EP0884926B1 - Verfahren und Vorrichtung zur optimierten Verarbeitung eines Störsignals während einer Tonaufnahme - Google Patents

Verfahren und Vorrichtung zur optimierten Verarbeitung eines Störsignals während einer Tonaufnahme Download PDF

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Publication number
EP0884926B1
EP0884926B1 EP98401368A EP98401368A EP0884926B1 EP 0884926 B1 EP0884926 B1 EP 0884926B1 EP 98401368 A EP98401368 A EP 98401368A EP 98401368 A EP98401368 A EP 98401368A EP 0884926 B1 EP0884926 B1 EP 0884926B1
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Prior art keywords
signal
power spectral
spectral density
observation
disturbing
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French (fr)
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EP0884926A1 (de
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Pascal Scalart
André Gilloire
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Orange SA
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France Telecom SA
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R29/00—Monitoring arrangements; Testing arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R3/00—Circuits for transducers, loudspeakers or microphones

Definitions

  • the invention relates to a method and a device for optimized processing of a disturbing signal during a sound recording.
  • the disturbing signal itself can be considered as the sum of N elementary components and verifying the relation:
  • a common solution proposed for solving such a problem may consist in co-operating a number N of devices, each of them being optimized and dedicated to the reduction or even the local elimination of 'a given component p k (t) of the disturbing signal.
  • the disturbing signal p (t) can be considered as composed of an observation noise b (t), vehicle rolling noise, aerodynamic noise such as wind, air flow, as well as an acoustic echo signal z (t) from the coupling acoustic between speaker and plug microphone of his.
  • the disturbing signal p (t) can be considered as composed, not only of an observation noise b (t) and a acoustic echo signal z (t), but also of a signal r (t) generated by the reverberation effect of the room where sound recording is performed.
  • the solutions proposed, in such a context, can be classified into two main types, depending on whether the signal is considered to be essentially harmful echo and noise, or noise and reverberation.
  • the solutions adopted correspond to a cascade of elementary treatments, each of them being adapted to a particular component of the disturbing signal.
  • EP-A-0 767 569 describes an echo processing system acoustics by an adaptive filtering technique.
  • a special feature of this system lies in the use of filter control parameters, not adaptation and forgetting factor, variable over time and adapting automatically to the acoustic environment.
  • a posteriori optimization of these treatments may, where appropriate, be considered. Such a procedure inevitably implies, on the one hand, a permanent exchange information between these elementary treatments and, on the other hand, the application of concerted constraints on the adjustment parameters of these. Such a posterior optimization of such systems has shown the limitations of this approach because of the results ultimately obtained.
  • the object of the present invention is to remedy the shortcomings and disadvantages of processes, processes and prior art systems previously described.
  • the process of a priori optimization of treatment a disturbing signal during a sound recording, from an observation signal formed by an original useful signal and this disturbing signal is implemented thanks to a method and device consisting of, respectively allowing an estimation of the disturbing signal to generate an estimated disturbing signal.
  • An estimation useful signal to generate an estimated useful signal and a filtering of the observation signal from the disturbing signal estimated and optimal filtering allow minimization of the error between the useful signal and the estimated useful signal.
  • the estimated useful signal converges to the original useful signal for a substantially zero error between the useful signal and the estimated useful signal.
  • the method and the device, objects of the invention find application in any context relating to the taking of sound, including hands-free mobile telephony, hands-free video conferencing, and more generally studio or audio operations.
  • the signal above disturbance consists at least of a signal of noise, which, due to the very definition of a signal noise, is considered to be significantly decorrelated from the original useful signal that we wish to recover following attenuation, or even elimination, of this noise signal.
  • object of the present invention is made from a signal of observation, noted y (t), available in step 100 of origin in FIG. 2a, this observation signal being deemed to be formed from the original useful signal to be recovered, noted s (t) and the disturbing signal, denoted p (t).
  • the disturbing signal in addition to the aforementioned noise signal, may have different contributions such as a signal echo, reverberation signal, or any other form of noise signal, as will be described later in the description.
  • a signal echo e.g., a signal echo
  • reverberation signal e.g., a signal echo
  • any other form of noise signal e.g., a substantially decorrelated noise signal from the useful signal, as mentioned previously.
  • this consists in carrying out an estimation at step 101 of the disturbing signal to generate a signal estimated disruptor, noted p and (t).
  • a signal estimated disruptor noted p and (t).
  • the signal is available estimated disturbance p and (t), but also of the observation signal y (t) previously mentioned.
  • the optimized treatment method consists in perform, in a step 102, from the observation signal abovementioned y (t), a rough estimate of the useful signal, the useful signal estimated, by convention, being deemed, in reason for the actual decorrelation of the original wanted signal and of the noise signal, consist of the difference between the signal of observation y (t) and the estimated disturbing signal p and (t).
  • a step 102 from the observation signal abovementioned y (t), a rough estimate of the useful signal, the useful signal estimated, by convention, being deemed, in reason for the actual decorrelation of the original wanted signal and of the noise signal, consist of the difference between the signal of observation y (t) and the estimated disturbing signal p and (t).
  • an estimated useful signal is obtained, obtained following the rough estimation step, this useful signal estimated approximately corresponding to the useful signal of origin s (t) and for this reason noted s andu.
  • the method of optimized processing, object of the present invention then consists in performing a filtering 103 of the signal observation y (t) from the estimated disturbing signal p and (t) and optimal filtering to generate a signal useful, noted su.
  • the optimal filtering 103 then makes it possible to perform a minimization, in a step 104, of the error between the useful signal estimated su and the useful signal su.
  • the whole process achieved by steps 103 and 104 through steps 101 and 102 then make it possible to obtain convergence, thanks to optimal filtering, the estimated useful signal s andu and useful signal su to the original useful signal s (t) for a substantially zero error between the useful signal su and the estimated useful signal s andu.
