EP1132896A1 - Procédé de filtrage fréquentiel appliqué au débruitage de signaux acoustiques mettant en oeuvre un filtre de Wiener - Google Patents

Procédé de filtrage fréquentiel appliqué au débruitage de signaux acoustiques mettant en oeuvre un filtre de Wiener Download PDF

Info

Publication number
EP1132896A1
EP1132896A1 EP00400623A EP00400623A EP1132896A1 EP 1132896 A1 EP1132896 A1 EP 1132896A1 EP 00400623 A EP00400623 A EP 00400623A EP 00400623 A EP00400623 A EP 00400623A EP 1132896 A1 EP1132896 A1 EP 1132896A1
Authority
EP
European Patent Office
Prior art keywords
magnitude
noise
frequency component
estimated
frequency
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP00400623A
Other languages
German (de)
English (en)
Inventor
François Bourzeix
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Motorola Solutions Inc
Original Assignee
Motorola Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Motorola Inc filed Critical Motorola Inc
Priority to EP00400623A priority Critical patent/EP1132896A1/fr
Publication of EP1132896A1 publication Critical patent/EP1132896A1/fr
Withdrawn legal-status Critical Current

Links

Images

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
    • 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

Definitions

  • the present invention relates to a method and apparatus for processing an acoustic signal and in particular for suppressing background acoustic noise from the acoustic signal.
  • Portable communication devices such as cellular telephones often need to detect and transmit a speech or similar signal in noisy environments such as a fast moving vehicle.
  • Methods of suppressing background acoustic noise have thus been developed which permit much better communication with such devices in noisy environments.
  • the general approach adopted by some such methods is to represent the overall acoustic signal as the frequency components of a plurality of frames, each frame basically representing a small portion (e.g., about 10 ms) of the acoustic signal, and then to attempt to detect and remove or suppress any noise components occurring within each frequency component of each frame.
  • One simple and crude method estimates the average magnitude of noise
  • This method can be enhanced by providing that the magnitudes
  • a more sophisticated method multiplies the magnitudes
  • G(k).
  • this must be estimated (e.g., by assuming that X(k) ⁇ S(k)).
  • MMSE Minimum Mean Square Estimation
  • of the frequency components s(k) are again multiplied by denoising filter components G(k) such that
  • G(k).
  • MIPS Millions of Instructions Per Second
  • a method of suppressing acoustic noise in an acoustic signal represented by the frequency components of a plurality of frames, each frame representing a small portion of the acoustic signal comprising the steps of estimating the average magnitude of noise in each frequency component over a plurality of frames, estimating the variability of the magnitude of noise in each frequency component; and generating denoising filter components in dependence on the estimated magnitude of noise in each frequency component, the estimated variability of the magnitude of noise in each frequency component and the magnitude of each frequency component, and varying the magnitude of each frequency component in dependence on the corresponding denoising filter component.
  • This method has the significant advantage of taking account of the variability of the magnitude of noise within each frequency component over time. In this way, it is possible to determine an approximate probability of any one frequency component being largely comprised of noise or alternatively of being largely comprised of wanted speech signal.
  • the method further comprises setting the filter components in dependence on the ratio of the magnitude of each frequency component to an estimated likely maximum magnitude of noise for that frequency component, whereby, if the ratio exceeds a predetermined amount for a given frequency component, the filter component corresponding to such a frequency component may be set to a maximum value which is preferably substantially equal to one, whereas, if the ratio is less than a second predetermined amount, the corresponding filter component may be set to a minimum value, which is preferably substantially equal to 0.15.
  • the filter components are varied in a linear dependence on the ratio of the magnitude of each frequency component to the estimated likely maximum magnitude of noise for that frequency component between a minimum value of the filter components at or below the second predetermined amount and a maximum value at or above the first predetermined value.
  • each filtering component By having a maximum value of each filtering component of about 1, it provides that for signals which are much larger than the maximum likely noise content, there is no signal attenuation. This is actually very beneficial for frequency components which are much larger than the likely maximum noise component, because since the phase of the noise component is not necessarily aligned to the phase of the speech (or similar) signal, the noise component is almost as likely to destructively interfere with the speech signal (thus reducing the overall magnitude of the frequency component relative to the clean speech equivalent) as to constructively interfere with it (thus increasing the magnitude of the frequency component relative to the clean speech equivalent).
  • the magnitudes of the frequency components are filtered to remove high frequency fluctuations thereof.
  • This filtering of the magnitudes of the frequency components is preferably achieved by generating a short term mean estimation of the mean magnitudes of the frequency components, preferably over approximately three frames.
  • an apparatus for suppressing acoustic noise in an acoustic signal represented by the frequency components of a plurality of frames, each frame representing a small portion of the acoustic signal comprising: means for estimating the average magnitude of noise in each frequency component over a plurality of frames; means for estimating the variability of the magnitude of noise in each frequency component; means for generating denoising filter components in dependence on the estimated magnitude of noise in each frequency component, the estimated variability of the magnitude of noise in each frequency component and the magnitude of each frequency component, and means for varying the magnitude of each frequency component in dependence on the corresponding denoising filter component.
  • FIG. 1 there is shown a series of steps or apparatus blocks showing the overall approach of noise suppression according to the present invention.
  • FIG. 1 initially as representing a series of method steps, these are now described in detail below.
  • the first step 10 is to take the acoustic signal s(n) (in the form of digital audio signal amplitude samples) and to perform high pass filtering to remove low frequency components (which do not carry much speech signal information although they may contain a large amount of unwanted background acoustic noise).
  • the second step 20 windows and overlaps (for example, by 50%) the high pass filtered acoustic signal. This step involves separating the signal into a series of overlapping segments and windowing them to form frames so that at the edge of each frame the amplitude of the signal is zero.
  • the third step 30 performs the Fast Fourier Transform on each windowed vector. Given a 256 input signal vector s(n), we obtain a 256 vector s(k) where n and k stand respectively for some time, and frequency indices. In what follows we shall indicate spectral data with bold characters: n, s....
  • the fourth step 40 performs a transformation of the FFT outputs, from Cartesian to polar co-ordinates.
  • the fifth step 50 uses the magnitude of the Fourier Transform, to evaluate the mean magnitude of spectral background noise mag(n(k)).
  • the sixth step 60 performs the estimation of de-noised speech spectral magnitude mag(s(k)) using the noise evaluation from block 50, and the noisy speech spectral magnitude.
  • the seventh, eighth and ninth steps perform the symmetrical operations to those performed by respectively 30,20 and 10: conversion from polar to Cartesian, inverse Fourier transforms and overlap add. It is to be noted that the signal phases is not modified by the algorithm since the noisy speech phases is used to reconstruct the clean speech signal in step 70. The main structure of this algorithm is very classical. The innovative feature of the algorithm is in the way noise is removed from speech in step 60. This step is now described in detail.
  • the step 60 can be subdivided into 3 sub-steps.
  • the first sub-step 110 is dedicated to evaluating the noise variance.
  • Step 50 output is the mean magnitude of background noise.
  • ⁇ (k) mean(mag(s(k)-n(k)))/mag(n(k)).
  • the third sub-step 130 is dedicated to calculating the denoising filter gain for each frequency channel. It is done as follows:

