EP1212751B1 - Procede d'attenuation de bruits parasites dans un champ de signal - Google Patents

Procede d'attenuation de bruits parasites dans un champ de signal Download PDF

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Publication number
EP1212751B1
EP1212751B1 EP20000958032 EP00958032A EP1212751B1 EP 1212751 B1 EP1212751 B1 EP 1212751B1 EP 20000958032 EP20000958032 EP 20000958032 EP 00958032 A EP00958032 A EP 00958032A EP 1212751 B1 EP1212751 B1 EP 1212751B1
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EP
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Prior art keywords
signal
level
distribution function
value
noise
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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.)
Expired - Lifetime
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EP20000958032
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German (de)
English (en)
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EP1212751A1 (fr
Inventor
Wolfgang Tschirk
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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 a method for suppressing noise in a signal field containing a plurality of signal components, each of which has a value of Accept signal levels and can be applied over an ordinate range from which a distribution function is determined in the signal field, which is a function of the signal level for each of its possible signal level argument values indicates how large the portion is those signal components whose signal level is lower than the argument value that Signal level values of the signal field are modified so that the distribution function of the modified signal field is equal to a predetermined reference distribution, the Sequence of signal components regarding their energy level remains unchanged as well Signal components whose original signal levels are the same, modified the same Signal levels are assigned, and as a reference distribution function one from a distribution function, which has been determined for a set of reference patterns Function is used.
  • Signal fields to which the method according to the invention relates are, for example, in Pattern recognition systems used to describe the patterns to be recognized.
  • the process of recognizing a pattern can usually be roughly as follows Steps to be split: capturing the pattern, preprocessing and classification.
  • the first step, the pattern acquisition is used to convert the original pattern, e.g. a spoken utterance by a user or a document written with text, in a format suitable for processing, e.g. in the form of an electronic signal that can be coded analog or digital, or a file of a predetermined format.
  • a signal / file format e.g. a raster image recording
  • speech recognition for example the utterance spoken by the user via an acoustic input, such as. a microphone, recorded, possibly pre-amplified and in an electrical Voice signal implemented in analog or digitized form.
  • the pattern captured in this way is fed to the preprocessing, which is a reduction in the processing data and a better differentiation of the samples to be determined reached.
  • the result of the preprocessing is a signal field, in the example of speech recognition a spectrum of utterance that can be fed into the classification system.
  • An essential step of preprocessing is often a signal analysis of the pattern signal, e.g. a signal analysis can be carried out for the electrical voice signal of the user utterance Form of a division into time frames (discretization) and a subsequent one Fourier transformation carried out within a time frame with decomposition into Frequency bands occur from which a time-frequency spectrum is obtained. So that is at the same time a - generally considerable - data reduction.
  • Another, Under certain circumstances, an essential step of the preprocessing is the reduction of interference noise in the pattern signal or the signal field obtained therefrom.
  • the signal field comprises a large number of signal components, each of which takes on its own value, here referred to as signal level, of the same type.
  • the signal components are naturally arranged within the signal field, this order being expressed with the help of one or more ordinate parameters.
  • a signal field realized as a time-frequency spectrum consists of many spectral components, each of which has its own energy level; the spectral components are sorted by time frame and frequency band.
  • Each signal component can thus be assigned its own area element of the ordinate area in the ordinate area over which the signal field extends, so that the area elements as a whole cover the ordinate area of the signal field.
  • the ordinate range can be one, two or more dimensions; accordingly, the area elements are line, area or ( n- dimensional) volume elements.
  • the signal field obtained by the preprocessing becomes the classification system fed. This determines which recognition class - i.e. in the case of speech recognition a word of a given vocabulary or a word string - a match given is.
  • the recognition result is then output, for example on an advertisement, or used for further processing, e.g. when entering a command language-oriented facility.
  • the reference noise signal Er is simulated on the basis of predefined or estimated noise parameters.
  • the subtraction of the energy levels can be carried out, for example, in relation to the linear energy levels or “convolutively” in the logarithmic range, ie in the formula mentioned the corresponding logarithms log E, etc. are used instead of the energy levels E, E r , E '.
  • EP 0 062 519 A1 teaches the elimination of interference in radar signals, the distribution the disturbances are known, although arbitrary, in contrast to previously known ones Procedures that require a Rayleigh or Weibull distributed disorder. Knowing the Distribution or at least the associated probability density from which you can get them can derive is a necessary prerequisite for the application of the procedure this Document. Without knowledge of such a distribution, troubleshooting is possible this method is therefore not feasible.