  • the estimated useful signal s andu or the useful signal su is then substantially equal to the useful signal original s (t) to the nearest filtering errors.
  • FIG. 2a shows the method of optimized processing of a disturbing signal, in accordance with the object of the present invention, in the time domain. It is pointed out in particular that the notions of estimating the disturbing signal, rough estimation of the useful signal and optimal filtering can be perfectly defined in the time domain.
  • the process, object of the present invention can also, in a way particularly advantageous, be implemented when, at aforementioned observation signal corresponds to a disturbing signal p (t) to which is added, in addition to the noise signal substantially decorrelated from the original useful signal s (t), a signal echo, noted z (t).
  • This echo signal corresponds, in particular in hands-free mobile phone situations for example, to a disturbing signal generated by a signal of observation, noted x (t), under conditions which will be explained in more detail later in the description.
  • the estimation of the disturbing signal in step 101 advantageously consists in carrying out a separate estimate of the contribution 101b of this signal from reception and contribution 101a of the noise signal to this disturbing signal.
  • the process applied can then be substantially identical to that explained in connection with FIG. 2a.
  • the useful signal su resulting from optimal filtering in step 103 converges to the value of the estimated useful signal su and, in consequently, towards the value of the original useful signal s (t).
  • This preferred embodiment is particularly advantageous due in particular to the fact that in the part of an implementation by digital techniques of filtering in the frequency domain, it is not necessary to implement an echo canceller unlike to the techniques that have been described in relation to the prior art previously in the description.
  • the processing method optimized, object of the present invention in the field frequency, may consist in performing in step 100 a frequency transform of the observation signal y (t) at using a Fourier transform, such as a transform fast, noted FFT in the usual way, to allow to generate a transformed signal Y (f), this signal being representative, in the frequency domain, of the signal observation.
  • a Fourier transform such as a transform fast, noted FFT in the usual way
  • the aforementioned step 100 consists in carrying out an estimation from the transformed signal Y (f) of a signal representative of the power spectral density of the observation signal, this signal being denoted ⁇ and yy (f).
  • step 100 we thus have not only the transformed signal Y (f) representative of the frequency transform of the observation signal y (t), but also the signal representative of the estimated power spectral density. of this observation signal, signal noted ⁇ and ⁇ (f).
  • the step 102 of estimation of the useful signal can then be carried out directly on the spectral density of estimated power, on the one hand, of the observation signal ⁇ and yy (f) and, on the other hand, of the signal representative of the estimated power spectral density of the disturbing signal obtained at the end of step 101, noted ⁇ and pp (f).
  • the step 102 of rough estimation of the useful signal then amounts to carrying out an a posteriori estimation of the power spectral density of the useful signal, which, for this reason is noted ⁇ and ss (f).
  • the signal representative of the estimated power spectral density of the aforementioned useful signal is then available.
  • the optimal filtering step 103 is carried out on the signal representative of the frequency transform of the observation signal Y (f) from signals representative of the estimated power spectral density of the disturbing signal ⁇ and pp (f) and of the signal representative of the estimated power spectral density of the useful signal, denoted ⁇ and ss (f ), available at the end of step 102 above.
  • the optimal filtering step 103 and the step of calculating an error and minimizing this error 104 can be carried out by means of the same global filtering step, noted for this reason 103 + 104 on the FIG.
  • the processing in the frequency domain in particular the digital processing allowing, thanks to the implementation of a single optimal filter, the optimization of the useful signal, the error signal between the useful signal and the useful signal estimated, or more exactly between the estimated power spectral densities of these signals, being directly available due to the optimal filtering performed.
  • the global filtering is represented in dotted lines as the meeting of steps 103 and 104 in FIG. 2c.
  • the process which is the subject of the present invention consists in performing a frequency transform of the observation signal, in step 100a, transformed denoted FFT, to generate the representative transformed signal in the frequency domain of the observation signal Y (f) as well as a frequency transform of the reception signal, in step 100b, to generate a transformed signal representative of the reception signal and denoted X (f).
  • an estimation step is carried out in steps 100a and 100b, this estimation step consisting in obtaining, from each transformed signal Y (f) and X (f) previously cited, to obtain a signal representative of the estimated power spectral density of the observation signal, noted for this reason ⁇ and yy (f), respectively of the reception signal, noted for this reason ⁇ and xx (f).
  • the density estimate power spectral of the observation signal, of the signal reception, the echo signal can be implemented at recursive filtering based on a forgetting factor, as will be described later in the description.
  • the estimation of the power spectral density of the disturbing signal carried out in step 101 consists in performing the step of estimating the power spectral density of the disturbing signal ⁇ and pp (f) on the signal representative of the spectral density.
  • power of the observation signal ⁇ and yy (f) available at the end of step 100a respectively on the signal representative of the power spectral density of the reception signal ⁇ and xx (f) available at the end of step 100b.
  • the density estimated power spectral resulting from the disturbing signal is deemed to consist of the sum of the estimated power spectral densities ⁇ and ppy (f) and ⁇ and ppx (f).
  • step 102 as represented in FIG. 2d also consists in carrying out an estimation of the spectral density of the useful signal ⁇ and ss (f) then deemed to be equal. unlike the estimated spectral densities of the observation signal ⁇ and yy (f) and the disturbing signal ⁇ and pp (f).
  • the signals of estimated spectral density of the useful signal ⁇ and ss (f) available in step 102 and of the disturbing signal ⁇ and pp (f) then make it possible to ensuring the optimal filtering in step 103 and, more generally, the global filtering 103 + 104 on the signal Y (f) representative in the frequency domain of the observation signal.
