EP1104925A1 - Verfahren zur Verarbeitung von Sprachsignalen durch die Subtraktion einer Rauschfunktion - Google Patents

Verfahren zur Verarbeitung von Sprachsignalen durch die Subtraktion einer Rauschfunktion Download PDF

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Publication number
EP1104925A1
EP1104925A1 EP99124195A EP99124195A EP1104925A1 EP 1104925 A1 EP1104925 A1 EP 1104925A1 EP 99124195 A EP99124195 A EP 99124195A EP 99124195 A EP99124195 A EP 99124195A EP 1104925 A1 EP1104925 A1 EP 1104925A1
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Prior art keywords
noise
signal
speech
spectrum
function
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EP99124195A
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English (en)
French (fr)
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Jens Erik Pedersen
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Siemens AG
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Siemens AG
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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

Definitions

  • the invention relates to methods for processing noise speech signals by subtracting a noise function in accordance with the generic class of the independent claims.
  • the methods for processing speech signals by subtracting a noise function having the characterising features of the independent claims have the advantage that the weighting of the noise signal depends either on amplitudes of an acquired signal spectrum or on a noise type. By depending on the acquired signal spectrum simultaneously a dependence on frequency and time is achieved. Therefore, an improved speech quality is realised by minimising thereby artefacts such as musical tones. Thus, if the invention is used in mobile phones, better quality phone calls will be the result but also other applications such as speech recognition and audio recording benefit from the invention by a reduction of the artefacts and therefore of an improved speech recognition or an improved audio recording.
  • the invention is improved by detecting a noise type of the detected noise spectrum, for example noise in a car, noise in an office or noise in the street. This knowledge is used to adapt the noise signal to its noise type. In this way, an improved weighting of the noise signal is achieved, and it will lead to an improved noise reduction of the speech signal.
  • a noise type of the detected noise spectrum for example noise in a car, noise in an office or noise in the street.
  • the upper limit limits the amplitudes of the spectral noise signal exceeding the upper limit, so that this method avoids musical tones. This leaves a small part of the original noise in the final signal, so that the listener can still hear what environment the speaker is in.
  • the lower limit sets the lower limit as an amplitude for those amplitudes of the spectral noise signal being under the lower limit. This is an easy and effective method for improving the noise signal.
  • a method of acquiring a spectral noise signal is used either during speech or when no speech is present. If the spectral noise signal is acquired during speech, it will be an excellent spectral noise signal because it is in time very near to the speech signal for which it is used for noise reduction. If the spectral noise signal is estimated during a time interval without speech, the processing of the spectral noise signal will be straight forward and very easy, since the noise is not masked by the speech signal.
  • Figure 1 shows a block diagram of a speech signal processing
  • figure 2 shows a flow chart of a noise reduction in speech signals
  • figure 3 shows a flow chart of weighting a noise signal depending on the signal-to-noise ratio of a speech signal
  • figure 4 shows a flow chart of a method for reducing noise in a speech signal using upper and lower limits for the spectral power of the noise
  • figure 5 shows an amplitude/frequency diagram with a noise signal and two limits
  • figure 6 shows a flow chart of a noise reduction method using stored noise types
  • figure 7 shows a flow chart of a noise reduction method in speech signals using an envelope of the signal spectrum.
  • the speech When speaking to a phone the speech is degraded by background noise.
  • the speech and the background noise are converted by a microphone of the phone set into electrical signals.
  • this background noise has to be removed or at least to be considerably reduced. If a too large amplitude from an amplitude of a speech signal is removed, the speech signal will exhibit unwanted audible effects - so called musical tones - when reconverted to acoustic signals by a loudspeaker.
  • the musical tones are more disturbing for listeners than noise, since the auditory recognition system of an human being tries to find an interpretation of those musical tones whereas noise is easily regarded by a listener as noise and, consequently, the noise does not interfere with the recognition of the speech as long as the signal strength of the noise is not too high.
  • Speech coding removes therefore redundancy from the speech signal.
  • the information per bit is considerably increased. Noise can influence the speech coding procedure and therefore what the speech information containing bit will look like. This can lead to a poor audio quality.
  • the reduction of noise before speech coding sets in is a precondition for excellent speech quality at the receiver.
  • Noise is a stochastic signal and therefore cannot be predicted. There are peaks and drops in the noise signal which one has to cope with if noise reduction and avoiding musical tones are the goals.
  • By transforming noise and a speech signal from a time domain to a frequency domain it is possible to identify whether a present signal is speech with noise or only noise. Alternatively, this can be done in the time domain.
  • FIG. 1 a block diagram of a speech signal processing for reducing noise is shown.
  • a microphone 50 with attached electronics is connected to a transforming unit 51 for transforming signals from the time domain to a frequency domain generating a signal spectrum.
  • the microphone transduces acoustical waves into electrical signal, the attached electronics amplifying and digitising the electrical signals.
  • the digitised signals are therefore fed into the transforming unit 51.
