EP0984433A2 - Suppression de bruit dans une unité de communication vocale et méthode d'opération - Google Patents

Suppression de bruit dans une unité de communication vocale et méthode d'opération Download PDF

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Publication number
EP0984433A2
EP0984433A2 EP99117227A EP99117227A EP0984433A2 EP 0984433 A2 EP0984433 A2 EP 0984433A2 EP 99117227 A EP99117227 A EP 99117227A EP 99117227 A EP99117227 A EP 99117227A EP 0984433 A2 EP0984433 A2 EP 0984433A2
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EP
European Patent Office
Prior art keywords
speech
noise
signal
periodic
gaussian noise
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Withdrawn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
EP99117227A
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German (de)
English (en)
Inventor
Dominic Sai Fan Chan
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 UK Ltd
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Motorola Ltd
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Publication date
Application filed by Motorola Ltd filed Critical Motorola Ltd
Publication of EP0984433A2 publication Critical patent/EP0984433A2/fr
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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
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • 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
    • G10L2021/02085—Periodic noise

Definitions

  • This invention relates to suppressing noise in communications systems and more particularly to suppressing periodic noise from car engines or police sirens in a mobile communications system.
  • voice communications systems such as the TErrestrial Trunked RAdio (TETRA) system for private mobile radio users, use speech processing units to encode and decode speech patterns.
  • TETRA TErrestrial Trunked RAdio
  • the speech encoder converts the analogue speech pattern into a suitable digital format for transmission and the speech decoder converts a received digital speech signal into an appropriate analog speech pattern.
  • the primary objective in the use of speech coding techniques is to reduce the occupied capacity of the speech patterns as much as possible, by use of compression techniques, without losing fidelity of speech signals.
  • Speech coding typically uses speech production modelling techniques to compress pulse code modulation (PCM) speech signals into bit-rates that are suitable for different kinds of bandwidth-limited applications such as speech communication systems or voice storage systems.
  • PCM pulse code modulation
  • the basic speech production model that is commonly used in speech coding algorithms, is shown in FIG. 1.
  • the model in FIG. 1 was used in early linear predictive coding (LPC) based vocoders.
  • LPC linear predictive coding
  • the LPC filter models the combined effect of the glottal pulse model, the vocal tract and the lip radiation.
  • voiced speech the voiced excitation, which consists of a pulse train separated by the pitch duration T, is used as an input signal to the LPC filter.
  • a gaussian noise source is used as the LPC filter input excitation.
  • the excitation in the ACELP case is a weighted combination of the innovative codebook vector and the adaptive codebook vector.
  • the innovative codebook in the ACELP case consists of code-vectors each contains only a small number of pulses and zero value elsewhere.
  • the periodicity of the excitation which is needed for voiced speech, derives from the last frame total LPC filter input excitation based on the present frame pitch lag value.
  • the main customers of TETRA radios are public safety organisations.
  • the noise level in the mobile operating environment is often higher than that in fixed telecommunication systems.
  • Wideband noise comes from the various operating environments; such as car or wind noise, street noise, babble noise.
  • Periodic noise mainly comes from repetitive motion in car engines, as well as the sirens of public safety vehicles.
  • the fundamental of the periodic noise is mainly concentrated at low frequencies, typically of the order of less than 250 Hz.
  • siren signal spectrum fulfils the conditions which most noise suppression algorithm uses to decide whether the incoming signal is a speech signal.
  • a speech communications unit includes a speech processor for receiving an input speech signal having a periodic noise interferer.
  • the speech processor is operably coupled to a noise determining means for determining an amplitude of the periodic noise interferer a gaussian noise generator for generating a known gaussian noise sequence and combining the speech signal with the known gaussian noise sequence to produce a resultant signal, and inputting the resultant signal into a noise suppression procedure to provide a noise suppressed speech signal, wherein the speech processor determines an amplitude level of the suppressed gaussian noise and subtracts a respective level of suppressed gaussian noise from the resultant signal thereby reducing the periodic noise content in the speech signal.
  • the introduction of a gaussian noise signal to the periodic interferer, into the speech signal reduces the periodic noise content of the signal.
  • the gaussian noise signal generated is of substantially equal amplitude to the periodic interferer.
  • the speech communications unit further includes a noise suppresser function, coupled to the output of the summing junction, for further suppressing noise in the speech signal.
  • the speech processor is either a speech post-processing function in a speech decoder or a speech pre-processing function in a speech encoder.
  • the speech communications unit preferably includes a gain adjuster, operably coupled to the gaussian noise generator and the noise suppresser function for receiving the known gaussian noise sequence and the noise suppressed signal, the gain adjuster being operably coupled to a second summing junction for recombining the gain adjusted signal with the noise suppressed signal thereby suppressing the periodic noise interferer.
  • a method of reducing a periodic interferer in a speech signal includes the steps of determining an amplitude of the periodic interferer; generating a gaussian noise signal of substantially similar amplitude to the periodic interferer; and introducing the gaussian noise signal into the speech signal having the periodic interferer to produce a resultant signal, inputting the resultant signal into a noise suppression procedure to provide a noise suppressed speech signal, wherein the speech processor determines an amplitude level of the suppressed gaussian noise and subtracts a respective level of suppressed gaussian noise from the resultant signal thereby reducing the periodic noise content in the speech signal.
  • a voiced excitation source 10 provides a pulse train signal, of pitch duration T into a voiced gain element 12.
  • the amplified pulse train signal from voiced gain element 12 is then selectively input, via a switch 14, to a Linear Predictive Coder (LPC) Filter 16.
  • LPC Linear Predictive Coder
  • an unvoiced excitation source 18 provides a gaussian noise signal into an unvoiced gain element 20.
  • the amplified gaussian noise signal from unvoiced gain element 20 is selectively input, via switch 14, to the Linear Predictive Coder (LPC) Filter 16, when no voice is present.