  • the minimization criterion can consist in minimizing the mean square error of estimation according to the relation (1): E [(su- s u) 2 ]
  • the above relation (1) can be used, either for processing in the time domain, i.e. for frequency domain treatment.
  • the estimated power spectral density of the useful signal ⁇ and ss (f) is not known a priori.
  • the optimized signal processing process disruptive in accordance with the object of the present invention, thus reduces to the implementation of a single filtering optimal, which allows to reduce overall all the components constituting the disturbing signal.
  • the signal disruptive may consist of a plurality of components provided there is sufficient decorrelation between the useful signal and the disturbing signal, i.e. each components constituting the latter. This assumption is widely verified in various applications linked by example of hands-free telephony in vehicles cars, or hands-free video conferencing, and, more generally, to all types of applications in which a plurality of components of a signal disruptive can be highlighted.
  • the estimated power spectral density of the disturbing signal ⁇ and pp (f) is then taken equal to the sum of the spectral densities of estimated power ⁇ and i pp (f) of each component of rank i of this disturbing signal.
  • the signal representative of the estimated power spectral density of the disturbing signal checks the relation (6): In this relation, P represents the number of components of the disturbing signal.
  • observation signal y (t) available is of course sampled at a sampling frequency adequate, successive samples being subdivided in sample blocks.
  • m denoting in fact the rank of the current block subjected to the treatment.
  • the technique of building up sample blocks is a classic technique, successive blocks samples that may be subject to some recovery typically equal to 50% in number of constituent samples of each block.
  • treatment with blocks is deemed to be carried out in the most general manner when the disturbing signal takes into account, not only the contribution of a noise signal, but also that generated by a reception signal x (t).
  • step 100a in addition to the subdivision of the observation signal into successive blocks of rank m, each block of samples being denoted Bm (t) is of course subjected to a frequency transformation FFT allowing d '' obtain blocks of samples in the frequency domain denoted Bm (f).
  • Step 100a also consists in carrying out an estimation of the power spectral density of the observation signal on the current block, the signal of the estimated power spectral density of the observation signal being noted ⁇ and yy (f, m) where m naturally denotes the index relating to the current block.
  • step 100a in fact, not only is the signal representative of the estimated power spectral density of the aforementioned observation signal ⁇ and yy (f, m), but also the representative block Bm (f) of the observation signal for the current block of rank m considered.
  • step 100b for which, by analogy with FIG. 2d, a corresponding processing is applied to the reception signal x (t), this processing then consisting of a subdivision into corresponding blocks of rank m, each block being denoted B'm (t), each aforementioned block being subjected to a frequency transformation, denoted FFT, this operation making it possible to obtain blocks representative of the blocks of samples in the frequency space and denoted for this reason B'm (f).
  • Step 100b shown in FIG. 2e also includes an operation of estimating the power spectral density of the reception signal on the current block B'm (f). At the end of step 100b in FIG. 2e, there is each current block B'm (f) representative of the sample block in the frequency domain and a signal representative of the estimated power spectral density of the reception signal for the aforementioned current block, this signal being noted ⁇ and xx (f, m).
  • the optimized processing method in accordance with the object of the present invention, then consists, in step 101, in carrying out an estimation of the power spectral density of each component of the signal disruptor previously cited ⁇ and i pp (f, m).
  • the signal representative of the power spectral density of each component of the disturbing signal ⁇ and i pp (f, m) is in fact constituted at least by the signal representative of the estimated power spectral density ⁇ and ppy ( f, m) representative of the contribution of the noise signal to the disturbing signal and by the signal representative of the estimated power spectral density of the contribution of the reception signal to this disturbing signal ⁇ and ppx (f, m).
  • the estimation of the power spectral density of each component of the disturbing signal ⁇ and i pp (f, m) is thus carried out from the reception signal and, more particularly, from the estimated power spectral density of the reception signal ⁇ and xx (f, m) and of the current block B'm (f), of the estimation of the power spectral density of the observation signal on the current block Bm (f) of the observation signal of the same rank m.
  • step 101 there is in fact, for the current block of rank m of the observation signal and of the reception signal, the estimated power spectral density of the observation signal on this block current noted ⁇ and yy (f, m) and, of course, an estimate of the power spectral density of the disturbing signal ⁇ and pp (f, m), which of course checks the previous relation (6).
  • the power spectral density of the useful signal is then estimated on the current block by a so-called posterior estimation.
  • the signal representative of the estimated power spectral density of the useful signal then checks the relation (7):
  • the posterior estimation operation 102a is then followed by a step 102b of a priori estimation of the amplitude of the spectrum of the useful signal on the current block.
  • AT ss (f, m) T (f, m).
  • the signal Y (f, m) can be obtained from the current block Bm (t) and application of a simple short-term Fourier transform on this block current to obtain the signal Y (f, m).
  • step 102b is then followed by the estimation of the power spectral density of the useful signal in step 102c shown in FIG. 2e.
  • Step 102c of estimating the power spectral density of the useful signal is carried out thanks to the implementation of a step 102d making it possible to generate, for each current block Bm (f), a weighting parameter ⁇ (m ) allowing a suitable weight to be assigned between the current estimate made from the filtering applied to the previous block of rank m-1 and the contribution for the current frame of the estimated power spectral density of the useful signal, which is of course represented by the signal ⁇ and ss-post (f, m).
  • step 102 there is of course the signal representative of the estimated power spectral density of the useful signal, noted ⁇ and ss (f, m).
  • the optimal filtering process can then be controlled for the current block on the signal Y (f, m) thanks to the global filtering previously described in relation to FIG. 2d in steps 103 and 104.
  • the disturbing signal is considered to be consisting of a noise and an echo generated by a signal reception.
