EP2760022B1 - Suppression de bruit dépendant de la largeur de bande audio - Google Patents

Suppression de bruit dépendant de la largeur de bande audio Download PDF

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Publication number
EP2760022B1
EP2760022B1 EP13153105.5A EP13153105A EP2760022B1 EP 2760022 B1 EP2760022 B1 EP 2760022B1 EP 13153105 A EP13153105 A EP 13153105A EP 2760022 B1 EP2760022 B1 EP 2760022B1
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Prior art keywords
audio
noise suppression
bandwidth
audio bandwidth
noise
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EP13153105.5A
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German (de)
English (en)
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EP2760022A1 (fr
Inventor
Phillip Alan Hetherington
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2236008 Ontario Inc
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2236008 Ontario Inc
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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
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/04—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques
    • G10L19/16—Vocoder architecture
    • G10L19/18—Vocoders using multiple modes
    • G10L19/24—Variable rate codecs, e.g. for generating different qualities using a scalable representation such as hierarchical encoding or layered encoding
    • 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
    • G10L21/0264—Noise filtering characterised by the type of parameter measurement, e.g. correlation techniques, zero crossing techniques or predictive techniques

Definitions

  • the present disclosure relates to the field of audio noise suppression.
  • a system and method for audio bandwidth dependent noise suppression are also possible.
  • Low-bandwidth (a.k.a. limited-bandwidth) communication systems typically use low-bitrate codecs that may be generally intolerant of noise. Reduction of noise is very important when the communication system is intolerant of noise. Higher (a.k.a. wider) bandwidth communication systems may tolerate more noise and may be more likely to be used for multimedia application that may include music. The application of significant noise reduction in higher bandwidth communications may create more undesirable artifacts than allowing the noise to pass through the system or applying less significant noise reduction.
  • G.718 Frame error robust narrow-band and wideband embedded variable bit-rate coding of speech and audio rom 8-32 kbit/s, ITU-T; STUDY PERIOF 2009-20012, INTERNATIONAL TELECOMMUNICATION UNION, GENEVA; CH, Study Group 16, 13 September 2010 (2010-09-13), pages 1-257 , describes a narrow-band (NB) and wideband (WB) embedded variable bit-rate coding algorithm for speech and audio operating in the range from 8 to 32 kbit/s which is designed to be robust to frame erasures.
  • NB narrow-band
  • WB wideband
  • US 5012519A describes noise in a speech-plus-noise input signal is suppressed by splitting the input signal into spectral channels and decreasing the gain in the each channel which has a low signal-to-noise ratio (SNR).
  • SNR signal-to-noise ratio
  • WO 2012/158157 A1 describes a noise suppression method for super-wideband audio signal by splitting the audio signal in a low band and a high band and performing sub-band processing. In case of narrowband or wideband audio signal, the splitting is not performed and the noise suppression is only performed for the low band.
  • a system and method for audio bandwidth dependent noise suppression may detect the audio bandwidth of an audio signal responsive to one or more audio indicators.
  • the audio indicators may include the audio sampling rate and characteristics of an associated compression format.
  • Noise suppression gains may be calculated responsive to the audio bandwidth. Noise suppression gains may mitigate undesirable noise in a reproduced output signal.
  • the noise suppression gains may be modified responsive to the detected audio bandwidth. Less noise reduction may be desirable when more audio bandwidth is available.
  • the modified noise suppression gains may be applied to the audio signal.
  • x ( t ) and n ( t ) denote a clean audio signal, and a noise signal, respectively.
  • G i,k are the noise suppression gains.
  • Various methods are known in the literature to calculate these gains.
  • One example further described below is a recursive Wiener filter.