  • the output from the LPC filter 16 is synthetic speech.
  • a series of amplified pulses from the voiced excitation source 10 are combined with amplified signals from an unvoiced excitation source 18, filtered with the resultant generated signal being representative of synthetic speech.
  • FIG. 2 a block diagram of a synthesis functional model of a basic ACELP codec is shown.
  • An excitation vector from the ⁇ Innovtive ⁇ codebook 30 is chosen and input to the voice gain element 31.
  • Another excitation vector from the ⁇ Adaptive ⁇ codebook 32 is also chosen according to the present frame pitch lag value T and input the gain element 33.
  • the output of voice gain element 31 and the output of voice gain element 33 are input to a summation device 34.
  • the output of the summation device 34 is input to the Linear Predictive Coder filter 35.
  • the output of the summation device 34 is also used to update the ⁇ Adaptive ⁇ codebook for next frame speech synthesis.
  • the output from the LPC filter 35 is then synthetic speech.
  • a series of amplified excitation vectors from the ⁇ Adaptive ⁇ codebook 32, incorporating a feedback path from the excitation vector source, are combined with a variety of amplified pulses selected from an ⁇ Innovative ⁇ codebook 30 (unvoiced excitation source).
  • the combined signal is then filtered with the resultant signal from the LPC filter being representative of synthetically generated speech.
  • the particular vectors are chosen to best imitate the speech signal to be transmitted, or being received.
  • FIG. 1 or FIG. 2 are implemented in the encoding functions of a speech codec.
  • the corresponding functions are required in reverse when decoding received speech.
  • FIG. 3 a block diagram of a decoding siren suppression algorithm, according to a preferred embodiment of the invention, is shown.
  • a speech signal is received and decoded in a speech decoder 50.
  • the decoded speech signal with a siren signal contained within it is then input to a first summing junction 56.
  • the decoded speech signal, together with the siren signal is processed to determine the amplitude of the siren signal in processor 52.
  • the amplitude of the siren signal is then used to generate a gaussian noise signal of that same amplitude in the noise generator 54.
  • the output gaussian noise signal is also fed to the first summing junction 56 and input to a sub-band gain adjustment block 60.
  • the output from the first summing junction 56 is effectively the decoded speech signal, plus the gaussian noise signal which hides the periodic noise from the siren.
  • This speech and gaussian noise signal is then used in a noise suppression algorithm 58 to reduce the level of the gaussian noise.
  • the improved speech signal i.e. with a reduced gaussian noise level, is then input to the sub-band gain adjustment block 60.
  • the output from the sub-band gain adjustment block 60 is combined with the improved speech signal in a second summing junction 62.
  • the resultant signal is the decoded speech signal with a greatly reduced siren noise effect.
  • This arrangement has the advantage of requiring just a single microphone input and avoids the need for precise estimation of the siren harmonic frequency.
  • the only requirements are the detection of the siren signal and determination of its amplitude.
  • suppression of siren signal under high signal to noise conditions is achieved using just a single microphone input. Furthermore, the need for precise estimation of the siren harmonic frequency is avoided, by purely detecting the siren signal and estimating its amplitude.
  • FIG. 4 a block diagram of an encoding siren suppression algorithm is shown.
  • the encoding process deals with the situation where the siren is close to the transmitting unit, performing the speech encoding function.
  • the encoding function is basically the decoding function in reverse, with the determination of the siren amplitude being calculated and used to generate a gaussian noise signal of similar amplitude.
  • a speech signal, having a siren signal contained within it, is input to a first summing junction 76. Additionally, the speech signal, together with the siren signal is processed to determine the amplitude of the siren signal in processor 72. The amplitude of the siren signal is then used to generate a gaussian noise signal of that same amplitude in the noise generator 74. The output gaussian noise signal is also fed to the first summing junction 76 and input to a sub-band gain adjustment block 80. The output from the first summing junction 76 is effectively the speech signal, minus the gaussian noise signal which hides the periodic noise from the siren.
  • This speech and gaussian noise signal is then used in a noise suppression algorithm 78 to reduce the level of the gaussian noise.
  • the improved speech signal i.e. with a reduced gaussian noise level, is then input to the sub-band gain adjustment block 80.
  • the output from the sub-band gain adjustment block 80 is combined with the improved speech signal in a second summing junction 82.
  • the resultant signal is the encoded speech signal with a greatly reduced siren noise content.
  • a speech signal is generated with an interfering periodic noise signal having a greatly reduced effect.
  • the results are shown with regard to amplitude versus time of the speech waveforms.
  • Three distinct waveforms are provided.
  • the first waveform 90 shows the input speech signal with the effect of the periodic interference (siren signal).
  • the periodic noise content can be clearly seen with the darkly shaded areas indicating a rapidly changing and relatively constant interfering source.
  • the second waveform 92 shows the input speech signal after applying the standard wide-band noise suppression algorithm. It is clearly shown that the periodic interference (siren signal) is not affected by the standard wide-band noise suppression algorithm, with the darkly shaded areas showing little change from the original speech plus siren signal.
  • the third waveform 94 shows the input speech signal after applying the gaussian noise suppression algorithm, together with the standard wide-band noise suppression algorithm.
  • the third waveform clearly shows a significant reduction in the periodic interference (siren signal) content, with the darkly shaded areas showing approximately a 10 dB siren suppression.
  • the present invention transforms a periodic noise contaminated speech signal into a wideband gaussian noise contaminated speech signal such that a standard wideband noise suppression procedure can be used to suppress the periodic noise.
  • the periodic noise is then detected and its average amplitude estimated.
  • a known gaussian noise sequence with a comparable amplitude is then added to the periodic noise contaminated speech signal.
  • the noise (periodic + gaussian) in the resultant signal is then suppressed using a standard wideband noise suppression procedure (for example the Motorola sub-band noise suppression algorithm).
  • a standard wideband noise suppression procedure for example the Motorola sub-band noise suppression algorithm.
  • the suppressed gaussian noise at the noise suppression procedure output is then calculated and subtracted.
  • the resultant signal is the speech signal with the periodic noise suppressed.

