WO2009109050A1 - System and method for enhancing a decoded tonal sound signal - Google Patents
System and method for enhancing a decoded tonal sound signal Download PDFInfo
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- WO2009109050A1 WO2009109050A1 PCT/CA2009/000276 CA2009000276W WO2009109050A1 WO 2009109050 A1 WO2009109050 A1 WO 2009109050A1 CA 2009000276 W CA2009000276 W CA 2009000276W WO 2009109050 A1 WO2009109050 A1 WO 2009109050A1
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
- G10L19/04—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques
- G10L19/26—Pre-filtering or post-filtering
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
- G10L25/18—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
Definitions
- the present invention relates to a system and method for enhancing a decoded tonal sound signal, for example an audio signal such as a music signal coded using a speech-specific codec.
- the system and method reduce a level of quantization noise in regions of the spectrum exhibiting low energy.
- a speech coder converts a speech signal into a digital bit stream which is transmitted over a communication channel or stored in a storage medium.
- the speech signal is digitized, that is, sampled and quantized with usually 16-bits per sample.
- the speech coder has the role of representing the digital samples with a smaller number of bits while maintaining a good subjective speech quality.
- the speech decoder or synthesizer operates on the transmitted or stored bit stream and converts it back to a sound signal.
- CELP Code-Excited Linear Prediction
- the CELP coding technique is a basis of several speech coding standards both in wireless and wireline applications.
- the sampled speech signal is processed in successive blocks of L samples usually called frames, where L is a predetermined number of samples corresponding typically to 10-30 ms.
- a linear prediction (LP) filter is computed and transmitted every frame. The computation of the LP filter typically uses a lookahead, for example a 5-15 ms speech segment from the subsequent frame.
- the L-sample frame is divided into smaller blocks called subframes.
- an excitation signal is usually obtained from two components, a past excitation and an innovative, fixed-codebook excitation.
- the component formed from the past excitation is often referred to as the adaptive- codebook or pitch-codebook excitation.
- the parameters characterizing the excitation signal are coded and transmitted to the decoder, where the excitation signal is reconstructed and used as the input of the LP filter.
- the spectrum exhibits a tonal structure wherein several tones are present (corresponding to spectral peaks) and are not harmonically related.
- These music signals are difficult to encode with a low bit rate speech-specific codec using an all-pole synthesis filter and a pitch filter.
- the pitch filter is capable of modeling voice segments in which the spectrum exhibits a harmonic structure comprising a fundamental frequency and harmonics of this fundamental frequency.
- such a pitch filter fails to properly model tones which are not harmonically related.
- the all-pole synthesis filter fails to model the spectral valleys between the tones.
- An objective of the present invention is to enhance a tonal sound signal decoded by a decoder of a speech-specific codec in response to a received coded bit stream, for example an audio signal such as a music signal, by reducing quantization noise in low-energy regions of the spectrum (inter-tone regions or spectral valleys).
- a system for enhancing a tonal sound signal decoded by a decoder of a speech-specific codec in response to a received coded bit stream comprising: a spectral analyser responsive to the decoded tonal sound signal to produce spectral parameters representative of the decoded tonal sound signal; and a reducer of a quantization noise in low-energy spectral regions of the decoded tonal sound signal in response to the spectral parameters from the spectral analyser.
- the present invention also relates to a method for enhancing a tonal sound signal decoded by a decoder of a speech-specific codec in response to a received coded bit stream, comprising: spectrally analysing the decoded tonal sound signal to produce spectral parameters representative of the decoded tonal sound signal; and reducing a quantization noise in low-energy spectral regions of the decoded tonal sound signal in response to the spectral parameters from the spectral analysis.
- the present invention further relates to a system for enhancing a decoded tonal sound signal, comprising: a spectral analyser responsive to the decoded tonal sound signal to produce spectral parameters representative of the decoded tonal sound signal, wherein the spectral analyser divides a spectrum resulting from spectral analysis into a set of critical frequency bands, and wherein each critical frequency band comprises a number of frequency bins; and a reducer of a quantization noise in low-energy spectral regions of the decoded tonal sound signal in response to the spectral parameters from the spectral analyser, wherein the reducer of quantization noise comprises a noise attenuator that scales the spectrum of the decoded tonal sound signal per critical frequency band, per frequency bin, or per both critical frequency band and frequency bin.
