EP0669606A2 - Procédé de réduction de bruit pour canaux de parole perturbés - Google Patents

Procédé de réduction de bruit pour canaux de parole perturbés Download PDF

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Publication number
EP0669606A2
EP0669606A2 EP95101977A EP95101977A EP0669606A2 EP 0669606 A2 EP0669606 A2 EP 0669606A2 EP 95101977 A EP95101977 A EP 95101977A EP 95101977 A EP95101977 A EP 95101977A EP 0669606 A2 EP0669606 A2 EP 0669606A2
Authority
EP
European Patent Office
Prior art keywords
speech
median
median filtering
signal
filtering
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.)
Granted
Application number
EP95101977A
Other languages
German (de)
English (en)
Other versions
EP0669606B1 (fr
EP0669606A3 (fr
Inventor
Klaus Dr.-Ing. Linhard
Heinz Klemm
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.)
Harman Becker Automotive Systems GmbH
Original Assignee
Daimler Benz AG
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Publication date
Application filed by Daimler Benz AG filed Critical Daimler Benz AG
Publication of EP0669606A2 publication Critical patent/EP0669606A2/fr
Publication of EP0669606A3 publication Critical patent/EP0669606A3/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
    • 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
    • G10L21/0232—Processing in the frequency domain

Definitions

  • the invention relates to a method for noise reduction of a disturbed speech signal with the aid of spectral subtraction.
  • the noise reduction using the spectral subtraction method is used in automatic speech recognition or in hands-free systems to improve speech quality, e.g. when calling from the motor vehicle.
  • the noise reduction by spectral subtraction is characterized in that relatively stationary interference can typically be reduced by approximately 10 dB without additional information about the interference being required. Only the disturbed voice channel is required.
  • the speech signal is divided into short overlapping time segments and processed segment by segment.
  • an estimate of the interference is determined in the speech pauses, and this estimate is subtracted in terms of amount in the spectral range.
  • the spectral subtraction can be implemented in different ways, but is usually implemented as a multiplicative filter in the frequency domain. This spectral subtraction shows the undesirable side effect of a residual musical noise, the "musical tones" and a speech distortion.
  • “Musical tones” are usually suppressed by excessive damping.
  • the excessive damping can be done by overestimating the interference with an overestimating factor or by choosing a special transmission characteristic.
  • the values of the current transfer function are determined for each frequency from the transfer characteristic. It is common to implement a magnitude characteristic in the spectral subtraction filter that has a higher attenuation than e.g. a characteristic curve based on the quadratic error criterion. Specially designed characteristic curves are also possible. Depending on the characteristic used, an overestimation of the disturbance by a factor of 1 to 3 is common.
  • the excessive damping due to the characteristic curve and the overestimation factor gives the desired effect of suppressing "musical tones", but it also has the side effect of a partly. considerable language distortion.
  • Median filtering has proven to be an advantageous method for further substantially improving the spectral subtraction method for reducing the noise of a disturbed speech signal.
  • the median filtering can be applied both to the magnitude spectrum of the disturbed input signal or the noise-reduced output signal after spectral subtraction and to the transfer function determined from the application of a transfer characteristic and can be carried out in the time direction or in the frequency direction.
  • the magnitude spectrum of the speech signal is composed of a sequence of segment spectra in accordance with the segmentation of the speech time signal.
  • the transfer function is represented by the time and frequency discrete values K i , 1 (eg equation (3)). A combination of different of these procedures can also be advantageous.
  • a preferred method provides, in speech pauses by using median filtering, preferably in the temporal direction, to preserve the natural impression of a weak background noise on the transmission function, and during speech activity by applying median filtering to the spectrum of magnitude of the speech signal to strongly suppress the "musical tones" to reach.
  • the separate detection of speech pauses and speech activity is provided and known in any case for determining an average noise signal during speech pauses, so that no special effort is required for this.
  • the methods according to the invention are easy to implement.
  • Median filtering is also already known for processing speech signals.
  • a median filter for successive short-term mean values which represent a measure of the average power of speech signal sections, is used as a smoothing filter. Speech pauses are recognized by comparing the smoothed sequence of values with a threshold value. A This does not result in interference-free speech signals.
  • FIG. 1 shows an example of an input signal E and an output signal A filtered with a median filter of length 3.