  • the observation signal is noted y (t) and is considered supplied by an M microphone
  • the reception signal denoted x (t) corresponds to that of the signal delivered to a loudspeaker HP in the context of mobile radiotelephony hands free for example.
  • the loudspeaker and the microphone M being necessarily close to each other, the contribution to the signal disturbing the reception signal cannot in any case be neglected, while of course other components such as engine noise from the vehicle, traffic noise generated by traffic neighbor for example constitute as many components and contributions to the disturbing signal.
  • the observation signal y (t) delivered by the microphone M is subjected by means of a module, denoted T 1 (f, m), T 1 (f) , to digital sampling at an appropriate frequency, to a subdivision by blocks and of course to a frequency transform, denoted FFT in FIG. 3a.
  • the module T 1 (f, m) then delivers the signal Y (f, m) representative in the frequency domain of the observation signal on the block of rank m considered.
  • reception signal via a module T 2 (f, m), T 2 (f), which makes it possible to deliver the representative signal in the frequency domain X (f, m) and the blocks B'm (f) representative of the reception signal for the block of rank m considered.
  • the modules T 1 (f, m) and T 2 (f, m) are modules of the conventional type, identical, synchronized by the same clock signal, not shown. As such, these modules will not be described in detail since they correspond to modules normally used in the corresponding technical field and, as such, perfectly known to those skilled in the art.
  • the optimized processing device, object of the present invention comprises a module 1.1 m for estimating the power spectral density of the observation signal delivering, at from this observation signal, or, more precisely, from the signal representative in the frequency domain of this observation signal, that is to say either the signal Y (f), or the signal Y (f, m), a digital signal representative of the estimated power spectral density of the observation signal and for this reason noted, for the same reason, ⁇ and yy (f), respectively ⁇ and yy (f, m) on the current block m considered.
  • the device according to the invention comprises a module 2.2 m for estimating the power spectral density of the disturbing signal receiving the reception signal, or, more precisely, the signal representative in the frequency domain of this reception signal, that is to say either the signal X (f, m), or the signal X (f).
  • the module 2 for estimating the power spectral density of the disturbing signal also receives the digital signal representative of the estimated power spectral density of the observation signal, that is to say the signal ⁇ and yy (f), respectively ⁇ and yy (f, m). It therefore delivers a digital signal representative of the estimated power spectral density of the disturbing signal, designated by ⁇ and pp (f).
  • the module 2.2 m in fact delivers all the signals representative of the estimated spectral power density of the components of the disturbing signal designated by ⁇ and i pp (f), respectively ⁇ and i pp (f, m).
  • a 3.3 m module for estimating the power spectral density of the useful signal which receives the digital signal representative of the estimated power spectral density of the observation signal ⁇ and yy (f), respectively ⁇ and yy (f, m) delivered by the module 1.1 m as well as the digital signal representative of the estimated power spectral density of the disturbing signal ⁇ and pp (f), respectively ⁇ and pp (f, m) or the components of the latter, as previously mentioned.
  • the 3.3 m module for estimating the spectral power density of the useful signal delivers by a process inspired by the general principle of spectral subtraction a digital signal, noted ⁇ and ss (f), respectively ⁇ and ss (f, m ) representative of the estimated power spectral density of the aforementioned useful signal.
  • the device for optimized processing of a disturbing signal object of the present invention, as shown in FIG. 3a, comprises a global filtering module, noted 4.4 m , making it possible to ensure optimal filtering of the representative signal in the frequency domain of the observation signal, that is to say the signal Y (f) respectively Y (f, m) delivered by the module T 1 (f, m), T 1 (f).
  • the 4.4 m filter module advantageously comprises a calculation module, denoted 4a, 4a m , of the coefficients of an optimal filter receiving the digital signal representative of the spectral density of estimated power of the disturbing signal ⁇ and pp (f), respectively ⁇ and pp (f, m), as well as the digital signal representative of the estimated power spectral density of the useful signal ⁇ and ss (f), respectively ⁇ and ss ( f, m).
  • the module 4a, 4a m shown in FIG. 3a delivers a digital filtering adaptation signal, denoted af , representative of an optimal filtering frequency response, verifying the relation (4) previously given in the description.
  • the estimated power spectral density of the disturbing signal corresponds to the sum of the spectral densities of the components of the disturbing signal according to relation (6) previously given in the description.
  • a module 4b, 4b m receives the signal representative of the frequency response, that is to say the signal af delivered by the module 4a, 4a m , to deliver , from the representative signal in the frequency domain of the observation signal, the useful signal su.
  • the optimal filter module 4b, 4b m can consist, for example, of a Wiener filter module.
  • the signal delivered by this filter module 4b, 4b m is then received by a reverse frequency transform module, for this reason noted FFT -1 , and by block synthesis, bearing the reference 5.5 m , which delivers, from of the optimal filtering signal, the useful signal proper su (t) reconstituted in the time domain.
  • FIG. 3a A more detailed description of a preferred embodiment of the 3 m module shown in FIG. 3a for estimating the power spectral density of the useful signal corresponding to the mode of implementation of the method, object of the present invention, as shown in FIG. 2e, will now be given in conjunction with FIG. 3b for processing by blocks of successive rank m.
  • the device which is the subject of the present invention comprises, in addition to the module T 1 (f, m) delivering a succession of successive current blocks of rank m, the module of estimation of the power spectral density of the observation signal on the current block ⁇ and yy (f, m), module 1 m , and the module of estimation of the power spectral density of each component of the disturbing signal ⁇ and 1 pp (f, m), module 2 m , the module for block estimation of the spectral power density of the useful signal, module 3 m , which advantageously comprises, as shown in FIG.
  • a module 30 m for estimation a posteriori of the power spectral density of the useful signal on the current block, noted ⁇ and ss-post (f, m) verifying the relation (7) previously mentioned in the description.