- the present invention still further relates to a method for enhancing a decoded tonal sound signal, comprising: spectrally analysing the decoded tonal sound signal to produce spectral parameters representative of the decoded tonal sound signal, wherein spectrally analysing the decoded tonal sound signal comprises dividing a spectrum resulting from the spectral analysis into a set of critical frequency bands each comprising a number of frequency bins; and reducing a quantization noise in low- energy spectral regions of the decoded tonal sound signal in response to the spectral parameters from the spectral analysis, wherein reducing the quantization noise comprises scaling the spectrum of the decoded tonal sound signal per critical frequency band, per frequency bin, or per both critical frequency band and frequency bin.
- Figure 1 is a schematic block diagram showing an overview of a system and method for enhancing a decoded tonal sound signal
- Figure 2 is a graph illustrating windowing in spectral analysis
- Figure 3 is a schematic block diagram showing an overview of a system and method for enhancing a decoded tonal sound signal
- Figure 4 is a schematic block diagram illustrating tone gain correction
- Figure 5 is a schematic block diagram of an example of signal type classifier.
- Figure 6 is a schematic block diagram of a decoder of a low bit rate speech-specific codec using a speech production model comprising a LP synthesis filter modeling the vocal tract shape (spectral envelope) and a pith filter modeling the vocal chords (harmonic fine structure).
- an inter-tone noise reduction technique is performed within a low bit rate speech-specific codec to reduce a level of inter-tone quantization noise for example in musical content.
- the inter-tone noise reduction technique can be deployed with either narrowband sound signals sampled at 8000 samples/s or wideband sound signals sampled at 16000 samples/s or at any other sampling frequency.
- the inter-tone noise reduction technique is applied to a decoded tonal sound signal to reduce the quantization noise in the spectral valleys (low energy regions between tones). In some music signals, the spectrum exhibits a tonal structure wherein several tones are present (corresponding to spectral peaks) and are not harmonically related.
- the pitch filter can model voiced speech segments having a spectrum that exhibits a harmonic structure with a fundamental frequency and harmonics of that fundamental frequency.
- the pitch filter fails to properly model tones which are not harmonically related.
- the all-pole LP synthesis filter fails to model the spectral valleys between the tones.
- the modeled signals will exhibit an audible quantization noise in the low-energy regions of the spectrum (inter-tone regions or spectral valleys).
- the inter-tone noise reduction technique is therefore concerned with reducing the quantization noise in low-energy spectral regions to enhance a decoded tonal sound signal, more specifically to enhance quality of the decoded tonal sound signal.
- the low bit rate speech-specific codec is based on a CELP speech production model operating on either narrowband or wideband signals (8 or 16 kHz sampling frequency). Any other sampling frequency could also be used.
- FIG. 6 An example 600 of the decoder of a low bit rate speech-specific codec using a CELP speech production model will be briefly described with reference to Figure 6.
- a fixed codebook 601 produces a fixed-codebook vector 602 multiplied by a fixed-codebook gain g to produce an innovative, fixed-codebook excitation 603.
- an adaptive codebook 604 is responsive to a pitch delay extracted from the received coded bit stream to produce an adaptive-codebook vector 607; the adaptive codebook 604 is also supplied (see 605) with the excitation signal 610 through a feedback loop comprising a pitch filter 606.
- the adaptive-codebook vector 607 is multiplied by a gain G to produce an adaptive-codebook excitation 608.
- the innovative, fixed-codebook excitation 603 and the adaptive-codebook excitation 608 are summed through an adder 609 to form the excitation signal 610 supplied to an LP synthesis filter 61 1 ; the LP synthesis filter 61 1 is controlled by LP filter parameters extracted from the received coded bit stream.
