WO2001041334A1 - Procede et appareil de suppression du bruit de fond acoustique dans un systeme de communications - Google Patents

Procede et appareil de suppression du bruit de fond acoustique dans un systeme de communications Download PDF

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Publication number
WO2001041334A1
WO2001041334A1 PCT/US2000/032610 US0032610W WO0141334A1 WO 2001041334 A1 WO2001041334 A1 WO 2001041334A1 US 0032610 W US0032610 W US 0032610W WO 0141334 A1 WO0141334 A1 WO 0141334A1
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WO
WIPO (PCT)
Prior art keywords
gain
noise
signal
channel
snr
Prior art date
Application number
PCT/US2000/032610
Other languages
English (en)
Inventor
Tenkasi V. Ramabadran
James P. Ashley
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 JP2001542485A priority Critical patent/JP2003517761A/ja
Priority to EP00980890A priority patent/EP1238479A4/fr
Priority to BR0016127-6A priority patent/BR0016127A/pt
Priority to KR1020027007102A priority patent/KR20020056957A/ko
Publication of WO2001041334A1 publication Critical patent/WO2001041334A1/fr

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/005—Control of transmission; Equalising
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04S—STEREOPHONIC SYSTEMS 
    • H04S1/00—Two-channel systems
    • H04S1/007—Two-channel systems in which the audio signals are in digital form

