EP0993671B1 - Verfahren zur bestimmung eines rauschmodells in einem gestörten audiosignal - Google Patents

Verfahren zur bestimmung eines rauschmodells in einem gestörten audiosignal Download PDF

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Publication number
EP0993671B1
EP0993671B1 EP98935094A EP98935094A EP0993671B1 EP 0993671 B1 EP0993671 B1 EP 0993671B1 EP 98935094 A EP98935094 A EP 98935094A EP 98935094 A EP98935094 A EP 98935094A EP 0993671 B1 EP0993671 B1 EP 0993671B1
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model
noise
frames
energy
search
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French (fr)
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EP0993671A1 (de
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Dominique Thomson-CSF PASTOR
Gérard Thomson-CSF REYNAUD
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Thales Avionics SAS
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Thales Avionics SAS
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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
    • 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
    • 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
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L2021/02168—Noise filtering characterised by the method used for estimating noise the estimation exclusively taking place during speech pauses

Definitions

  • the invention relates to improving the intelligibility of voice communications in the presence of noise. It no longer applies especially but not exclusively to telephone communications or by radiotelephone or other electronic means, at the speech recognition, etc., whenever the recording environment sound is noisy and may deteriorate the perception or recognition of the transmitted voice.
  • noise comes from engines, air conditioning, ventilation of on-board equipment, aerodynamic noise. These noises are picked up by the microphone in which the pilot or a member of the crew.
  • the invention provides a method of searching for a model of noise which can be used in particular in treatments for reducing noise.
  • Noise reduction treatments based on the noise model found allow to increase the signal / noise ratio of the transmitted signal, a aim being to deteriorate the intelligibility of the signal as little as possible.
  • the denoising denoising and denoising will be used to speak operations to remove or reduce noise components present in the signal.
  • the denoising can be based as we will see on the permanent search for an ambient noise model, on spectral analysis of this noise, and on the digital reconstruction of a useful signal eliminating as much as possible the modeled noise.
  • the noise model is sought in the noisy signals themselves and whenever a plausible noise pattern has been found, this noise model is stored for use. Then a new research begins to find a more suitable model or simply more recent.
  • the invention provides a search method automatic noise patterns in audio input signals noisy, in which we digitize the input signals, and we process these signals from a model found (for example in order to eliminate at best noise corresponding to the model), characterized in that the signals input are cut into successive frames of P samples each, and a repetitive search for a noise model is performed in permanence in the input signals themselves, looking for N successive frames having the expected characteristics of a noise, in storing the corresponding NxP samples to constitute a model of noise useful for processing denoising of input signals, and repeating the research to find a new noise model and store the new one model to replace the previous one or keep the previous model according to the respective characteristics of the two models.
  • the noise model used in particular for denoising is not a known predetermined model or a chosen model among several predetermined models, but this is a model found in the noisy signal itself, which allows not only to adapt denoising to the real annoying noise, but also to adapt the denoising to variations of this noise.
  • the noise model is obtained by considering that the signals whose energy is stable (and preferably, as we will see, whose energy is minimal), over a certain period probably represent noise; the invention is characterized, according to claim 1, in that the searching for a noise model then includes searching for N frames successive whose energies are close to each other (N being between a minimum value N1 and a maximum value N2), the calculation of the average energy of the N successive frames found, and the storage NxP samples as a new active model if the relationship between this average energy and the average energy of the frames of the active model previously stored is below a determined replacement threshold.
  • the search for N successive frames then comprises at minus the following iterative steps: calculation of the energy of a frame current of rank n can be added to a current model of preparation already comprising n-1 successive frames; ratio calculation between this energy and the energy of the previous frame of rank n-1 (and of preferably that of other previous frames between 1 and n-1); comparison of this ratio with a low threshold less than 1 and a high threshold greater than 1; and decision on the possibility of incorporating the frame of rank n in the model in being developed: the frame is not incorporated into the model if the report is not between the two thresholds; it is incorporated into the model if the ratio is between the two thresholds. The procedure is repeated on the next current frame of input signals, with incrementation of n, until the model is stopped.
  • n reaches the high value N2
  • the model developed cannot be taken count as an active model only if n-1 is already greater than or equal to the minimum N1, because the principle is that a noise model is representative if it has an approximately stable energy on at least N1 frames.
  • the model developed does not become active in place of the previous model only if the ratio between its average energy per frame and the average energy of the previous model does not exceed a threshold of predetermined replacement.
  • the search for a new model starts again as soon as the preparation of the previous one is interrupted.
  • the replacement of a previous model by a new model is inhibited as soon as speech is detected in noisy signals.
  • the presence of speech can indeed be detected by digital signal processing procedures (such as than those that can be used in speech recognition).
  • the signal analysis which allows denoising will be based on spectral analysis of signals in time intervals of duration D, which we will call “frames”, and which will have approximately this duration.