  • the 3 m module also includes a 31 m module for a priori estimation of the amplitude of the spectrum of the useful signal on the current block, verifying the relation (9) previously mentioned in the description.
  • the 31 m module receives, on the one hand, the signal ⁇ and ss-post (f, m) delivered by the module 30 m as well, on the other hand, the signal Y (f, m) delivered by the block T 1 (f, m), as well as a signal representative of the frequency response of the optimal filtering for the block preceding the current block, ie T (f, m-1) delivered for example by the block 4a m of FIG. 3a .
  • the block 31 m then delivers an a priori estimate of the amplitude of the spectrum of the useful signal denoted A ss (f, m).
  • a module for calculating the power spectral density of the useful signal, for the current block, module 32 m which receives the a priori estimation signal of the amplitude of the spectrum of the useful signal spectrum A ss (f , m) delivered by the module 31 m as well as a signal representative of a weighting coefficient or parameter ⁇ (m) from a module 33 m shown in FIG. 3b.
  • the parameter ⁇ (m) makes it possible to assign a suitable weight between the estimate made at the previous block of rank m-1 and the contribution for the current frame of the power spectral density of the useful signal, as mentioned previously in the description .
  • the parameter ⁇ (m) can be adjusted according to the characteristics of the useful signals and of the estimated noise.
  • the module 32 m then delivers the signal representative of the estimated power spectral density of the useful signal, verifying the relation (10) previously mentioned in the description.
  • an estimate of the power spectral density of single noise can be obtained in particular in the absence of an echo signal and useful signal.
  • this estimate may involve a estimation of the transfer function of the acoustic channel between the reception signal and the observation signal.
  • the device in such a case comprises, associated with the module 1.1 m for estimating the power spectral density of the observation signal, an additional module for estimating the power spectral density of the noise affecting this observation signal.
  • the module 2.2 m for estimating the power spectral density of the disturbing signal constitutes in fact a module for estimating the power spectral density of the acoustic echo, which delivers a signal representative of the estimated power spectral density of the acoustic echo, noted ⁇ and zz (f, m).
  • the module for calculating the coefficients of the optimal filter 4a, 4a m directly receives the signal representative of the estimated power spectral density of the acoustic echo ⁇ and zz (f, m) , the signal representative of the estimated power spectral density of the noise, denoted ⁇ and bb (f, m) and, of course, the signal representative of the estimated power spectral density of the observation signal, denoted ⁇ and yy (f , m).
  • the estimation module 1a, 1a m of the spectral density of the noise signal may advantageously include, as shown in FIG. 3d, a module for detecting the absence of useful signal and the absence of echo signal in the observation signal, and a first-order recursive filter having a factor of forgetting ⁇ bb , this forgetting factor consisting of a real coefficient between the value 0 and 1.
  • b (f, m) designates the frequency transform, Fourier transform, of the observation signal established over a current time segment of the observation signal in the absence of vocal activity , that is to say speech of one or the other of the two speakers in communication.
  • the estimation module 1 am in its version relating to block processing, described in a nonlimiting manner, includes the voice activity detection module 10 am receiving for example the signal Y (f, m) delivered by the module T 1 (f, m), a switch controlled 11 am by the voice activity detector module 10 am , a square elevation module 12 am , a multiplier circuit 13 am receiving the signal delivered by the square elevation module 12 am and the value 1- ⁇ bb .
  • a summer 14 am receives the signal delivered by the module 12 am , delivers the signal representative of the estimated power spectral density of the noise signal ⁇ and bb (f, m) and receives by a feedback loop the signal representative of the density estimated power spectral of the noise signal ⁇ and bb (f, m-1) relating to the block preceding the current block via a 15 am delay module, memory for example, and a weighting multiplier module 16 am receiving the value ⁇ bb .
  • the block B m (f) delivered by the module T 1 (f, m) corresponds to the frequency transform b (f, m) of the noise signal.
  • the module for estimating the power spectral density of the observation signal in particular the 1.1 m module, it is indicated that this can include, as shown in FIG. 3e, a filter first-order recursive, having a forgetting factor ⁇ yy consisting of a real coefficient between 0 and 1.
  • 2 In this relation, Y (f), respectively Y (f, m), designates the signal representative in the frequency domain of the observation signal, i.e. the frequency transform of this observation signal on the block current for example.
  • the recursive filter represented in FIG. 3e comprises elements similar to those represented in FIG. 3d, the notations am being modified in m respectively, the value ⁇ yy being adapted accordingly.
  • Figures 4a to 4e make it possible to evaluate the performances obtained thanks to the implementation of the process for processing an optimized disturbing signal and using of a device, in accordance with the object of the present invention, as shown for example in Figure 3c.
  • the x-axis is graduated in seconds and the ordinate axis in value amplitude in PCM digital coding, 16-bit coding corresponding to a maximum value of 32,768.
  • the application context related to radiotelephony hands-free in a motor vehicle is related to radiotelephony hands-free.
  • the signal sampling frequency was at a value of 8 kHz, the digital coding of the samples thus obtained being based on the PCM format, ie 16 bits linear.
  • the signal broadcast on the speaker, receive signal, and microphone signal i.e. the observation signal, have been recorded synchronously, the vehicle engine being stopped.
  • noise and local speech recorded separately in a same vehicle were artificially summoned to the signal echo.
  • the original echo signal, picked up by the microphone M, is shown in Figure 4a.
  • the noisy observation signal obtained as well as previously mentioned, is shown in Figure 4b, when the local voice, that is to say the speaker of the vehicle, was artificially disturbed by a noise signal and an echo signal corresponding to a human voice.
  • the signal represented by slots under the above records represents the voice activity detection in reception, i.e. on the reception signal received by the speaker HP.