Definitions

  • the present invention relates generally to noise suppression and, more particularly, to noise suppression in a communication system.
  • Noise suppression techniques in a communication system are well known.
  • the goal of a noise suppression technique is to reduce the amount of background noise during speech coding so that the overall quality of the coded speech signal presented to the user is improved.
  • Communication systems which implement speech coding include, but are not limited to, voice mail systems, cellular radiotelephone systems, trunked communication systems, airline communication systems, etc.
  • spectral subtraction One noise suppression technique, which has been implemented in cellular radiotelephone systems, is spectral subtraction.
  • the audio input is divided into individual spectral bands (channels) by a suitable spectral divider and the individual spectral channels are then attenuated according to the noise energy content of each channel.
  • the spectral subtraction approach utilizes an estimate of the background noise power spectral density to generate a signal-to-noise ratio of the speech in each channel (channel SNR), which in turn is used to compute a gain factor for each individual channel.
  • the gain factor is then used as an input to modify the channel gain for each of the individual spectral channels.
  • the channels are then recombined to produce the noise- suppressed output waveform.
  • the minimum gain factor is assigned this minimum gain factor so that it is maximally attenuated.
  • a channel which is deemed to be completely voice-like (high SNR values), will be assigned a gain factor of 1.0 (0 dB) so that it is not attenuated at all.
  • a channel which is somewhat voice-like (intermediate SNR values), will be assigned a gain factor between the minimum value and 1.0.
  • the choice of the minimum gain factor is based on the following two conflicting requirements: 1 )
  • the minimum gain factor should be low enough so that the background noise is attenuated sufficiently thereby rendering the noise-suppressed speech more pleasing to listen to.
  • the minimum gain factor should be high enough so that any unintentional but unavoidable suppression of weak speech sounds does not cause serious degradation to speech intelligibility.
  • a typical choice for the minimum gain factor value is 0.2239 (-13 dB).
  • SNR background noise power ratio
  • the above approach with a fixed minimum gain factor value works reasonably well when the overall speech signal power to the background noise power ratio, i.e., SNR, is reasonably high, e.g., 15 dB or higher.
  • SNR background noise power ratio
  • the use of wireless telephony is becoming so widespread that the demands placed on noise suppressor performance in acoustically harsher environments are ever increasing. For example, locations such as airports and train stations, as well as in-vehicle hands-free applications, are rapidly becoming normal operating environments for wireless telephony. The impact is that the normal expected signal-to-noise ratios (SNR's) are getting worse, and that the prior art noise suppression technology was not designed to cope with these harsher operating environments.
  • FIG. 1 generally depicts a block diagram of a speech coder for use in a communication system.
  • FIG. 2 generally depicts a block diagram of a noise suppression system in accordance with the invention.
  • FIG. 3 generally depicts the relationship between channel SNR (dB) and channel gain factor (dB).
  • the present invention provides a method for suppressing acoustic background noise in a communications system, the method comprised of estimating a signal component and a noise component of an input signal to produce a signal-to-noise ratio estimate; determining a maximum noise attenuation factor based on at least the signal-to-noise ratio estimate; generating a gain function based on at least the maximum noise attenuation factor; and applying the gain function to the input signal to produce a noise suppressed signal for use in the communications system.
  • a noise suppression system implemented in a communication system provides an improved level of quality during low signal-to-noise ratio (SNR) conditions thereby extending the SNR range over which noise suppression is useful.
  • the noise suppression system 109 inter alia, incorporates an adaptation block 290 that adapts the minimum gain factor value depending on the operating SNR level.
  • the operating SNR level 292, which serves as the input to the adaptation block 290, is reliably evaluated from channel energy 293 and background noise energy 294 values by the SNR level estimator 295.
  • the channel gain calculator 233 computes the gain factor to be applied to each channel based on the channel SNR. It uses two parameters, viz., MIN_GAIN (dB) and GAIN_SLOPE (dB/dB). With reference to Fig. 3, the operation of the channel gain calculator 233 can be explained as follows. When the channel SNR (dB) is below a certain threshold (CH_SNR_THLD), i.e., the channel is completely noise-like, the gain factor selected is minimum, i.e., MIN_GAIN so that the channel is maximally attenuated.
  • CH_SNR_THLD a certain threshold
  • the channel gain selected is 0 dB (i.e., 1.0 in linear scale) so that the channel is not attenuated at all.
  • the gain factor selected lies between MIN_GAIN and 0 dB.
  • the above approach for computing channel gain factors works well when the speech signal power to the background noise power ratio (SNR) is fairly high, e.g., 15 dB or higher. In this case, there is clearer separation between speech and noise.
  • the background noise is strongly attenuated (by -MIN_GAIN dB), the strong speech sounds are practically unattenuated, and the weak speech sounds are slightly attenuated (primarily the noisy channels). Speech quality is enhanced because the background noise is suppressed and there is no serious degradation in speech intelligibility.
  • the noise suppressor 109 When the noise suppressor 109 is required to perform at lower SNR levels, however, the above approach for computing the gain factors turns out to be unsatisfactory. At lower SNR levels, the separation between speech and noise is unclear especially for weak speech sounds. Consequently, such sounds are attenuated resulting in loss of intelligibility. Even though the background noise is attenuated, the loss of intelligibility causes the overall speech quality to degrade. In order to improve the noise suppressor performance at low SNR levels, the values of MIN_GAIN and GAIN_SLOPE are adjusted such that weak speech sounds are not attenuated as much. For example, suppose the value of MIN_GAIN is increased from -13 dB to -10 dB.
  • the speech energy in dB is a filtered version of samples of peak channel energy (dB) of voiced frames.
  • dB peak channel energy
  • the noise energy in dB is a filtered version of samples of total noise energy in all the channels of noise-only frames.
  • ch_enrg(i) - Float variable storing the channel energy, i.e., average energy in the i th frequency channel first - Static boolean variable which is true only for the first frame frame_count - Static integer variable indicating the frame number fupdate_flag - Boolean variable which overrides the update_flag and forces an update of background noise energy estimate gain_slope - Float variable storing the gain slope value, which is used in the computation of channel gain factors i - Integer variable used as an index min_gain - Static float variable storing the filtered value of the minimum gain value samples, which is used in the computation of channel gain factors min_gain_raw - Float variable storing the minimum gain value sample which is computed as function of the SNR level noise_enrg_dB - Float variable storing the noise energy sample in
  • SNR_THLD - Parameter serving as a SNR threshold.
  • a frame with a SNR larger than this value will be assigned the lowest value of minimum gain, i.e., MIN_GAIN_LOW.
  • VM_SUM_THLD - Parameter serving as a voice metric sum threshold. A frame with a voice metric sum higher than this is considered to be significantly voiced.
  • the steps involved in estimating the filtered speech energy are shown below.
  • the first step is to initialize the filtered speech energy estimate to some reasonable value:
  • the next step is to detect if the current frame is voiced by determining whether the voice metric sum exceeds a pre-selected threshold. If so, get the peak channel energy and use it as a sample in obtaining the filtered speech energy estimate. if (vm_sum > VM_SUM_THLD)
  • spch_enrg_dB_filt ⁇ 1 * spch_enrg_dB_filt + (1- ⁇ 1 ) * spch_enrg_dB; (6) ⁇ else
  • spch_enrg_dB_fiit ⁇ 2 * spch_enrg_dB_filt + (1- ⁇ 2) * spch_enrg_dB; (7) ⁇
  • the filters used are simple leaky integrators (or first order auto- regressive) and different integration constants are used depending on whether the speech energy in increasing or not. If the speech energy is increasing, the integration is fast; otherwise, it is slow. This ensures that the filtered estimate is smoother and it tracks the peak speech energy, which is more reliable in low SNR situations. If the current frame is not a voiced frame, the filtered speech energy estimate remains unchanged from the previous value.
  • the steps involved in estimating the filtered noise energy are shown below.
  • the first step is to initialize the filtered noise energy estimate to some reasonable value.
  • noise_enrg_dB_filt INIT_NOISE_ENRG_DB; (8)
  • the next step is to detect if the current frame is a noise-only frame by means of the update_flag and get the total noise energy to be used as a sample in obtaining the filtered noise energy estimate.
  • noise_enrg_dB_filt ⁇ * noise_enrg_dB_filt + (1- ⁇ ) * noise_enrg_dB; (10)
  • the filter used is a simple leaky integrator (or first order auto- regressive filter) and the integration constant is ⁇ . If the current frame is not a noise-only frame, the filtered noise energy estimate remains unchanged from the previous value.
  • the SNR level for the current frame in dB is obtained as
  • snr spch_enrg_dB_filt - noise_enrg_dB_filt; (11 )
  • min_gain and gain_slope are selected as follows. First, a raw value of minimum gain is computed from the SNR level and bounded to be within the limits defined by
  • min_gain_raw MIN_GAIN_HIGH
  • gain_slope is then calculated as follows.
  • gain_slope GAIN_SLOPE_HIGH + ⁇ * (MIN_GAIN_LOW - min_gain); (17)
  • the SNR level dependent min_gain and gain_slope are used in the computation of gain factors for the different channels using (1 ), (2), and (3) in which MINJ3AIN and GAIN_SLOPE are replaced by min_gain and gain_slope respectively.