  • the parameter ⁇ in (3) is a constant noise floor, which defines a maximum amount of noise attenuation across all frequency bins. For example, when ⁇ is set to 0.3, the system will attenuate the noise by a maximum of 10 dB (decibel) at each frequency bin k.
  • the noise reduction process may produce limited noise suppression gains that will range from 0 dB to 10 dB at each frequency bin k.
  • Narrowband communication may have, for example, an audio sample rate of 8 kHz with a 4 kHz audio bandwidth.
  • Wideband communication may have, for example, an audio sample rate of 16 kHz with an 8 kHz audio bandwidth.
  • Full band communication may have, for example, an audio sample rate of 32 kHz or greater with a 16 kHz or greater audio bandwidth.
  • the given associated audio sample rate and audio bandwidth for narrowband, wideband and full band are exemplary in nature and the values may be greater or less than the example values.
  • FIG. 1 is a schematic representation of a system for audio bandwidth dependent noise suppression.
  • a microphone 102 may receive a sound field that is an audible environment associated with the microphones 102. Many audible environments associated with the microphones 102 may include undesirable content that may be mitigated by processing a microphone signal output by the microphone 102 responsive to the received sound field.
  • a noise suppression gain calculator 104 calculates noise suppression gains G i,k using any of various methods that are known in the literature to calculate noise suppression gains.
  • a noise suppression gain applier 106 may apply the noise suppression gains to the microphone signal to mitigate undesirable content.
  • the background noise estimate may include signal information from previously processed frames.
  • the spectral magnitude of the background noise may be calculated using the background noise estimation techniques disclosed in U.S. Patent No. 7,844,453 .
  • alternative background noise estimation techniques may be used, such as a noise power estimation technique based on minimum statistics.
  • One or more audio indicators 110 may indicate the audio bandwidth.
  • One audio indicator 110 of the audio bandwidth may include the audio sample rate of the microphone signal.
  • the audio sampling rate utilized by the noise suppression gain calculator 104 may be an alternative audio indicator 110 as the audio signal 116 may be processed using a sample rate converter.
  • Another audio indicator 110 may include the type of compression format applied to the output signal 108.
  • Compression formats utilized for voice communication may include the 3 rd Generation Partnership Project (3GPP) Adaptive Multi-Rate (AMR) and 3 rd Generation Partnership Project 2 (3GPP2) Enhanced Variable Rate Codec B (EVRC-B).
  • Compression formats utilized for general audio communication may include Motion Pictures Expert Group (MPEG) Advanced Audio Coding (AAC).
  • MPEG Motion Pictures Expert Group
  • AAC Advanced Audio Coding
  • Another audio indicator 110 may include the data rate of the compression format applied to the output signal 108.
  • Another audio indicator 110 may include an energy detector that detects the energy of the audio signal 116 at various frequencies.
  • the energy detector may allow for an estimation of the audio bandwidth of a remote side audio signal.
  • the remote side device may be capable of only narrowband audio signals where it may be desirable to increase the amount of noise reduction.
  • a device that may be capable of limited audio bandwidth, for example narrowband may select a compression format suitable to the limited audio bandwidth. In this case, narrowband audio may cause a voice codec to be selected including AMR or EVRC-B.
  • An audio bandwidth detector 112 may detect the audio bandwidth of the audio signal responsive to one or more audio indicators 110.
  • Low-bandwidth audio communications may utilize low-bitrate compression formats, or codecs, including AMR and EVRC that may be intolerant of noise. Noise reduction may be important when codecs are intolerant of noise. Low audio-bandwidth communication may not be used for music or multimedia applications, so again, reduction of noise may be important. Higher bandwidth communication may tolerate more noise and may be more likely to be used for multimedia applications that involve music where less noise reduction may be desirable.