  • EP 0 548 527 A2 teaches a method for generating a level scale transformation a digital radiographic image, e.g. X-ray image in which a cumulative Distribution function of the image is used to measure the level distribution of the image to modify it to be substantially linear in the area of interest.
  • the task underlying this method namely a representation of the image in a form suitable for further investigation by viewing the image, differs significantly from that of the invention.
  • EP 0 720 358 A2 relates to the compression of video signal data.
  • the level distribution of an image modified so that each input level range is larger Output level range is assigned, the more input levels fall in the former range, the entire output level range being limited.
  • the task is namely, more uniform signal compression, from that of the invention significantly different. Accordingly, the compression according to this document a goal distribution not aimed; rather, the compression rule only uses parameters derived from the input signal.
  • the object is achieved by a method of the type mentioned at the outset, in which according to the invention for the modification of the signal level values based on a division the range of values of the signal levels into a number of level ranges for each level range to a first level representing this level range using the Distribution function and the value of the reference distribution function at the first level second level is selected for which the value of the distribution function is the same Value as close as possible to the reference distribution function, and those signal components, whose signal level falls between the first and the second level, the value of the first level is assigned.
  • sequence of signal components in terms of their energy levels remains unchanged, means that for each (any) pair of signal components, for which is the original level of the first component less than that of the second, after the assignment of modified levels to the signal components, the modified level the first component is not greater than (or equal to or less than) the modified level of the second component.
  • the parameter essential for the method according to the invention can be e.g. be determined with the help of experiments.
  • these or a selected one Part of these patterns are used to generate the reference distribution function.
  • advantageously, can then be used as a reference distribution function from a distribution function that for function has been determined using a set of reference patterns become.
  • the distribution function of the reference pattern set itself can function as a reference distribution function be used, or one of them, e.g. by simplifying the course of the curve, won function of the level.
  • Speech signals which are generated against a background of noise, e.g. that inside one Motor vehicle vehicles in operation, are spoken by noise, from various sources, e.g. the vehicle engine, other vehicles, wind etc., and often a mixture of sound components of high energy with unpredictable statistics regarding their timing and frequency.
  • the performance of speech recognition systems therefore quickly decreases when the background noise increases, for example because the vehicle speed is higher becomes.
  • the exemplary embodiment of the invention shown below relates to detection the English words 'zero', 'one', 'two', etc. to 'nine' for the digits 0 to 9 using of a speech recognition system in a car of the small car type.
  • the time axis records a time period of 0.992 s, which is divided into 31 frames T of the same duration (so-called 'frames').
  • the spectral energy is logarithmic in all figures as energy level E, with the unit dB and related to a basic level common to all figures.
  • Spectra of this type were used in the applicant's speech recognition attempts for utterances used about the vocabulary mentioned.
  • speech recognition system used takes place after preprocessing the utterance to be recognized by means of a Noise suppression as explained in more detail below is a classification in which a layered neural network, which has been trained with a training vocabulary was used as a pattern recognition system.
  • the Vocabulary of a number of speakers - advantageously both male and female female persons - in an environment that corresponds to the speaking environment of the car, spoken, for each word several times under noise-free conditions of the background noise (rest of the car).
  • FIG. 2 shows the energy distribution function P1 (E) for the spectrum S1 shown in FIG. 1
  • An energy distribution function P (E) assigned to a spectrum S gives as a function of Energy level E on how many of the spectral components S (T, F) of the spectrum in question S have an energy level which is lower than the specified energy level E, this number as a value between 0 and 1 based on the total number of spectral Components is expressed.
  • the energy distribution function has P1 at 48 dB the value 0.6, because 60% of the energy levels of spectrum S1 are below 48 dB.
  • a large (small) slope in the energy distribution function P (E) corresponds to an energy level, whose value in a large (small) number of components of the associated Spectrum S occurs.
  • An energy distribution function can also be used for a large number of Spectra are determined and then gives the proportion of the components of all spectra with energy level below the specified level E divided by the total number of components of all of these spectra.
  • FIG. 3 shows the spectrogram S2 for an utterance of the word by the same speaker at a car speed of 113 km / h (70 mph).
