectral excitation f'edN is determined as follows: <MAT> where a and b are described herein above. It is also possible to further increase the gain applied to the latest bands, where the energy matching of the LPC is the worst.

A combiner <NUM> combines the contributions to the modified (denormalized) frequency-domain excitation f'edN from the sub-calculators <NUM>, <NUM>, <NUM>, <NUM> and <NUM> to form the complete modified (de-normalized) frequency-domain excitation f'edN.

After the frequency domain processing is completed, an inverse frequency-time transform <NUM> is applied to the modified (de-normalized) frequency-domain excitation f'edN from combiner <NUM> to find the time-domain modified excitation. In this illustrative embodiment, the frequency-to-time conversion is achieved with the inverse of the same type II DCT as used for the time-to-frequency conversion giving a resolution of <NUM>. Again, any other transforms can be used. The modified time-domain excitation <MAT> is obtained as below: <MAT> where <MAT> is the frequency representation of the modified excitation, and L is the frame length. In this illustrative example, the frame length is <NUM> samples for a corresponding inner sampling frequency of <NUM> (AMR-WB).

Once the excitation modification is completed, the modified excitation is processed through the synthesis filter <NUM> to obtain a modified synthesis for the current frame. The overwriter <NUM> uses this modified synthesis to overwrite the decoded synthesis thus to increase the perceptual quality.

Claim 1:
A device for modifying, during decoding of a sound signal, a synthesis of a time-domain excitation decoded by a time-domain decoder (<NUM>), comprising:
a classifier (<NUM>, <NUM>, <NUM>) configured to classify the synthesis of the decoded time-domain excitation into one of a number of categories;
a first converter (<NUM>, <NUM>) configured to convert the decoded time-domain excitation into a frequency-domain excitation;
a modifier (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>,<NUM>) configured to modify the frequency-domain excitation depending on the category in which the synthesis of the decoded time-domain excitation is classified by the classifier (<NUM>, <NUM>, <NUM>);
a second converter (<NUM>, <NUM>) configured to convert the modified frequency-domain excitation into a modified time-domain excitation;
a synthesis filter (<NUM>) configured to be supplied with the modified time-domain excitation to produce a modified synthesis of the decoded time-domain excitation;
wherein the modifier (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>) comprises:
a calculator (<NUM>) configured to calculate a cut-off frequency where a time-domain excitation contribution stops to be used,
wherein the cut-off frequency has a minimum value of <NUM>;
a zeroer (<NUM>) configured to zero the frequency-domain excitation above the cut-off frequency;
a normalizer (<NUM>) of the frequency-domain excitation below the cut-off frequency;
a random noise generator (<NUM>) configured to generate a random noise; and
an adder (<NUM>) configured to add the random noise to the frequency-domain excitation zeroed above the cut-off frequency and normalized below said cut-off frequency.