  • the data rate and type of codec used may change the desired about of noise reduction. For example, an audio codec operating at a low data rate may be perceptibly improved by utilizing more noise suppression. In this case, more noise removal may allow the audio codec to allocate more data rate to the desired signal content.
  • a noise suppression gain modifier 114 may modify the noise suppression gains responsive to the audio bandwidth detected by the audio bandwidth detector 112.
  • the noise suppression gain modifier 114 may, for example, utilize a mechanism described by equation (3) where the audio bandwidth detector 112 may modify the parameter ⁇ .
  • the noise suppression gain modifier 114 may produce limited noise suppression gains that may, for example, have a maximum suppression varying from 10 dB to 12 dB when the audio bandwidth detector 112 detects narrowband audio.
  • the noise suppression gain modifier 114 may produce limited noise suppression gains that may, for example, have a maximum suppression varying from 6 dB to 8 dB when the audio bandwidth detector 112 detects wideband audio.
  • the noise suppression gain modifier 114 may produce limited noise suppression gains that may, for example, have a maximum suppression varying from 0 dB to 6 dB when the audio bandwidth detector 112 detects full band audio.
  • the audio bandwidth detector 112 may detect full band audio when a low data rate audio codec is utilized and the noise suppression gain modifier 114 may produce limited noise suppression gains that may have a maximum suppression varying from 6 dB to 10 dB.
  • a subband filter may process the microphone 102 to extract frequency information.
  • the subband filter may be accomplished by various methods, such as a Fast Fourier Transform (FFT), critical filter bank, octave filter band, or one-third octave filter bank.
  • the subband analysis may include a time-based filter bank.
  • the time-based filter bank may be composed of a bank of overlapping bandpass filters, where the center frequencies have non-linear spacing such as octave, 3 rd octave, bark, mel, or other spacing techniques.
  • the noise suppression gains may be calculated for each frequency bin or band of the subband filter. The resulting noise suppression gains may be filtered, or smoothed, over time and/or frequency.
  • Many communications channels may have a variable amount of available communication bandwidth over time. As the amount of communication bandwidth increases, the audio bandwidth of a signal carried by a communications channel may increase. The increased audio bandwidth may be utilized to support one or more of a higher audio sampling rate, utilizing a compression format with increased signal quality and a higher data rate for the associated compression format. Conversely, the amount of audio bandwidth may be reduced over time. The reduction in audio bandwidth may result in one or more of a lower audio sampling rate, utilizing a compression format with reduced signal quality and a lower data rate for the associated compression format.
  • the audio bandwidth detector 112 may trigger the noise suppression gain modifier 114 to cause a change in amount of noise suppression responsive to the dynamic bandwidth conditions of the communication channel and thereby the audio bandwidth.
  • Figure 3 is a representation of a method for audio bandwidth dependent noise suppression.
  • the method 300 may be, for example, implemented using the systems 100 and 200 described herein with reference to Figure 1 and Figure 2 .
  • the method 300 includes the act of detecting the audio bandwidth of an audio signal responsive to one or more audio indicators 302.
  • Noise suppression gain may be calculated responsive to the audio signal 304.
  • the noise suppression gains may be modified responsive to the detected audio bandwidth 306.
  • the modified noise suppression gains may be applied to the audio signal 308.
  • FIG. 2 is a further schematic representation of a system for audio bandwidth dependent noise suppression.
  • the system 200 comprises a processor 202, memory 204 (the contents of which are accessible by the processor 202), one or more microphones 102 and an I/O interface 206.
  • the memory 204 may store instructions which when executed using the process 202 may cause the system 200 to render the functionality associated with audio bandwidth dependent noise suppression as described herein.