  • Spectrograms S1 and S2 Fig. 1 and 3
  • the background energy level increases from about 25 dB to about 65 dB, the peaks of the utterance are at 85 dB, the speech components below 70 dB go in the noise background under.
  • the associated power distribution function P2 (E) is in FIG. 4 shown.
  • the energy distribution functions P1 and P2 show that the spectral distribution of the noise-free signal S1 is significantly different from that of the noisy one Signal S2 is in which the background energy is approximately 40 dB higher than in the case of the noise-free signal.
  • the spectral subtraction achieves a reduction in the noise level only on individual components of the resulting spectrum S3.
  • the noise component of the component in question in other components the level remains approximately the same, in some cases there is even an amplification (albeit whose effect is mitigated due to the logarithmic representation of the energy level). This can be seen in FIG. 5 in particular from the low-level components starting from time frame 20.
  • the noise suppression takes place for the present speech signal S2 using a given "template function", namely one as a reference serving power distribution function.
  • template function namely one as a reference serving power distribution function.
  • the energy distribution function would be the sum of those as a reference function Spectra are used which are used in training the speech recognition system for the the relevant word (here 'seven') can be used; because the word to be recognized is the speech recognition system is naturally not known in advance, this is not possible. It Instead, an energy distribution function is selected as a template function, which in Relative to the entirety of the words of the vocabulary to be recognized is appropriate. For example, that energy distribution function can be used as template function P0 which have been derived from the spectra of the entire training vocabulary.
  • sequence of the components should not be changed with regard to their energy levels, ie S '(T 1 , F 1 ) ⁇ S '(T 2 , F 2 ) if S (T 1 , F 1 ) ⁇ S (T 2 , F 2 ); this monotonous condition preserves the structures of the spectrum, at least qualitatively, when the spectrum S is suppressed into a modified spectrum S '.
  • the fitting function is monotonic due to the monotony condition (2), ie R (E 1 ) ⁇ R (E 2 ) if E 1 ⁇ E 2 .
  • the adaptation function R (E) is therefore clearly determined by comparing the energy distribution function P2 of the present signal with the reference function P0. Since the energy distribution functions P, P0 are also monotonically increasing functions, the adaptation function can be formally determined from this by reversing the reference function P0.
  • Table 1 shows an exemplary program pseudo code through which the invention Adaptation of a spectrum takes place.
  • the spectrum S to be adjusted is here in the Field variables S stored, which over the intervals Tmin .. Tmax and Fmin .. Fmax des Time-frequency space is defined.
  • the energy levels of the spectrum can be discrete values assume in the range of values between the energy levels Emin and Emax.
  • a reference energy distribution function is specified as a reference function P0.
  • the energy distribution functions are as fields over the given interval Emin. , Emax Are defined.
  • the associated power distribution function is determined and stored in the field variable PS.
  • the level value is determined for each component S [T, F] of the spectrum, and all components of the energy distribution function PS whose assigned energy level is above this level value are incremented.
  • inc denotes the increment function.
  • the level value E0 represents the modified one Level to the energy level E0 + dE. Then it is checked whether the level difference dE is positive (greater than 0); in this case all components S [T, F] of the spectrum, whose energy level falls in the interval between E0 and E0 + dE, to the energy level E0 posed. After the last run through the outer for loop, the field S contains the invention noise suppressed spectrum S '.
  • FIG. 7 shows the template function P0 (E0) used in the exemplary embodiment, namely the Energy distribution function for the above training vocabulary, d.s. the English Numerals 'zero' to 'nine'.
  • the result according to the invention Noise suppression with the aid of the mentioned template function P0 as the spectrogram S4 8 shown spectrum; the associated energy distribution function P4 is in FIG. 9 played.
  • a level range of the original spectrum can be treated together in this way be that the associated spectral components have a uniform modified level is assigned.
  • This modified level is related to a representative level value the relevant level range, e.g. the mean of the level range or the median the level across the components falling within the level range as described above determined, for example by means of the adaptation function.
  • the method according to the invention is particularly suitable for suppressing superimposed ones Disorders that the monotonic relation of the spectral components of the utterance do not or interfere only slightly.
  • Such disturbances include e.g. white noise, one linear or nonlinear amplification or attenuation of the entire spectrum as well various phenomena of the Lombard effect, which is known to change the Voice and pronunciation depending on the mental state of the speaker, e.g. Stress, describes.