- the LP synthesis filter 611 produces a synthesis sound signal 612, or decoded tonal sound signal that can be upsampled/downsampled in module 613 before being enhanced using the system 100 and method for enhancing a decoded tonal sound signal.
- 0023] For example, a codec based on the AMR-WB ([1] - 3GPP TS
- AMR-WB speech codec uses an internal sampling frequency of 12.8 kHz, and the signal can be re-sampled to either 8 or 16 kHz before performing reduction of the inter-tone quantization noise or, alternatively, noise reduction or audio enhancement can be performed at 12.8 kHz.
- Figure 1 is a schematic block diagram showing an overview of a system and method 100 for enhancing a decoded tonal sound signal.
- a coded bit stream 101 (coded sound signal) is received and processed through a decoder 102 (for example the decoder 600 of Figure 6) of a low bit rate speech-specific codec to produce a decoded sound signal 103.
- the decoder 102 can be, for example, a speech-specific decoder using a CELP speech production model such as an AMR-WB decoder.
- the decoded sound signal 103 at the output of the sound signal decoder 102 is converted (re-sampled) to a sampling frequency of 8 kHz.
- the inter-tone noise reduction technique disclosed herein can be equally applied to decoded tonal sound signals at other sampling frequencies such as 12.8 kHz or 16 kHz.
- Preprocessing can be applied or not to the decoded sound signal
- the decoded sound signal 103 is, for example, pre- emphasized through a preprocessor 104 before spectral analysis in the spectral analyser 105 is performed.
- the preprocessor 104 comprises a first order high-pass filter (not shown).
- the first order high-pass filter emphasizes higher frequencies of the decoded sound signal 103 and may have, for that purpose, the following transfer function:
- Spectral analysis of the pre-emphasized decoded sound signal 106 is performed in the spectral analyser 105.
- This spectral analysis uses Discrete Fourier Transform (DFT) and will be described in more detail in the following description.
- DFT Discrete Fourier Transform
- the inter-tone noise reduction technique is applied in response to the spectral parameters 107 from the spectral analyser 107 and is implemented in a reducer 108 of quantization noise in the low-energy spectral regions of the decoded tonal sound signal.
- the operation of the reducer 108 of quantization noise will be described in more detail in the following description.
- An inverse analyser and overlap-add operator 110 (a) applies an inverse DFT (Discrete Fourier Transform) to the inter-tone noise reduced spectral parameters 109 to convert those parameters 109 back to the time domain, and (b) uses an overlap-add operation to reconstruct the enhanced decoded tonal sound signal 1 11.
- - A postprocessor 112 post-processes the reconstructed enhanced decoded tonal sound signal 111 from the inverse analyser and overlap-add operator 1 10.
- This post-processing is the inverse of the preprocessing stage (preprocessor 104) and, therefore, may consist of de-emphasis of the higher frequencies of the enhanced decoded tonal sound signal. Such de-emphasis will be described in more detail in the following description.
- a sound playback system 114 may be provided to convert the post- processed enhanced decoded tonal sound signal 113 from the postprocessor 112 into an audible sound.
- the speech-specific codec in which the inter-tone noise reduction technique is implemented operates on 20 ms frames containing 160 samples at a sampling frequency of 8 kHz.
- the sound signal decoder 102 uses a 10 ms lookahead from the future frame for best frame erasure concealment performance. This lookahead is also used in the inter-tone noise reduction technique for a better frequency resolution.
- the inter-tone noise reduction technique implemented in the reduced 108 of quantization noise follows the same framing structure as in the decoder 102. However, some shift can be introduced between the decoder framing structure and the inter-tone noise reduction framing structure to maximize the use of the lookahead.
- the indices attributed to samples will reflect the inter-tone noise reduction framing structure.
- DFT Discrete Fourier Transform
- spectral analysis is performed in each frame using 30 ms analysis windows with 33% overlap. More specifically, the spectral analysis in the analyser 105 ( Figure 3) is conducted once per frame using a 256-point Fast Fourier Transform (FFT) with the 33.3 percent overlap windowing as illustrated in Figure 2. The analysis windows are placed so as to exploit the entire lookahead. The beginning of the first analysis window is shifted 80 samples after the beginning of the current frame of the sound signal decoder 102.