  • the general principle of the denoising process is based on a permanent and automatic search for a noise model which will be used to process the input signal to denois it.
  • This research is done on digitized u (t) signal samples stored in a buffer input.
  • This memory is capable of memorizing all the samples of several frames of the input signal (e.g. at least 2 frames).
  • the noise model sought consists of a succession multiple frames including energy stability and energy level relative suggest that it is an ambient noise and not a speech signal or some other disturbing noise. We will see later how this automatic search.
  • the denoising of the input signal u (t) is done from the model of noise that is in memory, and more precisely from the characteristics spectral of this model.
  • a Fourier transform and an estimate of average spectral density of noise are therefore performed on the model of stored noise.
  • the denoising operation is preferably done using a digital filtering from Wiener which will be discussed in more detail.
  • the filter of Wiener is parameterized by the spectral characteristics of the model of noise recorded and by the spectral characteristics of the signal u (t) to denoise.
  • the digitized input signal therefore undergoes a transform of Fourier and an estimate of spectral density.
  • the numerical values of the Fourier transform i.e. the input signal represented by its frequency components, are processed by the Wiener filter and the output of the Wiener filter represents, in frequency space, the signal digital denoised, that is to say rid as much as possible of the noise represented by the registered model.
  • the filtered digital signal is used either for the reconstruction of a sound signal in which the ambient noise has been partly eliminated, i.e. at the speech Recognition.
  • phase of automatic search for a noise model and the permanent updating of this model are crucial steps in the process and are more precisely the subject of the invention.
  • noise ambient is a signal with a stable minimum energy in the short term.
  • the number of frames intended to assess the noise stability is 5 to 20.
  • Energy must be stable over several frames, otherwise we must assume that the signal contains rather speech or noise other than ambient noise. It must be minimal, fault what we consider that the signal contains breathing or phonetic speech elements resembling noise but overlapping to ambient noise.
  • Figure 2 shows a typical evolution configuration temporal energy of a microphone signal at the time of a start speech emission, with a breath noise phase, which goes out for a few tens to hundreds of milliseconds to make room for the ambient noise alone, after which a high energy level indicates the presence speech, to finally return to ambient noise.
  • N1 5
  • a determined range of values for example between 1/3 and 3
  • the noise model is generally based on permanent ambient noise. Even before speaking, preceded by breathing, there is a phase where ambient noise alone is present for a sufficient time to be taken into account as an active noise model. This phase ambient noise alone after breathing is brief; the number N1 is chosen relatively weak, so that we have time to readjust the noise model on ambient noise after the breathing phase.
  • the ambient noise changes slowly, the change will be taken into account. account of the fact that the comparison threshold with the stored model is greater than 1. If it evolves more rapidly in the increasing direction, the evolution may not be taken into account, so it is best to plan to reset the search for a model from time to time noise. For example, in a stopped ground plane, the ambient noise will be relatively weak, and it should not be that during the phase of takeoff the noise model remains frozen on what it was at a standstill because a noise model is only replaced by a less energetic model or not much more energetic. The methods of reset envisaged.
  • FIG. 3 represents a flowchart of the operations of automatic search for an ambient noise pattern.
  • the input signal u (t), sampled at the frequency F e 1 / T e and digitized by an analog-digital converter, is stored in a buffer memory capable of storing all the samples of at least 2 frames.
  • n The number of the current frame in an operation of looking for a noise pattern is denoted by n and is counted by a counter as you search. At the initialization of the search, n is set to 1. This number n will be incremented progressively the development of a model of several successive frames. when analyzes the current frame n, the model already understands by hypothesis n-1 successive frames meeting the conditions imposed to be part of a model.
  • the signal energy of the frame is calculated by summing the squares of the numerical values of the samples of the frame. She is kept in memory.
  • the ratio between the energies of the two frames is calculated. If this ratio is between two thresholds S and S 'one of which is greater than 1 and the other is less than 1, we consider that the energies of the two frames are close and that the two frames can be part of a noise model.
  • the frames are declared incompatible and the search is reset by resetting n to 1.
  • the rank n is incremented of the current frame, and we perform, in a procedure loop iterative, an energy calculation of the next frame and a comparison with the energy of the previous frame or previous frames, using the thresholds S and S '.
  • the first type of comparison consists in comparing only the energy of the frame n to the energy of the n-1 frame.
  • the second type is to compare the energy of frame n at each of frames 1 to n-1. The second way leads to greater homogeneity of the model but it has the disadvantage of not not take sufficiently into account the cases where the noise level increases or decreases rapidly.
  • the energy of the frame of rank n is compared with the energy of the frame of rank n-1 and possibly of other frames previous (not necessarily all of them for that matter).
  • N2 is chosen so as to limit the calculation time in subsequent noise spectral density estimation operations.
  • n is less than N2
  • the homogeneous frame is added to the to help build the noise model, n is incremented and the next frame is analyzed.
  • n is equal to N2
  • the frame is also added to the n-1 previous homogeneous frames and the model of n homogeneous frames is stored for use in noise elimination. Searching for a model is also reset by resetting n to 1.
  • the previous steps relate to the first search for model. But once a model has been stored, it can at any time be replaced by a more recent model.