  • the test observation signal represented in figure 4b thus comprises periods of noise alone, periods echo alone in the noise, but also periods of double talk, periods during which the two speakers in correspondence speak at the same time.
  • the signal from test corresponds to a typical case in a mobile radio context hands free.
  • Average echo signal ratio (dB) 9.00 Signal to maximum echo ratio (dB) 38.61 Signal to minimum echo ratio (dB) -23.66 Standard deviation of signal to echo ratio (dB) 5.31 Average signal-to-noise ratio (dB) 6.17 Maximum signal-to-noise ratio (dB) 19.18 Minimum signal-to-noise ratio (dB) -27.38 Signal-to-noise ratio standard deviation (dB) 5.21
  • FIG. 4c represents the useful signal obtained in output of the device, the signal su of FIG. 3c.
  • the echo attenuation is evaluated by an energy measurement, known as ERLE, for Echo Return Loss Enhancement, this measurement being evaluated on blocks of 256 samples in the absence of recovery.
  • ERLE Echo Return Loss Enhancement
  • noise attenuation is assessed on blocks of 256 samples without overlap.
  • Figures 4d and 4e show that the process and the optimized treatment device, object of the present invention, reduce power average of the acoustic echo picked up by the microphone M, of around 15 dB during the single echo periods and around 10 dB during periods of double talk.
  • this reduction is around 18 dB during the noise period alone.
  • the optimized overall processing automatically adapts to the observation signal delivered by the microphone M. Indeed, we can then observe a 15 dB reduction in noise power during periods echo alone and 8 dB during periods of double talk.
  • the optimized treatment method and device disturbing signals, objects of the present invention appear very advantageous insofar as they allow to reduce the distortions introduced on the signal useful local speaking.
  • reducing the attenuation to the echo signal and noise signal during periods of vocal activity in transmission does not introduce undesirable effects on the signal transmitted to the correspondent distant because the echo signal and the noise signal remaining at the end of treatment are then subjectively masked by the local speech signal.
  • the method and the device, objects of the present invention are particularly well suited to radiotelephony hands-free mobile in motor vehicles. Indeed, while some European countries have already taken measures to ban the use of a handset classic cell phone while driving a motor vehicle, we should expect a generalization such measures. Analysis of hands-free telephony in vehicles helped to highlight the two main factors discomfort to the driver, corresponding not only to the simultaneous communication, but also ambient noise level, whereas for the correspondent of the latter, the most important genes are caused by the presence of noise and an acoustic echo, induced by the acoustic coupling existing between transducers.

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Acoustics & Sound (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Otolaryngology (AREA)
  • Noise Elimination (AREA)
  • Cable Transmission Systems, Equalization Of Radio And Reduction Of Echo (AREA)
  • Telephone Function (AREA)
  • Filters That Use Time-Delay Elements (AREA)

Claims (12)

  1. Verfahren zur optimierten Behandlung eines Störsignals bestehend aus mindestens einem Rauschsignal während einer Tonaufnahme, anhand eines Beobachtungssignals, das aus einem ursprünglichen Nutzsignal und dem Störsignal gebildet ist, dadurch gekennzeichnet, dass es besteht aus der Vornahme
    einer Schätzung des Störsignals zur Erzeugung eines geschätzten Störsignals;
    einer Schätzung des Nutzsignals zur Erzeugung eines geschätzten Nutzsignals durch Subtraktion des geschätzten Störsignals von dem Beobachtungssignal;
    einer Filterung des Beobachtungssignals anhand des geschätzten Störsignals und einer optimalen Filterung zur Erzeugung eines Nutzsignals, wobei die optimale Filterung eine Minimalisierung des Fehlers zwischen dem Nutzsignal und dem geschätzten Nutzsignal erlaubt, wobei das geschätzte Nutzsignal gegen das ursprüngliche Nutzsignal konvergiert, damit ein Fehler zwischen dem Nutzsignal und dem geschätzten Nutzsignal nahezu null beträgt.
  2. Verfahren nach Anspruch 1, dadurch gekennzeichnet, dass wenn die Tonaufnahme in Gegenwart eines Empfangssignals erfolgt, die Schätzung des Störsignals in der Vornahme einer Schätzung besteht, die getrennt ist von dem Beitrag des Empfangssignals und dem Beitrag des Rauschsignals des Störsignals.
  3. Verfahren nach Anspruch 1 oder 2, dadurch gekennzeichnet, dass es zur Behandlung des Störsignals im Frequenzbereich besteht aus
    der Vornahme einer Frequenzumwandlung des Beobachtungssignals bzw. des Empfangssignals zur Erzeugung im Frequenzbereich eines repräsentativen umgewandelten Signals des Beobachtungssignals bzw. Empfangssignals;
    der Schätzung, ausgehend von jedem umgewandelten Signal, eines repräsentativen Signals der spektralen Leistungsdichte des Beobachtungssignals bzw. des Empfangssignals;
    der Anwendung des Schritts der Schätzung des Störsignals auf das repräsentative Signal der spektralen Leistungsdichte des Beobachtungssignals bzw. auf das repräsentative Signal der spektralen Leistungsdichte des Empfangssignals;
    der Anwendung der optimalen Filterung auf das repräsentative umgewandelte Signal des Beobachtungssignals zur Erzeugung eines repräsentativen umgewandelten Signals des Nutzsignals.
  4. Verfahren nach Anspruch 3, dadurch gekennzeichnet, dass die optimale Filterung anhand eines repräsentativen Signals der geschätzten spektralen Leistungsdichte des Nutzsignals erfolgt, welche durch ein Spektralsubstraktionsverfahren bestimmt wurde und folgende Gleichung erfüllt: γ ss (f)=γ γγ (f)-γ pp (f) wobei
    γ γγ(f)
    die geschätzte spektrale Leistungsdichte des Beobachtungssignals bezeichnet;
    γ pp (f)
    die geschätzte spektrale Leistungsdichte des Störsignals bezeichnet.
