Classifications

• • 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
  • G10L19/06—Determination or coding of the spectral characteristics, e.g. of the short-term prediction coefficients
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
  • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
  • G10K11/002—Devices for damping, suppressing, obstructing or conducting sound in acoustic devices
• • 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
  • G10L19/02—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 spectral analysis, e.g. transform vocoders or subband vocoders
  • G10L19/0212—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 spectral analysis, e.g. transform vocoders or subband vocoders using orthogonal transformation
• • 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
  • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
  • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
• • 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
  • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
  • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
  • G10L21/0208—Noise filtering
  • G10L2021/02082—Noise filtering the noise being echo, reverberation of the speech

Definitions

• the invention relates to a method for suppressing the late reverberation of a sound signal.
• the invention is more particularly, but not exclusively, adapted to the field of the treatment of reverberation in a closed space.
• Figure 1 shows an omnidirectional sound source 100 positioned in a closed space 110, such as a motor vehicle or a room, and a microphone 120.
• a sound signal emitted by the omnidirectional sound source 100 is propagated in all directions.
• the signal observed at the microphone is formed by the superposition of several delayed and attenuated versions of the sound signal emitted by the omnidirectional sound source 100.
• the microphone 120 first captures the source signal 130, also called signal direct 130, but also reflected signals 140 on the walls of the closed space 110.
• the various reflected signals 140 have traveled acoustic paths of different lengths and have been attenuated by the absorption of the walls of the closed space 110, the phase and the amplitude of the reflected signals 140 picked up by the microphone 120 are therefore different.
• the microphone 120 picks up the early reflection signals with a small delay compared to the source signal 130, of the order of zero milliseconds to fifty milliseconds. Said early reflection signals are temporally and spatially separated from the source signal 130, but the human ear does not perceive these early reflection signals and the source signal 130 separately by virtue of an effect called "precedence effect".
• the sound signal emitted by the omnidirectional sound source 100 is a speech signal
• the temporal integration of the early reflection signals by the human ear makes it possible to highlight certain characteristics of the speech, which favors the speech signal. intelligibility of the sound signal.
• the boundary between early reflections and late reverberation is between fifty milliseconds and four-
• SUBSTITUTE SHEET (RULE 26) twenty milliseconds.
• the late reverberation includes many signals reflected close together in time and therefore impossible to separate. The set of these reflected signals is therefore considered in a probabilistic framework as a random distribution whose density increases with time.
• the sound signal emitted by the omnidirectional sound source 100 is a speech signal
• the late reverberation degrades the quality of said sound signal and its intelligibility. Said late reverberation also affects the performance of speech recognition and sound source separation systems.
• inverse filtering seeks to identify the impulse response of the closed space 1 10 and then build an inverse filter to compensate for the effects of reverberation at the sound signal.
• This method exploits in the time domain distortions introduced by the reverberation on parameters of a model of linear prediction of the sound signal.
• a filter maximizing the higher order moments of said residual is constructed. This method is suitable for short pulse responses and is mainly used to compensate for early reflection signals.
• this method assumes that the impulse response of the closed space 1 10 is invariant in time. In addition, this method does not model the late reverberation. This method must thus be combined with another method dealing with late reverberation. These two combined methods require many iterations before obtaining convergence, so that said methods can not be implemented for a real-time application. In addition, inverse filtering introduces artifacts such as pre-echoes, which must then be compensated.
• a second method called "cepstral” aims to separate the effect of the closed space 1 10 and the sound signal in the cepstral area. Indeed, the reverberation modifies the average and the variance of the cepstres of the signals reflected with respect to the average and the variance of the cepstres of the source signal 130. Thus, when the mean and the variance of the cepstres are normalized, the reverberation is attenuated.
• This method is particularly useful for speech recognition problems since the reference databases of the recognition systems can also be normalized to approach the signals picked up by the microphone 120. However, the effects of the closed space 1 10 and the sound signal are not completely separable in the cepstral domain. The implementation of the method therefore causes a distortion of the timbre of the sound signal emitted by the omnidirectional sound source 100. In addition, this method processes the early reflections rather than the late reverberation.
• a third method called "estimating the spectral power density of late reverberation" allows to establish a parametric model of late reverberation.
• an estimate of the spectral density of late reverberation power makes it possible to construct a spectral subtraction filter for the dereverberation.
• Spectral subtraction introduces artifacts, such as musical noise, but said artifacts can be limited by applying more complex filtering schemes used by denoising methods.
• the reverberation time is a difficult parameter to estimate accurately.
• the reverberation time estimate is distorted by background noise and other interfering sound signals.
• this estimate of the reverberation time is time consuming and therefore lengthens the execution time.
• a fourth method exploits the parsimony of the speech signals in the time / frequency plane.