  • the replacement condition is still a condition of energy, but this time it relates to the average energy of the model and not more about the energy of each frame.
  • the new model is considered better and we store it in place of the previous one. Otherwise, the new model is rejected and the old remains in force.
  • the threshold SR is preferably slightly greater than 1.
  • the SR threshold was less than or equal to 1, we would store at each times the least energetic homogeneous frames, which corresponds well to the fact that ambient noise is considered to be the energy level at below which we never descend. But, we would eliminate any possibility evolution of the model if the ambient noise starts to increase.
  • SR threshold was too high above 1, there is a risk of poorly distinguish ambient noise and other disturbing noises (breathing), or even some phonemes that sound like noise (consonants hissing or hissing for example). Noise removal from a noise pattern stalled on breath or on whistling consonants or hissing could then harm the intelligibility of the denoised signal.
  • the threshold SR is approximately 1.5. At above this threshold we will keep the old model; below this threshold we will replace the old model with the new one. In both cases, we will reset the search by restarting the reading of a first frame of the input signal u (t), and by putting n at 1.
  • the digital treatments of commonly used signal in speech detection identify the presence of words based on the characteristic spectra of periodicity of certain phonemes, in particular the corresponding phonemes to vowels or voiced consonants.
  • This inhibition is to prevent certain sounds from being taken for noise when these are useful phonemes, that a model of noise based on these sounds is stored and that noise suppression after the development of the model then tends to suppress all the sounds Similar.
  • Ambient noise can indeed increase significantly and fast, for example during the acceleration phase of the engines of a plane or other vehicle, air, land or sea. But the SR threshold requires that the previous noise model be kept when the energy average noise increases too quickly.
  • Periodicity can be based on duration average speech in the intended application; for example the durations of speech are on average a few seconds for the crew of a airplane, and the reset can take place with a periodicity of a few seconds.
  • the proper denoising treatment carried out from of a stored noise model, can be performed as follows, by working on the Fourier transforms of the input signal.
  • the Fourier transform of the input signal is carried out frame by frame and provides for each frame P samples in the frequency space, each sample corresponding to a frequency F e / i with i varying from 1 to P. These P samples will be processed preferably in a Wiener filter.
  • the Wiener filter is a digital filter of P coefficients each corresponding to one of the frequencies F e / i of the frequency space.
  • Each sample of the input signal in the frequency space is multiplied by the respective coefficient W i of the filter.
  • the set of P samples thus processed constitutes a denoised signal frame, in the frequency space.
  • these denoised frames are used directly in the frequency space.
  • the coefficients W i of the Wiener filter are calculated from the spectral density of the noisy input signal and the noise spectral density of the stored noise model.
  • the spectral density of a frame of the input signal is obtained from the Fourier transform of the noisy input signal. For each frequency, we take the squared module of the sample provided by the Fourier transform, to obtain a value DS i for each frequency F e / i.
  • the module squared of the P samples is calculated for each frame, and the N squared modules corresponding to the same frequency F e / i are averaged over the N frames of the noise model.
  • P noise density values DB i are obtained.
  • the sample of rank i of the Fourier transform of an input signal frame is multiplied by W i and the succession of the P samples thus multiplied by P Wiener coefficients constitutes the denoised input frame.
  • the implementation of the method according to the invention can be done at from non-specialized computers, provided with calculation programs required and receiving the digital signal samples as they are supplied by an analog-to-digital converter.
  • This implementation can also be done from a specialized computer based on digital signal processors, which allows more signals to be processed more quickly digital.
  • FIG. 4 represents an example of general architecture of a specialized computer receiving the sound signal to denois and providing real time an audible noise signal.
  • the computer includes two signal processors digital DSP1 and DSP2 and working memories associated with these processors.
  • Noise signals are passed through a converter analog-digital CA / D and are stored in parallel in two FIFO1 and FIFO2 buffers (of the "first-in, first-out" type, i.e. first in first out).
  • One of the memories is connected to the processor DSP1, the other to the DSP2 processor.
  • the DSP1 processor is the master processor and it is dedicated rather looking for a noise model. It is therefore programmed to execute at least the following operations: frame energy calculation, energy averaging, comparison with thresholds, comparison frame rank with N1 and N2, etc. It also calculates densities energy spectral of the noise model.
  • This DSP1 processor is coupled to a dynamic working memory DRAM1 in which we store the current frame sample during a calculation, the energy of a frame current, the energy of the previous frame (s), the samples of Fourier transform of the noise model. It is also coupled with a static working memory in which the tables used are stored the computation of Fourier transforms, and the comparison thresholds S and SR.
  • the DSP2 processor is dedicated rather to the calculation of transforms Fourier signal to denois, calculating the spectral density of this signal, calculating Wiener coefficients, Wiener filtering, and inverse Fourier transform if the latter is to be performed.
  • the DSP2 processor is coupled to a dynamic working memory DRAM2 and a static working memory SRAM2.
  • DRAM2 memory stores current frame samples, transform calculation results from Fourier, results of calculation of spectral energy density of the signal, the calculated Wiener coefficients, etc.
  • the SRAM2 memory stores in particular tables used for the computation of Fourier transforms.
  • the denoised sound signal samples calculated by the DSP2 processor are transmitted, through a circulating buffer FIFO3, to a digital analog converter CNIA, and to a circuit of smoothing which reconstructs the denoised sound signal in analog form.