  • the memory 204 may store instructions which when executed using the process 202 may cause the system 200 to render the functionality associated with the noise suppression gain calculator module 104, the noise suppression gain applier module 106, the audio indicators 110, the audio bandwidth detector module 112 and the noise suppression gain modifier 114 described herein.
  • data structures, temporary variables and other information may store data in data storage 208.
  • the processor 202 may comprise a single processor or multiple processors that may be disposed on a single chip, on multiple devices or distributed over more that one system.
  • the processor 202 may be hardware that executes computer executable instructions or computer code embodied in the memory 204 or in other memory to perform one or more features of the system.
  • the processor 202 may include a general purpose processor, a central processing unit (CPU), a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a digital circuit, an analog circuit, a microcontroller, any other type of processor, or any combination thereof.
  • the memory 204 may comprise a device for storing and retrieving data, processor executable instructions, or any combination thereof.
  • the memory 204 may include non-volatile and/or volatile memory, such as a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), or a flash memory.
  • RAM random access memory
  • ROM read-only memory
  • EPROM erasable programmable read-only memory
  • flash memory a flash memory.
  • the memory 204 may comprise a single device or multiple devices that may be disposed on one or more dedicated memory devices or on a processor or other similar device.
  • the memory 204 may include an optical, magnetic (hard-drive) or any other form of data storage device.
  • the memory 204 may store computer code, such as the noise suppression gain calculator module 104, the noise suppression gain applier module 106, the audio indicators 110, the audio bandwidth detector module 112 and the noise suppression gain modifier 114 described herein.
  • the computer code may include instructions executable with the processor 202.
  • the computer code may be written in any computer language, such as C, C++, assembly language, channel program code, and/or any combination of computer languages.
  • the memory 204 may store information in data structures including, for example, noise suppression gains and state variables.
  • the I/O interface 206 may be used to connect devices such as, for example, the one or more microphones 102, and to other components of the system 200.
  • the system 200 may include more, fewer, or different components than illustrated in Figure 2 . Furthermore, each one of the components of system 200 may include more, fewer, or different elements than is illustrated in Figure 2 .
  • Flags, data, databases, tables, entities, and other data structures may be separately stored and managed, may be incorporated into a single memory or database, may be distributed, or may be logically and physically organized in many different ways.
  • the components may operate independently or be part of a same program or hardware.
  • the components may be resident on separate hardware, such as separate removable circuit boards, or share common hardware, such as a same memory and processor for implementing instructions from the memory. Programs may be parts of a single program, separate programs, or distributed across several memories and processors.
  • the functions, acts or tasks illustrated in the figures or described may be executed in response to one or more sets of logic or instructions stored in or on computer readable media.
  • the functions, acts or tasks are independent of the particular type of instructions set, storage media, processor or processing strategy and may be performed by software, hardware, integrated circuits, firmware, micro code and the like, operating alone or in combination.
  • processing strategies may include multiprocessing, multitasking, parallel processing, distributed processing, and/or any other type of processing.
  • the instructions are stored on a removable media device for reading by local or remote systems.
  • the logic or instructions are stored in a remote location for transfer through a computer network or over telephone lines.
  • the logic or instructions may be stored within a given computer such as, for example, a CPU.