• late reverberation is modeled as a delayed and attenuated version of the current observation whose attenuation factor is determined by solving a maximum likelihood problem, with a parsimony constraint.
• the invention is intended in particular to solve all or part of the aforementioned problems.
• the invention relates to a method for suppressing the late reverberation of a sound signal characterized in that it comprises the following steps:
• the method which is the subject of the invention is rapid and has a reduced complexity. This method is therefore usable in real time. In addition, this method does not introduce artifacts and is robust to background noise. In addition, said method reduces background noise and is compatible with noise reduction methods.
• the invention can be implemented according to the embodiments advantageous below, which can be considered individually or in any combination technically operative.
• the method further comprises the following steps:
• the step of calculating the plurality of prediction vectors is performed by minimizing, for each vector prediction, the term Xv - D a, which is the Euclidean norm of the difference between the observation vector undersampled associated with said prediction vector and the analysis dictionary associated with said prediction vector multiplied by said prediction vector, taking into account the constraint a i ⁇ ,, according to which the norm 1 of said prediction vector is less than or equal to a parameter maximum intensity of late reverberation.
• the value of the maximum intensity parameter of the late reverberation is between 0 and 1.
• the method further comprises the following step:
• the method further comprises the following step: • application of a frequency-time transformation to the complex dereverberated signal in order to obtain a dereverberated temporal signal.
• the method further comprises a step of constructing a dereverberation filter according to the model
• ⁇ is the signal-to-noise ratio a posteriori.
• FIG. 1 (already described): a schematic representation of an omnidirectional sound source and a microphone positioned in a closed space according to an exemplary embodiment of the invention
• FIG. 2 a schematic representation of a device for dereverberation of a sound signal according to an exemplary embodiment of the invention
• FIG. 3 a schematic representation of a dereverberation unit of a dereverberation device of a sound signal according to an exemplary embodiment of the invention
• FIG. 4 a schematic representation of a unit for estimating the late reverberation of a dereverberation device of a sound signal according to an embodiment of the invention
• FIG. 5 a schematic representation of a grouping in a sub-band of a module of a complex time-frequency transform of an input signal according to an exemplary embodiment of the invention
• FIG. 6 a schematic representation of a prediction vector calculation unit of a device for the dereverberation of a sound signal according to an embodiment of the invention
• FIG. 7 is a diagrammatic representation of a prediction vector calculation unit of a dereverberation device for a sound signal according to an embodiment of the invention.
• FIG. 8 a schematic representation of a reverberation evaluation unit of a dereverberation device of a sound signal according to an exemplary embodiment of the invention
• FIG. 9 a functional diagram showing different steps of the method according to an exemplary embodiment of the invention.
• the invention implements a device for the dereverberation of a sound signal emitted by an omnidirectional sound source 100 positioned in a closed space 1 10, such as a motor vehicle or a room, and picked up by a microphone 120.
• Said device for Dereverberation is inserted into the audio processing chain of a device such as a telephone.
• This dereverberation device comprises a unit for applying a time-frequency transform 200, a dereverberation unit 210 and a unit for applying a frequency-time transform 220 (see FIG. 2).
• the dereverberation unit 210 comprises a late reverberation estimating unit 300 and a filtering unit 310 (see FIG.
• the late reverberation estimating unit 300 includes a subband consolidation unit 400, a prediction vector calculation unit 410 and a reverberation evaluation unit 420 (see Fig. 4).
• the prediction vector calculation unit 410 includes an observation construction unit 700, an analysis dictionary construction unit 710 and a LASSO 720 resolution unit (see Fig. 7).
• the reverberation evaluation unit 420 comprises a synthesis dictionary construction unit 800 (see FIG.
• a microphone 120 picks up an input signal x (t) formed by the superposition of several delayed and attenuated versions of the sound signal emitted by the omnidirectional sound source 100. Indeed, the microphone 120 first captures the source signal 130, also called direct signal 130, but also reflected signals 140 on the walls of the closed space 1 10. The various reflected signals 140 have traveled acoustic paths of different lengths and have been attenuated by the absorption of the walls of the closed space 1 10, the phase and the amplitude of the reflected signals 140 picked up by the microphone 120 are different.
• the microphone 120 captures the reflection signals early with a small delay compared to the source signal 130, of the order of zero milliseconds to fifty milliseconds. Said early reflection signals are temporally and spatially separated from the source signal 130 but the human ear does not perceive these early reflection signals and the source signal 130 separately by virtue of an effect called "precedence effect".
• the sound signal emitted by the omnidirectional sound source 100 is a speech signal
• the temporal integration of the early reflection signals by the human ear makes it possible to highlight certain characteristics of the speech, which favors the speech signal. intelligibility of the sound signal.