  • the signal spectrum is then fed from the transforming unit 51 into a decision unit 52 deciding whether the signal spectrum is noise or noise and speech signals. If it is a noise signal, the noise signal is then transferred over a first output of the decision unit 52 to a noise processor 53 weighting the noise signal generating a noise function. If the signal spectrum consists of noise and speech signals then the signal spectrum is transferred from the decision unit 52 to an adder 54 subtracting the noise function coming from the noise processor 53 from the signal spectrum coming from the decision unit 52 in order to generate speech signals with reduced noise. The signal spectrum is always transferred to the adder 54, whereas the noise function is only updated when only a noise signal is present in the signal spectrum.
  • These speech signals are then transformed from the frequency domain to the time domain by a retransforming unit 55.
  • a retransforming unit 55 At the output of the retransforming unit 55 the speech signals with reduced noise in the time domain are ready for further processing.
  • the transforming unit 51, the decision unit 52, the noise processor 53, the adder 54 and the retransforming unit 55 are implemented on one processor as different software programs or functions. Alternatively, more than one processor can be implemented to perform the above mentioned tasks.
  • the noise processor 53 performs either one of the following algorithms for processing the noise signal considering amplitudes of the signal spectrum after the invention:
  • a noise processing is performed using stored noise spectra by comparing those stored noise spectra with the noise signal.
  • Fig. 2 a flow chart of a method for noise reduction and reduction of the musical tones of speech signals after the invention is shown.
  • This noise reduction and all other noise reduction methods are implemented on a processor.
  • noise reduction methods are implemented in software running on processors.
  • step 1 of the noise reduction method acoustical waves emitted by a speaker are converted by a mobile phone into electrical signals using a microphone as a transducer.
  • the electrical signal are then amplified, filtered and digitised using a signal processing unit connected to the microphone.
  • step 2 of the noise reduction method the electrical signals are transformed from a time domain to a frequency domain in order to generate the signal spectrum.
  • a processor In a mobile phone, a processor is placed performing the transformation of the digitised electrical signals from the time domain into the frequency domain using a Fast Fourier Transform (FFT).
  • FFT is a well-known algorithm for processors to perform the transformation of the signals of the time domain into the frequency domain. This transformation consists of a sampled signal in the time domain and of using the samples of the sampled signal for a well-known equation to perform the transformation.
  • Wavelets are mathematical functions cutting up data into different frequency components and then studying each component with a resolution matched to its scale. Wavelets perform especially well on discontinuities and spikes in the signals to be analysed.
  • step 3 it is checked by the processor whether the signal spectrum represents speech signals with noise or a noise signal.
  • VAD Voice Activity Detector
  • the VAD algorithm is one of the GSM (Global System for Mobile Communication) speech coders and it detects whether there is speech activity or not by comparing a spectral power density of the signal spectrum with predefined values. Does the spectral power density exceed those predefined values, the VAD decides that speech is present.
  • GSM Global System for Mobile Communication
  • a similar algorithm can be implemented on a separate processor which is useful for distinguishing between speech and background noise because a processor is fully dedicated to voice activity detection.
  • a background noise signal is updated in predefined time intervals, for example 480 ms. This saves bandwidth for other mobile phones to communicate.
  • GSM Global System for Mobile communications
  • step 10 the noise signal will be processed using predefined factors stored in the mobile phone. These factors have been determined using the knowledge on a human auditory reception system. Alternatively, this step can be omitted.
  • step 5 a signal-to-noise ratio of the speech signal depending on the frequency is calculated. For this, the noise signal is subtracted from the speech signal and then a resulting difference is divided by the noise signal. This is done for certain frequencies in the acquired spectrum of the speech signal. The number of those frequencies determines the accuracy and the complexity of the noise reduction method.
  • step 4 after having performed step 10, the noise signal is weighted to generate a noise function by a function depending on the signal-to-noise ratio of the speech signal using thereby the result of step 5.
  • the noise signal is weighted. Therefore, according to this solution, the weighting of the noise signal is only possible if speech signals are present.
  • step 13 this method of weighting the noise signal is shown.
  • step 13 the method is started.
  • step 14 the signal-to-noise ratio of the speech signals is compared with a first predefined limit. If the signal-to-noise ratio is higher than the first predefined limit, then, in step 15, the spectral noise signal is set to zero, since the speech signals are due to a very high signal-to-noise ratio of an excellent quality, so that an improvement by reducing noise will not lead to an audible improvement for the listener.
  • step 16 the signal-to-noise ratio of the speech signals is compared to a second predefined limit. If the signal-to-noise ratio of the speech signals is above the second predefined limit, then in step 17 the weighting of the noise signal is performed after a predefined function depending on the signal-to-noise ratio.
  • step 18 the signal-to-noise ratio of the speech signals is compared to a third predefined limit. If the speech signals are above the third predefined limit, then, in step 19, the noise signal is weighted by a constant weighting factor.
  • the predefined function of step 17 connects the zero-value weighting appearing in step 15 with this constant weighting factor in step 19.
  • step 20 the signal-to-noise ratio of the speech signals is compared to a fourth predefined limit. If the signal-to-noise ratio of the speech signals is above this fourth predefined limit, then, in step 21, the noise signal is weighted with a predefined function.
  • step 22 the weighting of the noise signal for the frequencies where the signal-to-noise ratio is equal or below the fourth predefined limit is set to zero. This is done because if the signal-to-noise ratio is that low it is already so noisy that a reduction of the noise would not lead to an improvement but it would introduce unwanted audible effects.
  • step 21 the predefined function applied in step 21 for weighting the noise signal connects linearly the constant weighting factor and zero.