  5. Verfahren nach Anspruch 3 oder 4, dadurch gekennzeichnet, dass für ein Störsignal, das aus mehreren Bestandteilen des Störsignals besteht, die geschätzte spektrale Leistungsdichte des Störsignals γ pp (f) gleichgesetzt wird mit der Summe γ i pp (f) der geschätzten spektralen Leistungsdichten jedes Bestandteils des Rangs i des Störsignals und die folgende Gleichung erfüllt:
    Figure 00470001
    wobei P die Anzahl der Bestandteile des Störsignals darstellt.
  6. Verfahren nach Anspruch 4 oder 5, dadurch gekennzeichnet, dass für eine blockweise Behandlung im Frequenzbereich des Beobachtungssignals, wobei dieses Signal in aufeinanderfolgende Abtastblöcke unterteilt ist, das Verfahren für jeden laufenden Block des Rangs m im Hinblick auf die Bildung der geschätzten spektralen Leistungsdichte des Nutzsignals besteht aus der Vornahme
    einer Schätzung der spektralen Leistungsdichte des Beobachtungssignals für den laufenden Block γ γγ(f,m);
    einer Schätzung der spektralen Leistungsdichte jedes Bestandteils des Störsignals γ i pp (f,m), anhand des Empfangssignals, des laufenden Blocks des Rangs m des Beobachtungssignals und der Schätzung der spektralen Leistungsdichte des Beobachtungssignals für den laufenden Block γ γγ(f,m);
    einer a posteriori-Schätzung der spektralen Leistungsdichte des Nutzsignals für den laufenden Block,
    wobei γ ss-post (f,m) die Gleichung erfüllt:
    Figure 00470002
    einer a priori-Schätzung der Spektralamplitude des Nutzsignals für den laufenden Block, die die Gleichung erfüllt: Ass (f,m)=T(f,m-1) . Y(f,m)
    wobei
    T(f,m-1)
    die frequenzabhängige Antwort der auf den vorhergehenden Block angewendeten optimalen Filterung bezeichnet und
    Y(f,m)
    die kurzfristige Fourier-Transformation, für den laufenden Block, des Beobachtungssignals bezeichnet, wobei die geschätzte spektrale Leistungsdichte des Nutzsignals für den laufenden Block die Gleichung erfüllt:
    γ ss (f,m)= β(m)|Ass (f,m)|2 + (1- β(m))γ ss-post (f,m) wobei in dieser Gleichung ß(m) für den laufenden Block einen Gewichtungsparameter bezeichnet, der es erlaubt, eine angepasste Gewichtung durchzuführen zwischen der laufenden Schätzung, durchgeführt anhand der auf den vorhergehenden Block des Rangs m-1 angewendeten Filterung, und dem Beitrag für den laufenden Rahmen der spektralen Leistungsdichte des Nutzsignals.
  7. Vorrichtung zur optimierten Behandlung eines Störsignals während einer Tonaufnahme, anhand eines Beobachtungssignals, das aus einem Nutzsignal und dem Störsignal gebildet ist, wobei das Störsignal aus einem Rauschen und einem von einem Empfangssignal erzeugten Echo besteht, dadurch gekennzeichnet, dass die Vorrichtung für eine Behandlung im Frequenzbereich dieser Signale mindestens umfasst:
    Mittel zur Schätzung der spektralen Leistungsdichte des Beobachtungssignal, welche anhand des Beobachtungssignals ein digitales Signal abgeben, das repräsentativ ist für die geschätzte spektrale Leistungsdichte des Beobachtungssignals γ γγ(f);
    Mittel zur Schätzung der spektralen Leistungsdichte des Störsignals, welche das Empfangssignal und das digitale Signal empfangen, das repräsentativ ist für die geschätzte spektrale Leistungsdichte des Beobachtungssignals γ γγ(f) und ein digitales Signal abgeben, das repräsentativ ist für die geschätzte spektrale Leistungsdichte des Störsignals γ pp (f) ;
    Mittel zur Schätzung der spektralen Leistungsdichte des Nutzsignals, welche das digitale Signal empfangen, das repräsentativ ist für die geschätzte spektrale Leistungsdichte des Beobachtungssignals γ γγ(f), sowie das digitale Signal, das repräsentativ ist für die geschätzte spektrale Leistungsdichte des Störsignals γ pp (f), und welche mittels Spektralsubtraktion ein digitales Signal abgeben, das repräsentativ ist für die geschätzte spektrale Leistungsdichte des Nutzsignals γ ss (f);
    Mittel zur Berechnung der Koeffizienten eines optimalen Filters, welche das digitale Signal empfangen, das repräsentativ ist für die geschätzte spektrale Leistungsdichte des Störsignals γ pp (f), sowie das digitale Signal, das repräsentativ ist für die geschätzte spektrale Leistungsdichte des Nutzsignals γ ss (f), und welche ein digitales Filterungsanpassungssignal abgeben, das repräsentativ ist für eine frequenzabhängige Filterungsantwort der Form: T(f) = γ ss(f) γ ss(f)+γpp(f)
    Mittel zur optimalen Filterung, welche das Beobachtungssignal und das digitale Filterungsanpassungssignal empfangen und das geschätzte Nutzsignal abgeben, welches repräsentativ ist für das Nutzsignal.