• the microphone 120 captures the late reverberation between fifty milliseconds and eighty milliseconds after the arrival of the source signal 130.
• the late reverb includes many signals reflected close together over time and thus impossible to separate.
• the set of these reflected signals is therefore considered in a probabilistic framework as a random distribution whose density increases with time.
• the sound signal emitted by the omnidirectional sound source 100 is a speech signal
• the late reverberation degrades the quality of said sound signal and its intelligibility. Said late reverberation also affects the performance of speech recognition and sound source separation systems.
• the input signal x (t) is sampled at a sampling frequency f s .
• the input signal x (t) is thus subdivided into samples.
• the power spectral density of the late reverberation is estimated then a dereverberation filter is constructed by the dereverberation unit 210.
• the estimate of the power spectral density of the late reverberation, the construction of the dereverberation filter and the application of said dereverberation filter are performed in the frequency domain.
• a time-frequency transformation is applied to the input signal x (t) by the application unit of the Short-Fourier Transform 200 to obtain a complex time-frequency transform of the input signal x (t) denoted X c ⁇ cf.
• the transformation Time-frequency is a short-term Fourier Transformation.
• Each element X kn of the complex time-frequency transform X e is calculated as follows:
• k is a frequency sampling index of value between 1 and a number K
• n is a time index of value between 1 and a number N
• w (m) is a sliding window of analysis
• m is the index of the elements belonging to a frame
• M is the length of a frame, that is to say the number of samples of a frame
• R is the step of advancement of the time-frequency transformation.
• the input signal x (t) is analyzed by frames of length M with a pitch R equal to M / 4 samples. For each frame of the input signal x (t) in the time domain a discrete time-frequency transform of sampling frequency index k and of time index n is thus calculated by means of the time-frequency transformation algorithm to get a comunic X kn signal defined by dule of the complex signal X kn and ZX kn is the phase of the complex signal X kn .
• the power spectral density estimation of the late reverberation is performed on the module of the complex time-frequency transform of the input signal X e , denoted X.
• the phase of the complex time-frequency transform X e denoted ZX is kept in memory and is used to reconstruct a dereverberated signal in the time domain after application of the dereverberation filter.
• the module X of the complex time-frequency transform of the input signal X e is then grouped into sub-bands. More precisely, said module X comprises the number K of spectral lines denoted X k .
• the term "spectral line” here designates all the samples of the module X of the complex time-frequency transform of the input signal X for the sampling frequency index k and all the time indices n.
• the sub-banding unit 400 groups the K spectral lines X k into a number J of sub-bands, in order to obtain a sub-sampled module denoted X having a number J of spectral lines denoted X where j is a subsampling frequency index between 1 and the number J.
• Each subband thus comprises a plurality of spectral lines X k , the frequency index k belonging to an interval having a lower bound b j and an upper bound e r in one example, each sub-band corresponds to one octave to take into account the pattern of sound perception of the human ear.
• the subband grouping unit 400 calculates, for each subband, a mean Mean of the spectral lines X k of said subband in order to obtain the J spectral lines X j of the module sub-sampled by frequency X (see Figure 5).
• the prediction vector calculation unit 410 calculates for each spectral line X j of the subsampled module at frequency X and for each temporal index n a prediction vector a. n (see Figure 6). More precisely, in a step 905, the observation construction unit 700 constructs, for each temporal index n and sub-sampling frequency index j, a subsampled observation vector Xv jn from the set of samples X ni . n belonging to the jth spectral line X.
• Each subsampled observation vector Xv n is defined by
• Each observation vector v n is of size N xl, where the number N is the length of the observation.
• the length of observation N is the number of the frames of the time-frequency transformation necessary for the estimation of the late reverberation.
• the length of the observation N makes it possible to define the temporal resolution of the estimate. As the length of observation N increases, the complexity of the system decreases.
• the subsampling of the X module of the complex time-frequency transform of the input signal X e makes it possible, among other things, to apply the method in real time.
• the analysis dictionaries building unit 710 builds analysis dictionaries D a . More precisely, for each temporal index n and sub-sampling frequency index j, an analysis dictionary D j a n is constructed by concatenating a number L of past observation vectors determined in step 905.
• the delay ⁇ is the frame delay between the subsampled current observation vector Xv in and the other subsampled observation vectors belonging to the analysis dictionary D j a n .
• Said delay ⁇ makes it possible to reduce the distortions introduced by the method.
• This delay ⁇ also makes it possible to improve the separation of the late reverberation and the early reflections.
• a number L + ⁇ + ⁇ of frames must be kept in memory.
• LASSO the resolution unit of LASSO 720 solves a problem called "LASSO" which is to minimize the Euclidean norm
• Xv. - D a a taking into account the constraint ⁇ Where ⁇ is a maximum intensity setting.