  • parabolic or exponential functions can be implemented for these predefined functions.
  • the predefined limits are set according to listening tests.
  • step 6 the weighted noise signal, that is the noise function, is stored in a processor.
  • step 7 the noise function is subtracted from the signal spectrum of the original speech signals. These are the speech signals before the unweighted noise signal was subtracted for calculating the signal-to-noise ratio.
  • step 8 the spectrum of the speech signal with reduced noise is transformed from the frequency domain into the time domain using inverse FFT.
  • step 9 speech coding on the speech signal with reduced noise in the time domain is performed.
  • step 24 a speech signal or noise are converted to an electrical signal using a microphone.
  • Attached electronics to the microphone amplifies and digitises the electrical signal.
  • step 25 the electrical signal is transformed form the time domain to the frequency domain using FFT generating the signal spectrum.
  • step 26 it is detected using VAD whether speech signals or a noise signal are present. If a noise signal is present, in step 28, the noise signal is weighted.
  • Fig. 5 the weighting of the spectral noise signal with an amplitude s as a function of the frequency f is shown.
  • a limit 11 for the high amplitudes is set. Is an amplitude equal or above the limit 11, then it is set to this limit 11. This avoids that too much noise is removed from the speech signal and thereby the appearance of musical tones.
  • An exemplary signal is added to Fig. 4 to show amplitudes of that signal exceeding the limits in both directions.
  • an optionally lower limit 12 is also added in this method, so that very low noise amplitudes in the spectrum of the noise which are equal or under the limit 12 are set to the limit 12.
  • background noise does not necessarily disturb a listener and it gives the listener an impression that a connection between him and a speaker is still in existence if only noise is transmitted.
  • the noise processed with the upper and the lower limit is transmitted.
  • the upper limit 11 must be included whereas the lower limit can be included.
  • step 29 the weighted noise signal is stored, so that when a speech signal is present, the weighted noise signal is subtracted from the speech signal to generate a speech signal with reduced noise in step 30.
  • step 31 the speech signal with reduced noise is transformed from the frequency domain to the time domain using inverse FFT.
  • step 32 speech coding on the speech signal with reduced noise in the time domain is performed.
  • step 33 a speech signal or noise are converted to an electrical signal using a microphone. Attached electronics to the microphone amplifies and digitises the electrical signal.
  • step 34 the electrical signal is transformed form the time domain to the frequency domain using FFT.
  • step 35 it is detected using VAD whether a speech signal or a noise signal is present. If a spectral noise signal is present, that means no speech signal, in step 36, the noise signal is weighted.
  • the weighting of the noise signal is to analyse the noise spectrum by using stored noise spectra. If the noise type is detected, for example noise in a car, noise in an environment where many people are speaking or noise in a street, then the measured noise can be weighted according to its noise type optimising both the noise reduction and the reduction of artefacts (musical tones).
  • step 37 the weighted noise signal is stored, so that when a speech signal is present, the weighted noise signal is subtracted from the speech signal to generate a speech signal with reduced noise in step 38.
  • step 39 the speech signal with reduced noise is transformed from the frequency domain to the time domain using inverse FFT.
  • step 40 speech coding on the speech signal with reduced noise in the time domain is performed.
  • step 41 a speech signal or noise are converted to an electrical signal using a microphone. Attached electronics to the microphone amplifies and digitises the electrical signal.
  • step 42 the electrical signal is transformed form the time domain to the frequency domain using FFT.
  • step 43 it is detected using VAD whether a speech signal or a noise signal is present. If a spectral noise signal is present, that means no speech signal, in step 43, the noise signal is weighted with an envelope of the speech signal. Thus, the weighting occurs only when a speech signal is present.
  • the envelope of the speech signal is calculated using FFT, also in step 42. It is practically the same as the speech signal, so the weighting is performed using the speech signal.
  • the envelope is used to avoid a noise reduction of the speech signal which would lead to unwanted audible effects that means too much noise reduction is avoided. Especially at those frequencies where most of the speech signal energy is located.
  • the envelope of the speech signal is weighted by factors that are stored already in the processor. These factors have been found by using listening tests.
  • step 45 the weighted noise signal is stored, so that the weighted noise signal is subtracted from the present speech signal to generate a speech signal with reduced noise in step 46.
  • step 47 the speech signal with reduced noise is transformed from the frequency domain to the time domain using inverse FFT.
  • step 48 speech coding on the speech signal with reduced noise in the time domain is performed.
  • the invention is usable for other applications. Speech recognition demands a signal with good signal-to-noise ratio for a proper recognition of the speech. Thus, a speech recognition system would considerably benefit from using the invention.
  • Another application is audio recording either for only audio reproduction or in combination with video recording.
  • an improved noise reduction algorithm focussing on improving speech and/or music quality benefits from the invention.
  • the noise spectrum is either acquired when no speech signal is present or during a speech signal.
  • the first solution is straight forward, since the background noise is transformed from the time domain to the frequency domain to generate the noise spectrum.
  • the speech signal with the noise is transformed from the time domain to the frequency domain and then by analysing the speech signal, the speech signal itself is modelled using the processor and then this model of the speech signal is subtracted from the transformed speech signal to generate a noise spectrum as the difference between the measured speech signal and the model.
  • the processor uses stored knowledge on the spoken language in order to estimate what was said.