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  • 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)
  • Soundproofing, Sound Blocking, And Sound Damping (AREA)
  • Circuit For Audible Band Transducer (AREA)

Claims (10)

  1. Procédé mis en oeuvre par ordinateur pour suppression de bruit dépendant d'une largeur de bande audio, comprenant :
    la détection (302) de la largeur de bande audio d'un signal audio (116) en réponse à un ou à plusieurs indicateurs audio ;
    le calcul (304) de gains de suppression de bruit en réponse au signal audio (116) ;
    la modification (306) des gains de suppression audio en réponse à la largeur de bande audio détectée, la modification des gains de suppression audio en réponse à la largeur de bande audio détectée étant basée sur une modification maximale d'un plancher de bruit associée aux gains de suppression de bruit ; les gains de suppression de bruit modifiés limitant une atténuation de bruit appliquée au signal audio (116) en réponse à la largeur de bande audio détectée ; et
    l'application (308) des gains de suppression de bruit modifiés au signal audio (116).
  2. Procédé pour suppression de bruit dépendant d'une largeur de bande audio selon la revendication 1, dans lequel les indicateurs audio comprennent l'un ou plusieurs parmi un débit d'échantillons audio associé au signal audio (116), un détecteur d'énergie, un format de compression associé au signal audio (116) et un débit de données associé au format de compression.
  3. Procédé pour suppression de bruit dépendant d'une largeur de bande audio selon la revendication 2, dans lequel le format de compression comprend l'un ou plusieurs parmi un débit multiple adaptatif, un codec B à débit variable amélioré et un codage audio avancé du Motion Pictures Expert Group.
  4. Procédé pour suppression de bruit dépendant d'une largeur de bande audio selon les revendications 1 à 3, dans lequel le plancher de bruit définit une atténuation maximale pour la largeur de bande audio détectée.
  5. Procédé pour suppression de bruit dépendant d'une largeur de bande audio selon la revendication 1, dans lequel la limite de l'atténuation de bruit comprend :
    de 10 à 12 dB pour la largeur de bande audio détectée jusqu'à une largeur de bande audio de 4 kHz inclus ;
    de 6 à 8 dB pour une largeur de bande audio détectée d'une largeur de bande audio de 4 kHz à 8kHz inclus ; et
    de 0 à 6 dB pour une largeur de bande audio détectée d'une largeur de bande audio supérieure ou égale à 16 kHz.
  6. Procédé pour suppression de bruit dépendant d'une largeur de bande audio selon les revendications 1 à 5, comprenant de plus :
    la détection de changements dans la largeur de bande audio d'un signal audio (116) en réponse aux indicateurs audio au nombre d'un ou de plusieurs ;
    dans lequel la modification des gains de suppression de bruit en réponse à la largeur de bande audio détectée comprend le traitement des changements détectés dans la largeur de bande audio.
  7. Procédé mis en oeuvre par ordinateur selon les revendications 1 à 6, comprenant de plus la génération d'un jeu de sous-bandes pour le signal audio (116) à l'aide d'un filtre de sous-bande ou d'une transformée de Fourier rapide.
  8. Procédé mis en oeuvre par ordinateur selon les revendications 1 à 7, comprenant de plus la génération d'un jeu de sous-bandes pour le signal audio (116) selon une technique d'espacement de bandes critiques, en octaves, en mels, ou en barks.
  9. Système pour suppression de bruit dépendant d'une largeur de bande audio, le système comprenant :
    un processeur (202) ; et
    une mémoire (204) couplée au processeur (202), contenant des instructions qui, lorsqu'elles sont exécutées par le processeur, provoquent l'exécution par le système des étapes selon l'une quelconque des revendications de procédé 1 à 8.
  10. Procédé mis en oeuvre par ordinateur selon les revendications 1 à 8, dans lequel les indicateurs audio comprennent l'un ou plusieurs parmi un format de compression associé au signal audio ou un débit de données associé au format de compression.
EP13153105.5A 2013-01-29 2013-01-29 Suppression de bruit dépendant de la largeur de bande audio Active EP2760022B1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP13153105.5A EP2760022B1 (fr) 2013-01-29 2013-01-29 Suppression de bruit dépendant de la largeur de bande audio
CA2840851A CA2840851C (fr) 2013-01-29 2014-01-27 Attenuation du bruit dependant de la largeur de bande audio

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EP13153105.5A EP2760022B1 (fr) 2013-01-29 2013-01-29 Suppression de bruit dépendant de la largeur de bande audio

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EP2760022B1 true EP2760022B1 (fr) 2017-11-01

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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012158157A1 (fr) * 2011-05-16 2012-11-22 Google Inc. Procédé de suppression de bruit à très large bande

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
IL84948A0 (en) * 1987-12-25 1988-06-30 D S P Group Israel Ltd Noise reduction system
WO2004090870A1 (fr) * 2003-04-04 2004-10-21 Kabushiki Kaisha Toshiba Procede et dispositif pour le codage ou le decodage de signaux audio large bande
US7844453B2 (en) 2006-05-12 2010-11-30 Qnx Software Systems Co. Robust noise estimation

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012158157A1 (fr) * 2011-05-16 2012-11-22 Google Inc. Procédé de suppression de bruit à très large bande

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