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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)
  • Noise Elimination (AREA)
EP99117227A 1998-09-04 1999-09-02 Suppression de bruit dans une unité de communication vocale et méthode d'opération Withdrawn EP0984433A2 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
GB9819224A GB2341299A (en) 1998-09-04 1998-09-04 Suppressing noise in a speech communications unit
GB9819224 1998-09-04

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EP0984433A2 true EP0984433A2 (fr) 2000-03-08

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020177372A1 (fr) * 2019-03-06 2020-09-10 哈尔滨工业大学（深圳） Procédé et système de séparation de voix basés sur un module vocal antérieur super-gaussien et un apprentissage profond, et support de stockage

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB9905788D0 (en) * 1999-03-12 1999-05-05 Fulcrum Systems Ltd Background-noise reduction
JP6136995B2 (ja) * 2014-03-07 2017-05-31 株式会社Ｊｖｃケンウッド 雑音低減装置

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Publication number Priority date Publication date Assignee Title
JPH01118900A (ja) * 1987-11-01 1989-05-11 Ricoh Co Ltd 雑音抑圧装置
WO1994018666A1 (fr) * 1993-02-12 1994-08-18 British Telecommunications Public Limited Company Reduction du bruit
US5903819A (en) * 1996-03-13 1999-05-11 Ericsson Inc. Noise suppressor circuit and associated method for suppressing periodic interference component portions of a communication signal

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020177372A1 (fr) * 2019-03-06 2020-09-10 哈尔滨工业大学（深圳） Procédé et système de séparation de voix basés sur un module vocal antérieur super-gaussien et un apprentissage profond, et support de stockage

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