- FFT Fast Fourier Transform
- the analysis windows are used to weight the pre-emphasized, decoded tonal sound signal 106 for frequency analysis.
- the analysis windows are flat in the middle with sine function on the edges ( Figure 2) which is well suited for overlap-add operations. More specifically, the analysis window can be described as follow:
- Lwmdo * - 240 samples is the si/c of the analysis window. Since a 256-point FTT (X/ 7 - 7 - 256) is used, the windowed signal is padded with 16 zero samples.
- An alternative analysis window could be used in the case of a wideband signal with only a small lookahead available.
- This analysis window could have the following shape: W F 1 FT M
- L tundox fg 360 is the size of the wideband analysis window
- a 512- pomt FFT is used Therefore, the windowed signal is padded with 152 zero samples
- Other radix FFT can potentially be used to reduce as much as possible the zero padding and reduce the complexity
- FFT is performed on the windowed, decoded tonal sound signal to obtain one set of spectral parameters per frame
- the resulting spectrum is divided into critical frequency bands using the intervals having the following upper limits; (17 critical bands in the frequency range 0-4000 Hz and 21 critical frequency bands in the frequency range 0-8000 Hz) (See [2J: J. D. Johnston, "Transform coding of audio signal using perceptual noise criteria," IEEE J. Select. Areas Commun., vol. 6, pp. 314-323, Feb. 1988).
- the critical frequency bands ⁇ 100.0, 200.0, 300.0, 400.0, 510.0, 630.0, 770.0, 920.0, 1080.0, 1270.0, 1480.0, 1720.0, 2000.0, 2320.0, 2700.0, 3150.0, 3700.0, 4400.0, 5300.0, 6700.0, 8000.0 ⁇ Hz.
- M CB ⁇ 3, 3, 3, 3, 3, 4, 5, 4, 5, 6, 7, 7, 9, 10, 12, 14, 17, 22, 28, 44, 41 ⁇ .
- the spectral analyser 105 of Figure 3 also computes the energy of the spectrum per frequency bin, for the first 17 critical bands (115 bins excluding the DC component) using the following relation:
- the spectral analyser 105 computes a total frame spectral energy as an average of the spectral energies of the first 17 critical frequency bands calculated by the spectral analyser 105 in a frame using, the following relation:
- Figure 3 more specifically the above calculated average spectral energy per critical band, spectral energy per frequency bin, and total frame spectral energy are used in the reducer 108 to reduce quantization noise and perform gain correction.
- the inter-tone noise reduction technique conducted by the system and method 100 enhances a decoded tonal sound signal, such as a music signal, coded by means of a speech-specific codec.
- a decoded tonal sound signal such as a music signal
- a speech-specific codec coded by means of a speech-specific codec.
- non-tonal sounds such as speech are well coded by a speech-specific codec and do not need this type of frequency based enhancement.
- the system and method 100 for enhancing a decoded tonal sound signal further comprises, as illustrated in Figure 3, a signal type classifier 301 designed to further maximize the efficiency of the reducer 108 of quantization noise by identifying which sound is well suited for inter-tone noise reduction, like music, and which sound is not, like speech.
- the signal type classifier 301 comprises the feature of not only separating the decoded sound signal into sound signal categories, but also to give instruction to the reducer 108 of quantization noise to reduce at a minimum any possible degradation of speech.
- a schematic block diagram of the signal type classifier 301 is illustrated in Figure 5. In the presented embodiment, the signal type classifier 301 has been kept as simple as possible.
- the principal input to the signal type classifier 301 is the total frame spectral energy E 1 as formulated in Equation (6).
- the signal type classifier 301 comprises a finder 501 that determines a mean of the past forty (40) total frame spectral energy (E t ) variations calculated using the following relation:
- the finder 501 determines a statistical deviation of the energy variation history ⁇ E over the last fifteen (15) frames using the following relation:
- the signal type classifier 301 comprises a memory 502 updated with the mean and deviation of the variation of the total frame spectral energy E 1 as calculated in Equations (7) and (8).