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  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Multimedia (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Compression, Expansion, Code Conversion, And Decoders (AREA)
  • Cable Transmission Systems, Equalization Of Radio And Reduction Of Echo (AREA)
  • Noise Elimination (AREA)
  • Transceivers (AREA)

Abstract

La présente invention concerne un procédé et un appareil de suppression du bruit de fond acoustique dans un système de communications. Un niveau de rapport signal/bruit (SNR) de fonctionnement est évalué de manière fiable à partir des valeurs d'énergie de canal (293) et d'énergie du bruit de fond (294) à l'aide d'un estimateur du niveau du SNR (295). Un facteur de gain minimum et une pente de gain sont adaptés (290) en fonction du niveau de fonctionnement du niveau du SNR. En utilisant ces valeurs adaptées et le SNR de canal, le gain de canal est sélectionné (233). Lorsque le canal SNR est en-dessous d'un certain seuil, ce canal est entièrement à caractéristique de bruit et le facteur de gain sélectionné est minimum, de sorte que le canal soit atténué au maximum. Lorsque le SNR du canal est passablement élevé, le gain de canal sélectionné a une valeur de 0 dB. Pour les valeurs intermédiaires du SNR du canal, le facteur de gain sélectionné se situe entre le minimum et 0 dB.
PCT/US2000/032610 1999-12-03 2000-11-30 Procede et appareil de suppression du bruit de fond acoustique dans un systeme de communications WO2001041334A1 (fr)

Priority Applications (4)