• ⁇ is a maximum intensity setting.
• LARS a known method, called LARS, according to the Anglo-Saxon acronym of "Least Angle Regression" solves the problem.
• the constraint a ni ⁇ makes it possible to favor solutions with few non-zero elements, that is, parsimonious solutions.
• the maximum intensity parameter ⁇ adjusts the estimated maximum intensity of the late reverberation. This maximum intensity parameter ⁇ depends a priori on the acoustic environment, that is to say in an example of the closed space 1 10.
• the method operates in a wide variety of closed spaces 1 10 without requiring any particular adjustment, which makes it possible to avoid errors in estimating the reverberation time of the closed space 1 10.
• the method according to the invention does not require a parameter to be estimated, which allows the application of said method in real time.
• the value of the maximum intensity parameter ⁇ is between 0 and 1. In one example, the value of the maximum intensity parameter ⁇ is equal to 0.5, which is a good compromise between the reduction of the reverberation and the overall quality of the process.
• a current observation vector Xv kn is created from the set of samples belonging to the k-th spectral line X k of the module X of the complex time-frequency transform and between the instants n x and n, denoted by X ni . n where n is the index of current time and n - x n is the size of the memory of the dereverberation apparatus.
• a step 909 the structural unit of a synthesis dictionary 800 builds a dictionary D s synthesis. More precisely, for each temporal index n and each sampling frequency index k, the synthetic dictionary D k s n is constructed by concatenating a number L of past observation vectors determined in step 908.
• the synthesis dictionary D k s is thus defined as the matrix
• an estimate of the power spectral density of the late reverberation or the late reverberation spectrum X k ' n is constructed by multiplying the dictionary of synthesis D k s n with the prediction vector.
• the prediction vector. n therefore indicates the columns of the synthesis dictionary that have been selected for estimating the reverberation, as well as the contribution of each of them to the reverberation.
• the spectrum of the late reverberation X e is considered in the rest of the process as a noise signal to be eliminated.
• a filtering of the reverberation is carried out by the filter unit 31 0. More specifically, in a step 91 1, for each time index n and each sampling frequency index k, a dereverberation filter G k is built according to the formula
• ⁇ ⁇ ⁇ is the signal-to-noise ratio a priori, calculated as follows
• ⁇ ⁇ is the signal-to-noise ratio a posteriori, calculated according to the formula
• R kn is the smoothed late reverberation calculated as follows
• a is a first smoothing constant and ⁇ is a second smoothing constant.
• first smoothing constant a is 0.77 and the second smoothing constant ⁇ is 0.98.
• the estimated reverberation is non-stationary in the long term because the sound signal emitted by the omnidirectional sound source 100, which causes said estimated reverberation is not stationary in the long term.
• Excessive variations in the estimated reverb can introduce annoying artifacts during filtering.
• a recursive smoothing is performed to calculate the power spectral density of the late reverberation.
• observation vectors Xv kn are filtered by the dereverberation filter G kn calculated in step 91 1 to obtain a signal module dereverberated Y kn calculated as follows
• the filter constructed in step 91 1 strongly attenuates certain vectors of observations Xv kn , which generates artifacts harmful to the quality of the dereverberated signal. To limit said artifacts, a lower bound is imposed on the attenuation of the filter. Thus, for each sampling frequency index k and for each time index n, if the dereverberation filter G kn is less than or equal to a minimum value of the dereverberation filter Gmin, then said dereverberation filter G in is equal to said value filter minimum Gmin dereverberation.
• a step 91 3 for each sampling frequency index k and each time index n, the dereverberated signal module Y kn and the phase
• a frequency-time transformation is applied by the unit for applying a frequency-time transformation 220 to the dereverbere complex signal Y kn in order to obtain a time-domain dereverberated time signal y (t).
• the frequency-time transformation is a Short Term Inverse Fourier Transformation.
• the value of the number of observation vectors L is equal to 1 0, the value of the number of observation length N is equal to 8, the value of the delay ⁇ is equal to 5, the value of the parameter of maximum intensity ⁇ is equal to 0.5, the value of the number K is equal to 257, the value of the number J is equal to 1 0, the value of the length of a frame M is equal to 51 2 and the value minimum of the Gmin dereverberation filter is equal to -1 2 decibels.
• This choice of parameters allows the application of the process in real time.
• the method of suppressing the late reverberation of a sound signal according to the invention is rapid and has a reduced complexity. This method is therefore usable in real time. In addition, this method does not introduce artifacts and is robust to background noise. In addition, said method reduces background noise and is compatible with noise reduction methods.
• the method of suppressing the late reverberation of a sound signal according to the invention requires a single microphone to accurately process the reverberation.

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• 2013
  • 2013-07-23 FR FR1357226A patent/FR3009121B1/fr active Active
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