- the resulting deviation ⁇ E is compared to four (4) floating thresholds in comparators 503-506 to determine the efficiency of the reducer 108 of quantization noise on the current decoded sound signal.
- the output 302 ( Figure 3) of the signal type classifier 301 is split into five (5) sound signal categories, named sound signal categories 0 to 4, each sound signal category having its own inter-tone noise reduction tuning.
- the five (5) sound signal categories 0-4 can be determined as indicated in the following Table:
- the sound signal category 0 is a non-tonal sound signal category, like speech, which is not modified by the inter-tone noise reduction technique. This category of decoded sound signal has a large statistical deviation of the spectral energy variation history.
- the tree in between sound signal categories includes sound signals with different types of statistical deviation of spectral energy variation history.
- Sound signal category 1 (biggest variation after "speech type" decoded sound signal) is detected by the comparator 506 when the statistical deviation of spectral energy variation history is lower than a Threshold 1.
- a controller 510 is responsive to such a detection by the comparator 506 to instruct, when the last detected sound signal category was > 0, the reducer 108 of quantization noise to enhance the decoded tonal sound signal within the frequency band 2000 to —*- Hz by reducing the inter-tone quantization noise by a maximum allowed amplitude of 6 dB.
- Sound signal category 2 is detected by the comparator 505 when the statistical deviation of spectral energy variation history is lower than a Threshold 2.
- a controller 509 is responsive to such a detection by the comparator 505 to instruct, when the last detected sound signal category was > 1 , the reducer 108 of quantization noise to enhance the decoded tonal sound signal within the frequency band 1270 to
- Sound signal category 3 is detected by the comparator 504 when the statistical deviation of spectral energy variation history is lower than a Threshold 3.
- a controller 508 is responsive to such a detection by the comparator 504 to instruct, when the last detected sound signal category was > 2, the reducer 108 of quantization noise to enhance the decoded tonal sound signal within the frequency band 700 to —
- Sound signal category 4 is detected by the comparator 503 when the statistical deviation of spectral energy variation history is lower than a Threshold 4.
- a controller 507 is responsive to such a detection by the comparator 503 to instruct, when the last detected signal type category was > 3, the reducer 108 of quantization
- the signal type classifier 301 uses floating thresholds 1-4 to split the decoded sound signal into the different categories 0-
- floating thresholds 1-4 are particularly useful to prevent wrong signal type classification. Typically, decoded tonal sound signal like music gets much lower statistical deviation of its spectral energy variation than non-tonal sound signal like speech. But music could contain higher statistical deviation and speech could contain lower statistical deviation. It is unlikely that speech or music content changes from one to another on a frame basis.
- the floating thresholds acts like reinforcement to prevent any misclassif ⁇ cation that could result in a suboptimal performance of the reducer 108 of quantization noise.
- Counters of a series of frames of sound signal category 0 and of a series of frames of sound signal category 3 or 4 are used to respectively decrease or increase thresholds.
- a counter 512 counts a series of more than 30 frames of sound signal category 3 or 4
- the floating thresholds 1 -4 will be increased by a threshold controller 514 for the purpose of allowing more frames to be considered as sound signal category 4.
- the counter 513 is reset to zero.
- the inverse is also true with sound signal category 0. For example, if a counter 513 counts a series of more than 30 frames of sound signal category 0, the threshold controller 514 decreases the floating thresholds 1-4 for the purpose of allowing more frames to be considered as sound signal category 0.
- the floating thresholds 1 -4 are limited to absolute maximum and minimum values to ensure that the signal type classifier 301 is not locked to a fixed category.
- Thresii) Thres(i) + TH _ UP[_ ⁇ ELSE IF (Nbr _ catO _ frame > 30 )
- VAD Voice Activity Detector
- the frequency band of allowed enhancement and/or the level of maximum inter-tone noise reduction could be completely dynamic (without hard step).