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  • Engineering & Computer Science (AREA)
  • Human Computer Interaction (AREA)
  • Quality & Reliability (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Computational Linguistics (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
  • Soundproofing, Sound Blocking, And Sound Damping (AREA)
  • Noise Elimination (AREA)

Claims (8)

  1. Verfahren zur automatischen Bestimmung von Rauschmodellen in verrauschten Eingangs-Sprachsignalen, das die Digitalisierung der Eingangssignale und die Bearbeitung dieser Signale ausgehend von einem gefundenen Modell umfaßt, wobei die Eingangssignale in aufeinanderfolgende Rahmen von je P Tastproben zerschnitten werden und eine wiederholte Suche nach einem Rauschmodell permanent in den Eingangssignalen selbst durchgeführt wird, indem N aufeinanderfolgende Rahmen mit den erwarteten Rauschmerkmalen gesucht werden, indem die N·P entsprechenden Tastproben zur Bildung eines für die Rauschverminderungsbearbeitung der Eingangssignale nützlichen Rauschmodells gespeichert werden und indem die Suche zur Ermittlung eines neuen Rauschmodells und zur Speicherung dieses neuen Modells anstelle des vorausgegangenen oder zum Erhalt des vorausgegangenen Modells je nach den Merkmalen der beiden Modelle wiederholt wird, dadurch gekennzeichnet, daß die Suche nach einem Rauschmodell die Suche nach N aufeinanderfolgenden Rahmen, deren Energien nahe beieinander liegen, wobei N zwischen einem Mindestwert N1 und einem Höchstwert N2 liegt, die Berechnung der mittleren Energie der N aufeinanderfolgenden gefundenen Rahmen und die Speicherung der N· P Tastproben als neues aktives Modell enthält, wenn das Verhältnis zwischen dieser mittleren Energie und der mittleren Energie der Rahmen des vorher gespeicherten aktiven Modells kleiner als eine bestimmte Ersatzschwelle ist.
  2. Verfahren nach Anspruch 1, dadurch gekennzeichnet, daß die Suche nach N aufeinanderfolgenden Rahmen die folgenden iterativen Verfahrensschritte enthält: die Berechnung der Energie eines laufenden Rahmens des Rangs n, der einem gerade bearbeiteten Modell mit bereits n-1 aufeinanderfolgende Rahmen hinzugefügt werden könnte, die Berechnung des Verhältnisses zwischen dieser Energie und der Energie des vorausgegangenen Rahmens des Rangs n-1, Vergleich dieses Verhältnisses mit einer unteren Schwelle kleiner als 1 und einer oberen Schelle größer 1 und die Entscheidung über die mögliche Eingliederung des Rahmens des Rangs n in das gerade bearbeitete Modell abhängig vom Ergebnis des Vergleichs.
  3. Verfahren nach Anspruch 2, dadurch gekennzeichnet, daß die Suche nach N aufeinanderfolgenden Rahmen auch die Berechnung des Verhältnisses zwischen der Energie des laufenden Rahmens und der Energie eines oder mehrerer anderer, vorausgegangener Rahmen sowie den Vergleich mit den Schwellen umfaßt, wobei der Rahmen in das gerade bearbeitete Modell abhängig vom Ergebnis des Vergleichs eingegliedert wird.