Landscapes

  • Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Quality & Reliability (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Noise Elimination (AREA)
EP00400623A 2000-03-08 2000-03-08 Procédé de filtrage fréquentiel appliqué au débruitage de signaux acoustiques mettant en oeuvre un filtre de Wiener Withdrawn EP1132896A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP00400623A EP1132896A1 (fr) 2000-03-08 2000-03-08 Procédé de filtrage fréquentiel appliqué au débruitage de signaux acoustiques mettant en oeuvre un filtre de Wiener

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP00400623A EP1132896A1 (fr) 2000-03-08 2000-03-08 Procédé de filtrage fréquentiel appliqué au débruitage de signaux acoustiques mettant en oeuvre un filtre de Wiener

Publications (1)

Publication Number Publication Date
EP1132896A1 true EP1132896A1 (fr) 2001-09-12

Family

ID=8173589

Family Applications (1)

Application Number Title Priority Date Filing Date
EP00400623A Withdrawn EP1132896A1 (fr) 2000-03-08 2000-03-08 Procédé de filtrage fréquentiel appliqué au débruitage de signaux acoustiques mettant en oeuvre un filtre de Wiener

Country Status (1)

Country Link
EP (1) EP1132896A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7613608B2 (en) 2003-11-12 2009-11-03 Telecom Italia S.P.A. Method and circuit for noise estimation, related filter, terminal and communication network using same, and computer program product therefor
CN101950563A (zh) * 2010-08-20 2011-01-19 东南大学 基于分数傅里叶变换的二维维纳滤波的取证语音增强方法
CN111613239A (zh) * 2020-05-29 2020-09-01 北京达佳互联信息技术有限公司 音频去噪方法和装置、服务器、存储介质

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0913810A2 (fr) * 1997-10-31 1999-05-06 Sony Corporation Extraction de caractéristiques et reconnaisance des formes
EP0918317A1 (fr) * 1997-11-21 1999-05-26 Sextant Avionique Procédé de filtrage fréquentiel appliqué au débruitage de signaux sonores mettant en oeuvre un filtre de Wiener