- RedGain, ' l . ⁇
- RedGain ' Allow — band] , where RedGain, is a maximum gain reduction per band, FEhBand is the first band where the inter-tone noise reduction is allowed (vary typically between 400Hz and 2kHz or critical frequency bands 3 and 12). Allow red is the level of noise reduction allowed per sound signal category presented in the previous table and maxjband is the maximum band for the inter tone noise reduction (17 for Narrowband (NB) and 20 for Wideband (WB)).
- J0071 j Inter-tonc noise reduction is applied (see reducer 108 of quantization noise ( Figure 3)) and the enhanced decoded sound signal is reconstructed using an overlap and add operation (see overlap add operator 303 ( Figure 3)).
- the reduction of inter-tone quantization noise is performed by scaling the spectrum in each critical frequency band with a scaling gain limited between g m j n and 1 and derived from the signal-to-noise ratio (SNR) in that critical frequency band.
- SNR signal-to-noise ratio
- a feature of the inter-tone noise reduction technique is that for frequencies lower than a certain frequency, for example related to signal voicing, the processing is performed on a frequency bin basis and not on critical frequency band basis.
- a scaling gain is applied on every frequency bin derived from the SNR in that bin (the SNR is computed using the bin energy divided by the noise energy of the critical band including that bin).
- This feature has the effect of preserving the energy at frequencies near harmonics or tones preventing distortion while strongly reducing the quantization noise between the harmonics.
- per bin analysis can be used for the whole spectrum. Per bin analysis can alternatively be used in all critical frequency bands except the last one.
- inter-tone quantization noise reduction is performed in the reducer 108 of quantization noise.
- per bin processing can be performed over all the 115 frequency bins in narrowband coding (250 frequency bins in wideband coding) in a noise attenuator 304.
- the minimum scaling gain g min is derived from the maximum allowed inter-tone noise reduction in dB, NR max . As described in the foregoing description (see the table above), the signal type classifier 301 makes the maximum allowed noise reduction NR ma ⁇ varying between 6 and 12 dB. Thus minimum scaling gain is given by the relation:
- the scaling gain can be computed in relation to the SNR per frequency bin then per bin noise reduction is performed.
- Per bin processing is applied only to the first 17 critical bands corresponding to a maximum frequency of 3700 Hz.
- the maximum number of frequency bins in which per bin processing can be used is 115 (the number of bins in the first 17 bands at 4 kHz).
- per bin processing is applied to all the 21 critical frequency bands corresponding to a maximum frequency of 8000 Hz.
- the maximum number of frequency bins for which per bin processing can be used is 250 (the number of bins in the first 21 bands at 8kHz).
- noise reduction starts at the fourth critical frequency band (no reduction performed before 400 Hz).
- the signal type classifier 301 could push the starting critical frequency band up to the 12 th .
- the first critical frequency band on which inter- tone noise reduction is performed is somewhere between 400 Hz and 2 kHz and could vary on a frame basis.
- the scaling gain for a certain critical frequency band, or for a certain frequency bin can be computed as a function of the SNR in that frequency band or bin using the following relation:
- the variable SNR of Equation (10) is either the SNR per critical frequency band, SNR CB (i) , or the SNR per frequency bin, SNR B!N (k) , depending on the type of per bin or per band processing.
- the SNR per critical frequency band is computed as follows:
- N CB (0 where E ⁇ (/) and E ⁇ (i) denote the energy per critical frequency band for the past and current frame spectral analyses, respectively (as computed in Equation (4)), and N CB (J.) denote the noise energy estimate per critical frequency band.
- E B ⁇ m ) (Jc) and E B!N (k) denote the energy per frequency bin for the past ( 1 ⁇ and the current frame spectral analysis, respectively (as computed in Equation (5))
- N CB (i) denote the noise energy estimate per critical frequency band
- J 1 is the index of the first frequency bin in the / th critical frequency band
- M CB (i) is the number of frequency bins in critical frequency band i as defined herein above.
- the scaling gain could be computed in relation to the SNR per critical frequency band or per frequency bin for the first voiced bands. If K vo ⁇ > 0 then per bin processing can be performed in the first K V0IC bands. Per band processing can then be used for the rest of the bands. In the case where K VOIC - 0 per band processing can be used over the whole spectrum.