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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)
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  • Physics & Mathematics (AREA)
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EP99124195A 1999-12-03 1999-12-03 Verfahren zur Verarbeitung von Sprachsignalen durch die Subtraktion einer Rauschfunktion Withdrawn EP1104925A1 (de)

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1349148A1 (de) * 2000-12-28 2003-10-01 NEC Corporation Verfahren und vorrichtung zur rauschunterdrueckung
EP1526511A1 (de) 2003-10-21 2005-04-27 Alcatel Endgerät eines Telefonsystems zur Verwaltung im Empfang der Wiedergabequalität der Sprache
US7495832B2 (en) 2002-12-17 2009-02-24 Nec Corporation Light dispersion filter and optical module
CN106409297A (zh) * 2016-10-18 2017-02-15 安徽天达网络科技有限公司 一种语音识别方法

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US5757937A (en) * 1996-01-31 1998-05-26 Nippon Telegraph And Telephone Corporation Acoustic noise suppressor
US5781883A (en) * 1993-11-30 1998-07-14 At&T Corp. Method for real-time reduction of voice telecommunications noise not measurable at its source
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US5757937A (en) * 1996-01-31 1998-05-26 Nippon Telegraph And Telephone Corporation Acoustic noise suppressor
FR2765715A1 (fr) * 1997-07-04 1999-01-08 Sextant Avionique Procede de recherche d'un modele de bruit dans des signaux sonores bruites

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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1349148A1 (de) * 2000-12-28 2003-10-01 NEC Corporation Verfahren und vorrichtung zur rauschunterdrueckung
EP1349148A4 (de) * 2000-12-28 2008-05-21 Nec Corp Verfahren und vorrichtung zur rauschunterdrueckung
US7590528B2 (en) 2000-12-28 2009-09-15 Nec Corporation Method and apparatus for noise suppression
US7495832B2 (en) 2002-12-17 2009-02-24 Nec Corporation Light dispersion filter and optical module
US7944613B2 (en) 2002-12-17 2011-05-17 Nec Corporation Optical module having three or more optically transparent layers
US8456741B2 (en) 2002-12-17 2013-06-04 Nec Corporation Optical module having three or more optically transparent layers
EP1526511A1 (de) 2003-10-21 2005-04-27 Alcatel Endgerät eines Telefonsystems zur Verwaltung im Empfang der Wiedergabequalität der Sprache
CN106409297A (zh) * 2016-10-18 2017-02-15 安徽天达网络科技有限公司 一种语音识别方法

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