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0913810A2 (fr) * 1997-10-31 1999-05-06 Sony Corporation Extraction de caractéristiques et reconnaisance des formes
EP0918317A1 (fr) * 1997-11-21 1999-05-26 Sextant Avionique Procédé de filtrage fréquentiel appliqué au débruitage de signaux sonores mettant en oeuvre un filtre de Wiener

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
FEI X ET AL: "Speech enhancement by spectral magnitude estimation - A unifying approach", SPEECH COMMUNICATION,NL,ELSEVIER SCIENCE PUBLISHERS, AMSTERDAM, vol. 19, no. 2, 1 August 1996 (1996-08-01), pages 89 - 104, XP004013500, ISSN: 0167-6393 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7613608B2 (en) 2003-11-12 2009-11-03 Telecom Italia S.P.A. Method and circuit for noise estimation, related filter, terminal and communication network using same, and computer program product therefor
CN101950563A (zh) * 2010-08-20 2011-01-19 东南大学 基于分数傅里叶变换的二维维纳滤波的取证语音增强方法
CN101950563B (zh) * 2010-08-20 2012-04-11 东南大学 基于分数傅里叶变换的二维维纳滤波的取证语音增强方法
CN111613239A (zh) * 2020-05-29 2020-09-01 北京达佳互联信息技术有限公司 音频去噪方法和装置、服务器、存储介质
CN111613239B (zh) * 2020-05-29 2023-09-05 北京达佳互联信息技术有限公司 音频去噪方法和装置、服务器、存储介质

Similar Documents

Publication Publication Date Title
JP4567655B2 (ja) 音声信号中の暗騒音を抑制する方法及び装置、並びに反響消去を伴う対応する装置
US8010355B2 (en) Low complexity noise reduction method
JP4210521B2 (ja) 雑音低減法および装置
US6377637B1 (en) Sub-band exponential smoothing noise canceling system
US7620172B2 (en) Method and system for eliminating noises and echo in voice signals
EP1855457B1 (fr) Annulation d'écho à canaux multiples à l'aide d'une étape de décorrélation
JP4162604B2 (ja) 雑音抑圧装置及び雑音抑圧方法
KR101052445B1 (ko) 잡음 억압을 위한 방법과 장치, 및 컴퓨터 프로그램
EP1806739B1 (fr) Systeme de suppression du bruit
EP2045801B1 (fr) Traitement de signal audio efficace dans le régime de sous-bande, méthode, système et programme d'ordinateur associé
EP2031583B1 (fr) Estimation rapide de la densité spectrale de puissance de bruit pour l'amélioration d'un signal vocal
JP2003534570A (ja) 適応ビームフォーマーにおいてノイズを抑制する方法
JP2002517021A (ja) 線形たたみ込みおよび因果的フィルタリングを用いたスペクトルサブトラクションによる信号雑音低減
EP1526510B1 (fr) Méthode et dispositif pour l'annulation d'écho avec des taux arbitraires d'échantillonnage du signal de retour
US20030018471A1 (en) Mel-frequency domain based audible noise filter and method
JP2008052277A (ja) 拡張された帯域幅を有する音響信号を提供するための方法およびシステム
US20080152157A1 (en) Method and system for eliminating noises in voice signals
EP1995722B1 (fr) Procédé de traitement d'un signal d'entrée acoustique pour fournir un signal de sortie avec une réduction du bruit
JP2000330597A (ja) 雑音抑圧装置
US20060184361A1 (en) Method and apparatus for reducing an interference noise signal fraction in a microphone signal
US20030033139A1 (en) Method and circuit arrangement for reducing noise during voice communication in communications systems
EP1132896A1 (fr) Procédé de filtrage fréquentiel appliqué au débruitage de signaux acoustiques mettant en oeuvre un filtre de Wiener
WO2006077934A1 (fr) Dispositif et procede de suppression de bruit par division de bande
CN108074580B (zh) 一种噪声消除方法及装置
US7177805B1 (en) Simplified noise suppression circuit

Legal Events

Date Code Title Description
PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AT BE CH CY DE DK ES FI FR GB GR IE IT LI LU MC NL PT SE

AX Request for extension of the european patent

Free format text: AL;LT;LV;MK;RO;SI

AKX Designation fees paid
REG Reference to a national code

Ref country code: DE

Ref legal event code: 8566

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE APPLICATION IS DEEMED TO BE WITHDRAWN

18D Application deemed to be withdrawn

Effective date: 20020313