  • the median filter first sorts the values within the data window F and then outputs the mean value med.
  • the median filter hides short signal peaks, but receives the remaining signal edges.
  • the following noise example was used: vehicle interior noise at 140 km / h, 12 kHz sampling frequency, segment length 512 values, the last 256 values of each segment are set to zero, the first 256 values of each segment are multiplied by the Hanning window, segments are half overlapped, i.e. a new segment every 10.67ms.
  • FIG. 2 shows first the frequency (linear 0 to 6 kHz) the spectrum for 4 successive segments (time interval 10.67 ms, index I) and then the time (0 to 2.5 seconds) the signal curve for 4 successive discrete frequencies (index i ), representative of all 256 frequencies. It is shown as a typical property of the "musical tones" that the course over the frequency has relatively extensive disturbances (broad impulses), whereas the course over time has a strong impulsive character (narrow impulses). It is precisely the pulse-like character in the temporal direction that makes median filtering particularly effective. A pulse-like fault is deleted. A longer window length of the median filter is required for pulse-like disturbances with wider pulses.
  • the median filter can also be carried out on the input signal before the spectral subtraction. Ideally, this does not result in the creation of "musical tones" that would otherwise be solved by post-filtering with the median filter.
  • the median filtering on the input signal can then be advantageous if "musical tones” influence the various processing steps implemented in the spectral subtraction filter (apart from the characteristic function). It shouldn't go on possible advantages or disadvantages of median filtering on the input or output signal are discussed. In principle, both options are available and, apart from special cases of implementation, are equivalent.
  • the median filter can also be carried out on the transfer function K instead of on the magnitude spectrum of a speech signal.
  • FIG. 4 shows the transfer function K over time and over frequency. The same section is shown as in FIG. 2.
  • the transfer function shows a similar behavior as the output signal in FIG. 2nd
  • FIG. 5 shows the transfer function filtered in time with the 3-fold median. The same section is shown as in FIG. 3.
  • the median filtering in the temporal direction is extremely effective for the same reasons as for the output signal.
  • the pulse-suppressing property of median filtering has a particularly significant effect on the increased impulse disturbance and thus on the "musical tones".
  • Median filtering has a repairing effect on the pulse-like disturbance.
  • the median filtering on the magnitude spectrum of the input or output signal results in the higher gain in the suppression of impulse-like disturbances, but can also lead to changes which are particularly noticeable during speech pauses, while the median filtering of the transmission values in speech pauses essentially increases a pure attenuation of the signal, which makes it sound quieter but natural. Ideally, there are no "musical tones".
  • a preferred embodiment of the invention takes advantage of this by performing the median filtering in the case of speech activity on the magnitude spectrum and in speech pauses on the transmission values. The required speech-pause decision is available anyway with the spectral subtraction, since the formation of the noise estimate is only carried out during the speech pauses.
  • a median filtering in the frequency direction can also be carried out in accordance with equation (6).
  • the detailed explanations given apply analogously to filtering in the frequency direction. It can be seen that as the number of samples within a time segment decreases, the median filtering in the frequency direction gains advantages over the filtering in the time direction and vice versa.
  • the window length is equal to the minimum median window length 3.
  • larger window lengths lead to a further suppression of the "musical tones", but under certain circumstances also to a leveling of the speech signal which is perceived as unnatural.
  • the preferred window length is therefore 3 as indicated by way of example.
  • a larger window length can be appropriate for median filtering.
  • the time interval covered by the median temporal filtering window should not exceed 50 ms ten.
  • the window length of the median filter is based on the data segment length.
  • the data segment length should be less than 64, the median filter not greater than 5.

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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)
  • Soundproofing, Sound Blocking, And Sound Damping (AREA)
  • Reduction Or Emphasis Of Bandwidth Of Signals (AREA)
  • Interconnected Communication Systems, Intercoms, And Interphones (AREA)
EP95101977A 1994-02-23 1995-02-14 Procédé de réduction de bruit d'un signal vocal perturbé Expired - Lifetime EP0669606B1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE4405723A DE4405723A1 (de) 1994-02-23 1994-02-23 Verfahren zur Geräuschreduktion eines gestörten Sprachsignals
DE4405723 1994-02-23

Publications (3)

Publication Number Publication Date
EP0669606A2 true EP0669606A2 (fr) 1995-08-30
EP0669606A3 EP0669606A3 (fr) 1995-10-25
EP0669606B1 EP0669606B1 (fr) 1999-09-22