- the smoothing factor O gS used for smoothing the scaling gain g s and can be made adaptive and inversely related to the scaling gain g s itself.
- the smoothing is stronger for smaller gains g s .
- This approach prevents distortion in high SNR segments preceded by low SNR frames, as it is the case for voiced onsets.
- the smoothing procedure is able to quickly adapt and use lower scaling gains upon occurrence of, for example, a voiced onset.
- J 1 is the index of the first frequency bin in the critical frequency band i and M CB (0 is the number of frequency bins in that critical frequency band.
- Temporal smoothing of the scaling gains prevents audible energy oscillations, while controlling the smoothing using a gs prevents distortion in high SNR speech segments preceded by low SNR frames, as it is the case for voiced onsets for example.
- XA ' k + J 1 ) g BlN M k + J 1 ) XR (K + J 1 ), and x) (k + j, )
- J 1 is the index of the first frequency bin in the critical frequency band /
- M CB (i) is the number of frequency bins in that critical frequency band.
- the smoothed scaling gains g CB LP (i) are updated for all critical frequency bands (even for voiced critical frequency bands processed through per bin processing - in this case g CB LP (i) is updated with an average of g mxP (k) belonging to the critical frequency band /)•
- the smoothed scaling gains g BIN LP (k) are updated for all frequency bins in the first 17 critical frequency bands, that is up to frequency bin 1 15 in the case of narrowband coding (the first 21 critical frequency bands, that is up to frequency bin 250 in the case of wideband coding).
- the scaling gains are updated by setting them equal to g CB LP (i) in the first 17 (narrowband coding) or 21 (wideband coding) critical frequency bands.
- the inter-tone noise reduction is performed on the first 17 critical frequency bands (up to 3680 Hz). For the remaining 1 1 frequency bins between 3680 Hz and 4000 Hz, the spectrum is scaled using the last scaling gain g s of the frequency bin corresponding to 3680 Hz.
- the Parseval theorem shows that the energy in the time domain is equal to the energy in the frequency domain. Reduction of the energy of the inter-tone noise results in an overall reduction of energy in the frequency and time domains.
- the reducer 108 of quantization noise comprises a per band gain corrector 306 to rescale the energy per critical frequency band in such a manner that the energy in each critical frequency band at the end of the rescaling will be close to the energy before the inter-tone noise reduction.
- the per band gain corrector 306 comprises an analyser 401 ( Figure 4) which identifies the most energetic bins prior to inter-tone noise reduction as the bins scaled by a scaling gain between ]0.8, 1.0] in the inter-tone noise reduction phase.
- the analyser 401 may also determine the per bin energy prior to inter-tone noise reduction using, for example, Equation (5) in order to identify the most energetic bins.
- the per band gain corrector 306 comprises an analyser 402 to determine the per band spectral energy prior to inter-tone noise reduction using Equation (18), and an analyser 403 to determine the per band spectral energy after the inter-tone noise reduction using Equation (18).
- the per band gain corrector 306 further comprises a calculator 404 to determine a corrective gain as the ratio of the spectral energy of a critical frequency band before inter-tone noise reduction and the spectral energy of this critical frequency band after inter-tone noise reduction has been applied.
- E CB is the critical band spectral energy before inter-tone noise reduction
- E CB ' is the critical frequency band spectral energy after inter-tone noise reduction.
- the total number of critical frequency bands covers the entire spectrum from 17 bands in Narrowband coding to 21 bands in Wideband coding. [00100]
- the rescaling along the critical frequency band / can be performed as follows:
- J 1 is the index of the first frequency bin in the critical frequency band / and M CB ⁇ ) is the number of frequency bins in that critical frequency band.
- M CB ⁇ is the number of frequency bins in that critical frequency band.
- a calculator 405 of the per band gain corrector 306 determines the ratio of energetic events (ratio of the number of energetic bins on total number of frequency bins) per critical frequency band as follow:
- the calculator 405 then computes an additional correction factor to the corrective gain using the following formula:
- the rescaling is performed only in the frequency bins previously scaled by a scaling gain between ] 0.96, 1.0] in the inter-tone noise reduction phase.