  8. Vorrichtung nach Anspruch 7, dadurch gekennzeichnet, dass für ein Störsignal, das aus mehreren Bestandteilen des Störsignals besteht, die Mittel zur Schätzung der spektralen Leistungsdichte des Nutzsignals das digitale Signal empfangen, welches repräsentativ ist für die geschätzte spektrale Leistungsdichte des Beobachtungssignals γ γγ(f), sowie das digitale Signal, welches repräsentativ ist für die geschätzte spektrale Leistungsdichte γ i pp (f) der verschiedenen Bestandteile des Störsignals, und ein digitales Signal abgeben, welches repräsentativ ist für die geschätzte spektrale Leistungsdichte des Nutzsignals γ ss (f).
  9. Vorrichtung nach Anspruch 8, dadurch gekennzeichnet, dass für eine blockweise Behandlung im Frequenzbereich des Beobachtungssignals die Vorrichtung umfasst:
    Mittel zur Unterteilung des Beobachtungssignals in aufeinanderfolgende Blöcke, welche das Beobachtungssignal empfangen und eine Abfolge von aufeinanderfolgenden, laufenden Blöcken des Rangs m abgeben;
    Mittel zur Schätzung der spektralen Leistungsdichte des Beobachtungssignals für den laufenden Block γ yy (f,m),
    Mittel zur Schätzung der spektralen Leistungsdichte jedes Bestandteils des Störsignals γ i pp (f,m) anhand des Empfangssignals, des laufenden Blocks des Rangs m des Beobachtungssignals und der Schätzung der spektralen Leistungsdichte des Beobachtungssignals für den laufenden Block γ yy (f,m);
    Mittel zur blockweisen Schätzung der spektralen Leistungsdichte des Nutzsignals, welche umfassen:
    Mittel zur a posteriori-Schätzung der spektralen Leistungsdichte des Nutzsignals für den laufenden Block,
    wobei γ ss - post (f,m) die Gleichung erfüllt:
    Figure 00500001
    Mittel zur a priori-Schätzung der Spektralamplitude des Nutzsignals für den laufenden Block, welche die Gleichung erfüllen: Ass(f,m) = T(f,m-1) . Y(f,m)
    wobei
    T(f,m-1)
    die frequenzabhängige Antwort der auf den vorhergehenden Block angewandten optimalen Filterung bezeichnet;
    Y(f,m)
    die kurzfristige Fourier-Transformation, für den laufenden Block, des Beobachtungssignals bezeichnet, wobei die geschätzte spektrale Leistungsdichte des Nutzsignals für den laufenden Block die Gleichung erfüllt:
    y ss (f,m) = β(m)|Ass (f,m)|2 + (1-β(m)) γ ss-post (f,m) in der ß(m) für den laufenden Block einen Gewichtungsparameter bezeichnet, der es erlaubt, eine angepasste Gewichtung durchzuführen zwischen der laufenden Schätzung, durchgeführt anhand der auf den vorhergehenden Block des Rangs m-1 angewendeten Filterung, und dem Beitrag für den laufenden Rahmen der spektralen Leistungsdichte des Nutzsignals.
  10. Vorrichtung nach Anspruch 7, dadurch gekennzeichnet, dass für ein Störsignal, das aus einem Echosignal des Empfangssignals und einem Rauschsignal gebildet ist, wobei das Rauschsignal nahezu entkoppelt ist von dem Echosignal und die Mittel zur Schätzung der spektralen Leistungsdichte des Echosignals ein digitales Signal abgeben, das repräsentativ ist für die geschätzte spektrale Leistungsdichte des Echosignals γ zz (f), die Vorrichtung ferner Mittel zur Schätzung der spektralen Leistungsdichte des Rauschsignals umfasst, welche ein digitales Signal an die Mittel zur Berechnung der Koeffizienten eines optimalen Filters abgeben, das repräsentativ ist für die geschätzte spektrale Leistungsdichte des Rauschsignals γ bb (f), wobei die Berechnungsmittel ein digitales Filterungsanpassungssignal abgeben, das repräsentativ ist für eine frequenzabhängige Filterungsantwort der Form: T(f) = γ ss(f) γ ss(f)+ γ bb(f)+ γ zz(f) mit γ ss (f) = γ yy (f) - γ bb (f) - γ zz (f) .
  11. Vorrichtung nach Anspruch 7, 9 und 10, dadurch gekennzeichnet, dass die Mittel zur Schätzung der spektralen Leistungsdichte des Rauschsignals aufweisen:
    ein Mittel zur Erfassung der Abwesenheit des Nutzsignals und der Abwesenheit des Echosignals in dem Beobachtungssignal;
    - einen rekursiven Filter der ersten Ordnung, der ein Vergessensmoment λbb aufweist, welches ein realer Koeffizient zwischen 0 und 1 ist, wobei der rekursive Filter das digitale Signal abgibt, welches repräsentativ ist für die geschätzte spektrale Leistungsdichte des Rauschsignals γ bb (f) der Form:
    γ bb (f,m) = λ bb .γ bb (f,m-1) + (1-λbb) (|b(f,m)|2) in der b(f,m) die Fourier-Transformation des Beobachtungssignals bezeichnet, die für ein laufendes Zeitsegment des Beobachtungssignals in Abwesenheit einer Sprechaktivität erstellt wird.
  12. Vorrichtung nach einem der Ansprüche 7 bis 11, dadurch gekennzeichnet, dass die Mittel zur Schätzung der spektralen Leistungsdichte des Beobachtungssignals aufweisen:
    einen rekursiven Filter der ersten Ordnung, der ein Vergessensmoment λ yy aufweist, welches ein realer Koeffizient zwischen 0 und 1 ist, wobei der rekursive Filter das digitale Signal abgibt, welches repräsentativ ist für die geschätzte spektrale Leistungsdichte des Beobachtungssignals γ yy (f)der Form: γ yy (f)= λyy.γ yy(f)+ (1- λyy) . |Y(f)|2
    wobei Y(f) die Fournier-Transformation des laufenden Zeitsegments des Beobachtungssignals darstellt.
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