  4. Verfahren nach einem der Ansprüche 2 und 3, dadurch gekennzeichnet, daß für den Fall, daß der Rahmen des Rangs n in das Modell eingegliedert wird, die Größe n um eine Einheit inkrementiert wird, um die Bearbeitung des Modells fortzusetzen, wenn n kleiner als N2 ist, während im gegenteiligen Fall die Bearbeitung des Modells beendet wird, daß die mittlere Energie der n Rahmen und das Verhältnis zwischen dieser Energie und der mittleren Energie der Rahmen des vorher gespeicherten Modells berechnet wird, daß das vorausgegangene Modell beibehalten oder durch das gerade bearbeitete Modell gemäß dem Wert dieses Verhältnisses ersetzt wird und die iterative Suche nach einem neuen Modell wieder begonnen wird.
  5. Verfahren nach einem der Ansprüche 2 und 3, dadurch gekennzeichnet, daß für den Fall, daß der laufende Rahmen des Rangs n nicht in das gerade bearbeitete Modell eingegliedert wird,
    die Bearbeitung des Modells von n-1 Rahmen beendet wird,
    das Verhältnis zwischen der mittleren Energie der Rahmen des gerade bearbeiteten Modells und der mittleren Energie der Rahmen des vorher gespeicherten Modells berechnet und das vorausgegangene Modell beibehalten oder durch das neue Modell je nach dem Wert dieses Verhältnisses ersetzt wird, wenn n größer als N1 ist,
    und die iterative Suche nach einem neuen Modell wieder begonnen wird.
  6. Verfahren nach einem der vorstehenden Ansprüche, dadurch gekennzeichnet, daß man das Vorliegen von Sprache im Eingangssignal sucht und die Suche nach einem neuen Modell verhindert, wenn Sprache vorliegt.
  7. Verfahren nach einem der vorstehenden Ansprüche, dadurch gekennzeichnet, daß man periodisch die Suche neu startet, indem man das neue Modell unabhängig von den jeweiligen Merkmalen des neuen Modells und des vorausgegangenen Modells durchsetzt.
  8. Verfahren nach einem der vorstehenden Ansprüche, dadurch gekennzeichnet, daß man die verrauschten Eingangssignale ausgehend von einem gefundenen Rauschmodell durch spektrale Filterung bearbeitet, um das Rauschen entsprechend dem Modell möglichst gut zu beseitigen.
EP98935094A 1997-07-04 1998-07-03 Verfahren zur bestimmung eines rauschmodells in einem gestörten audiosignal Expired - Lifetime EP0993671B1 (de)

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FR9708509A FR2765715B1 (fr) 1997-07-04 1997-07-04 Procede de recherche d'un modele de bruit dans des signaux sonores bruites
FR9708509 1997-07-04
PCT/FR1998/001428 WO1999001862A1 (fr) 1997-07-04 1998-07-03 Procede de recherche d'un modele de bruit dans des signaux sonores bruites

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EP0993671B1 true EP0993671B1 (de) 2002-06-12

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US (1) US6438513B1 (de)
EP (1) EP0993671B1 (de)
JP (1) JP4338226B2 (de)
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FR (1) FR2765715B1 (de)
WO (1) WO1999001862A1 (de)

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FR2765715A1 (fr) 1999-01-08
US6438513B1 (en) 2002-08-20
FR2765715B1 (fr) 1999-09-17
WO1999001862A1 (fr) 1999-01-14
JP4338226B2 (ja) 2009-10-07
JP2002513479A (ja) 2002-05-08
DE69806006T2 (de) 2002-12-19
DE69806006D1 (de) 2002-07-18
EP0993671A1 (de) 2000-04-19

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