- the bit rate is closer will be the energy of the spectrum to the desired energy level.
- the gain correction factor Cp might not be always used.
- a calculator 307 of the inverse analyser and overlap add operator 110 computes the inverse FFT.
- the calculated inverse FFT is applied to the scaled spectral components 308 to obtain a windowed enhanced decoded sound signal in the time domain given by the following relation:
- the signal is then reconstructed in operator 303 using an overlap add operation for the overlapping portions of the analysis. Since a sine window is used on the original decoded tonal sound signal 103 prior to spectral analysis in the spectral analyser 105, the same windowing is applied to the windowed enhanced decoded tonal sound signal 309 at the output of the inverse FFT calculator prior to the overlap add operation.
- the doubled windowed enhanced decoded tonal sound signal is given by the relation:
- the overlap add operation for constructing the enhanced sound signal is performed using the relation:
- n 0,...,L window /3-1
- the overlap-add operation for constructing the enhanced decoded tonal sound signal is performed as follows: «/ - TM ⁇ _ y (0) (whi , 9 . window m / ⁇ JX) / x Q r / ⁇ _ ,
- x ⁇ d (n) is the double windowed enhanced decoded tonal sound signal from the analysis of the previous frame.
- the enhanced decoded tonal sound signal can be reconstructed up to 80 samples from the lookahead in addition to the present inter-tone noise reduction frame.
- deemphasis is performed in the postprocessor 112 on the enhanced decoded sound signal using the inverse of the above described preemphasis filter.
- the postprocessor 112 therefore comprises a deemphasis filter which, in this embodiment, is given by the relation:
- Inter-tone noise energy estimates per critical frequency band for inter-tone noise reduction can be calculated for each frame in an inter-tone noise energy estimator (not shown), using for example the following formula:
- the energy threshold (thr _ener CB ) is used to compute a first inter- tone noise level estimation per critical band (tmp _ ener CB ) which corresponds to the mean of the energies (E BIN ) of all the frequency bins below the preceding energy threshold inside the critical frequency band, using the following relation:
- mcnt is the number of frequency bins of which the energies (E BlN ) are included in the summation and mcnt ⁇ M CB (i) . Furthermore; the number mcnt of frequency bins of which the energy ( ⁇ BIN ) is below the energy threshold is compared to the number of frequency bins (M CB ) inside a critical frequency band to evaluate the ratio of frequency bins below the energy threshold. This ratio accepted ratio CB is used to weight the first, previously found inter-tone noise level estimation (tmp _ ener CB ) .
- a weighting factor p CH of the inter-tone noise level estimation is different among the bit rate used and the accepted _ratioce- A high accepted ratio CB for a critical frequency band means that it will be difficult to differentiate the noise energy from the signal energy. In that case it is desirable to not reduce too much the noise level of that critical frequency band to not risk any alteration of the signal energy. But a low accepted j-atioc ⁇ indicates a large difference between the noise and signal energy levels then the estimated noise level could be higher in that critical frequency band without adding distortion.
- the factor ⁇ CB is modified as follow:
- inter-tone noise estimation per critical frequency band can be smoothed differently if the inter-tone noise is increasing or decreasing.
- N 0 represents the current noise energy for the specified critical frequency band (i) and N ⁇ represents the noise energy of the past frame of the same critical frequency band.
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EP09717868A EP2252996A4 (en) | 2008-03-05 | 2009-03-05 | System and method for enhancing a decoded tonal sound signal |
JP2010548995A JP5247826B2 (en) | 2008-03-05 | 2009-03-05 | System and method for enhancing a decoded tonal sound signal |
RU2010140620/08A RU2470385C2 (en) | 2008-03-05 | 2009-03-05 | System and method of enhancing decoded tonal sound signal |
CA2715432A CA2715432C (en) | 2008-03-05 | 2009-03-05 | System and method for enhancing a decoded tonal sound signal |
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