Application Number Priority Date Filing Date Title
JP2001542485A JP2003517761A (ja) 1999-12-03 2000-11-30 通信システムにおける音響バックグラウンドノイズを抑制するための方法と装置
EP00980890A EP1238479A4 (fr) 1999-12-03 2000-11-30 Procede et appareil de suppression du bruit de fond acoustique dans un systeme de communications
BR0016127-6A BR0016127A (pt) 1999-12-03 2000-11-30 Método e aparelho para suprimir o ruìdo acústico de segundo plano em um sistema de comunicação
KR1020027007102A KR20020056957A (ko) 1999-12-03 2000-11-30 통신 시스템에서 음향 배경 잡음을 억제하기 위한 방법 및장치

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US16895599P 1999-12-03 1999-12-03
US60/168,955 1999-12-03
US72393900A 2000-11-28 2000-11-28
US09/723,939 2000-11-28

Publications (1)

Publication Number Publication Date
WO2001041334A1 true WO2001041334A1 (fr) 2001-06-07

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EP (1) EP1238479A4 (fr)
JP (1) JP2003517761A (fr)
KR (1) KR20020056957A (fr)
BR (1) BR0016127A (fr)
WO (1) WO2001041334A1 (fr)

Citations (9)

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Publication number Priority date Publication date Assignee Title
US4430754A (en) * 1980-08-29 1984-02-07 Victor Company Of Japan, Ltd. Noise reducing apparatus
US4628529A (en) * 1985-07-01 1986-12-09 Motorola, Inc. Noise suppression system
US4630305A (en) * 1985-07-01 1986-12-16 Motorola, Inc. Automatic gain selector for a noise suppression system
US5406635A (en) * 1992-02-14 1995-04-11 Nokia Mobile Phones, Ltd. Noise attenuation system
US5432859A (en) * 1993-02-23 1995-07-11 Novatel Communications Ltd. Noise-reduction system
US5544250A (en) * 1994-07-18 1996-08-06 Motorola Noise suppression system and method therefor
US5659622A (en) * 1995-11-13 1997-08-19 Motorola, Inc. Method and apparatus for suppressing noise in a communication system
US6088668A (en) * 1998-06-22 2000-07-11 D.S.P.C. Technologies Ltd. Noise suppressor having weighted gain smoothing
US6104993A (en) * 1997-02-26 2000-08-15 Motorola, Inc. Apparatus and method for rate determination in a communication system

Family Cites Families (1)

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Publication number Priority date Publication date Assignee Title
US5706395A (en) * 1995-04-19 1998-01-06 Texas Instruments Incorporated Adaptive weiner filtering using a dynamic suppression factor

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4430754A (en) * 1980-08-29 1984-02-07 Victor Company Of Japan, Ltd. Noise reducing apparatus
US4628529A (en) * 1985-07-01 1986-12-09 Motorola, Inc. Noise suppression system
US4630305A (en) * 1985-07-01 1986-12-16 Motorola, Inc. Automatic gain selector for a noise suppression system
US5406635A (en) * 1992-02-14 1995-04-11 Nokia Mobile Phones, Ltd. Noise attenuation system
US5432859A (en) * 1993-02-23 1995-07-11 Novatel Communications Ltd. Noise-reduction system
US5544250A (en) * 1994-07-18 1996-08-06 Motorola Noise suppression system and method therefor
US5659622A (en) * 1995-11-13 1997-08-19 Motorola, Inc. Method and apparatus for suppressing noise in a communication system
US6104993A (en) * 1997-02-26 2000-08-15 Motorola, Inc. Apparatus and method for rate determination in a communication system
US6088668A (en) * 1998-06-22 2000-07-11 D.S.P.C. Technologies Ltd. Noise suppressor having weighted gain smoothing

Non-Patent Citations (1)

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Title
See also references of EP1238479A4 *

Also Published As

Publication number Publication date
KR20020056957A (ko) 2002-07-10
EP1238479A4 (fr) 2005-07-27
JP2003517761A (ja) 2003-05-27
EP1238479A1 (fr) 2002-09-11
BR0016127A (pt) 2002-08-06

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