Family

ID=6510930

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EP95101977A Expired - Lifetime EP0669606B1 (fr) 1994-02-23 1995-02-14 Procédé de réduction de bruit d'un signal vocal perturbé

Country Status (4)

Country Link
EP (1) EP0669606B1 (fr)
AT (1) ATE185014T1 (fr)
DE (2) DE4405723A1 (fr)
ES (1) ES2138669T3 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1998003965A1 (fr) * 1996-07-19 1998-01-29 Daimler-Benz Ag Procede pour reduire les parasites dans un signal vocal
EP1065656A2 (fr) * 1994-05-13 2001-01-03 Sony Corporation Procédé et dispositif pour la réduction du bruit dans des signaux de paroles

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7450693B2 (en) 2001-03-07 2008-11-11 T-Mobile Deutschland Gmbh Method and device for improving voice quality on transparent telecommunication-transmission paths
DE10136491B4 (de) * 2001-03-07 2004-11-25 T-Mobile Deutschland Gmbh Verfahren und Vorrichtung zur Verbesserung der Sprachqualität auf transparenten Telekommunikations-Übertragungswegen
DE10311587A1 (de) * 2003-03-14 2004-09-23 Volkswagen Ag Verfahren und Vorrichtung zum Freisprechen in einem Kraftfahrzeug
DE102011002976A1 (de) 2011-01-21 2012-07-26 Behr Gmbh & Co. Kg Kältemittelkondensatorbaugruppe
EP2673777B1 (fr) * 2011-02-10 2018-12-26 Dolby Laboratories Licensing Corporation Suppression de bruit combinée et signaux hors emplacement
US9173025B2 (en) 2012-02-08 2015-10-27 Dolby Laboratories Licensing Corporation Combined suppression of noise, echo, and out-of-location signals
US8712076B2 (en) 2012-02-08 2014-04-29 Dolby Laboratories Licensing Corporation Post-processing including median filtering of noise suppression gains

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0110467A1 (fr) * 1982-11-23 1984-06-13 Philips Kommunikations Industrie AG Dispositif pour la détection des silences dans les signaux de paroles
US4837828A (en) * 1982-05-12 1989-06-06 Nec Corporation Pattern feature extracting system
DE4229577A1 (de) * 1992-09-04 1994-03-10 Daimler Benz Ag Verfahren zur Spracherkennung mit dem eine Anpassung von Mikrofon- und Sprachcharakteristiken erreicht wird

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE3243232A1 (de) * 1982-11-23 1984-05-24 Philips Kommunikations Industrie AG, 8500 Nürnberg Verfahren zur erkennung von sprachpausen
US4682230A (en) * 1986-03-21 1987-07-21 Rca Corporation Adaptive median filter system

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4837828A (en) * 1982-05-12 1989-06-06 Nec Corporation Pattern feature extracting system
EP0110467A1 (fr) * 1982-11-23 1984-06-13 Philips Kommunikations Industrie AG Dispositif pour la détection des silences dans les signaux de paroles
DE4229577A1 (de) * 1992-09-04 1994-03-10 Daimler Benz Ag Verfahren zur Spracherkennung mit dem eine Anpassung von Mikrofon- und Sprachcharakteristiken erreicht wird

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
ICASSP 81, Bd.1, 30. M{rz 1981, ATLANTA Seiten 1086 - 1088 T.L.PETERSEN ET AL. 'Acoustic suppression in the context of a perceptual model' *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1065656A2 (fr) * 1994-05-13 2001-01-03 Sony Corporation Procédé et dispositif pour la réduction du bruit dans des signaux de paroles
EP1065656A3 (fr) * 1994-05-13 2001-01-10 Sony Corporation Procédé et dispositif pour la réduction du bruit dans des signaux de paroles
WO1998003965A1 (fr) * 1996-07-19 1998-01-29 Daimler-Benz Ag Procede pour reduire les parasites dans un signal vocal
US6687669B1 (en) 1996-07-19 2004-02-03 Schroegmeier Peter Method of reducing voice signal interference

Also Published As

Publication number Publication date
DE59506864D1 (de) 1999-10-28
ATE185014T1 (de) 1999-10-15
ES2138669T3 (es) 2000-01-16
EP0669606B1 (fr) 1999-09-22
DE4405723A1 (de) 1995-08-24
EP0669606A3 (fr) 1995-10-25

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