  • the method of noise suppression according to the invention changes what is to be processed Signal even in the absence of noise, since the template function P0 generally from the energy distribution function of the undisturbed utterance is different. This can may create a source of noise-free detection errors. To do this To avoid, for example, training the speech recognition system with the help of spectra that are already performed with the method according to the invention the template function used has been adjusted. The training vocabulary can contain these spectra instead of or together with the original spectra.
  • Another approach is to use the method according to the invention only if if the presence of noise is detected, e.g. in the period shortly before the statement; otherwise, the speech signal of speech recognition without noise suppression fed. This approach does not require an estimate of the noise beyond that Detection of noise would go out.
  • the adaptation of the Spectrum can be significantly simplified by the fact that only a fixed number of Parameters of the template function are used, and the adjustment with regard to these parameters are done.
  • the mean and the spread of the distribution of the Template function can be used.
  • the mean and Scattering the distribution of the energy distribution function is determined, and from the comparison this parameter with those of the template function becomes a linear transformation for the Energy level of the spectrum determined.
  • this linear transformation there is a modified spectrum in which the disturbing effect of the background noise is significantly reduced.
  • a linear transformation is not is sufficient, e.g. a higher order transformation can be used which results from the Comparison of a corresponding number of parameters of the energy distribution function and the template function, e.g. higher moments of the distributions.
  • the method according to the invention is not only suitable for reducing interference for acoustic signals, e.g. Speech signals; rather, it can also be used for other types of patterns be used, which is characterized by a one-dimensional or multi-dimensional field applied feature size can be described. Accordingly, possible areas of application are e.g. character recognition in written, text or the like, reconstruction and / or evaluation of pictures etc.

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  • Engineering & Computer Science (AREA)
  • Human Computer Interaction (AREA)
  • Quality & Reliability (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Computational Linguistics (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
  • Noise Elimination (AREA)
  • Circuit For Audible Band Transducer (AREA)

Claims (2)

  1. Procédé pour supprimer des bruits parasites dans un champ de signal (S2) contenant une multitude de composantes de signal qui adoptent chacune une valeur d'un niveau de signal et peuvent être portées sur une plage d'ordonnée (T,F), avec lequel est déterminée à partir du champ de signal (S2) une fonction de distribution (P2(E)) qui indique en fonction du niveau de signal à chacune de ses valeurs d'argument de niveau de signal (E) possibles, quelle est la proportion des composantes de signal dont le niveau de signal est inférieur à la valeur d'argument (E), et les valeurs de niveau de signal du champ de signal (S2) sont modifiées de sorte que la fonction de distribution (P4(E)) du champ de signal modifié (S4) est égale à une distribution de référence (P0(E)) prédéfinie, avec lequel l'ordre des composantes de signal reste inchangé du point de vue de leur niveau d'énergie, les composantes de signal dont les niveaux de signal originaux sont égaux sont associées à des niveaux de signal modifiés égaux, et comme fonction de distribution de référence (P0) est utilisée une fonction générée à partir d'une fonction de distribution qui était destinée à un jeu d'échantillons de référence
       caractérisé en ce que
       Pour la modification des valeurs de niveaux de signal à partir d'un découpage de la plage de valeurs des niveaux de signal en un certain nombre de plages de niveau pour chaque plage de niveau
    à un premier niveau (E0) représentant cette plage de niveau, en utilisant la fonction de distribution (P2) et la valeur de la fonction de distribution de référence au premier niveau (P0(E0)), est choisi un deuxième niveau pour lequel la valeur de la fonction de distribution (P2(E)) se rapproche autant que possible de la valeur mentionnée de la fonction de distribution de référence (P0(E0)), et
    la valeur du premier niveau (E0) est attribuée aux composantes de signal dont le niveau de signal se trouve entre le premier et le deuxième niveau.
  2. Procédé selon la revendication 1, caractérisé en ce qu'il est réalisé pour un champ de signal sous forme de spectre d'un signal acoustique variant en fonction du temps et/ou de la fréquence.
EP20000958032 1999-09-10 2000-08-28 Procede d'attenuation de bruits parasites dans un champ de signal Expired - Lifetime EP1212751B1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AT00958032T ATE280990T1 (de) 1999-09-10 2000-08-28 Verfahren zur unterdrückung von störrauschen in einem signalfeld

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
AT155999 1999-09-10
AT0155999A AT408286B (de) 1999-09-10 1999-09-10 Verfahren zur unterdrückung von störrauschen in einem signalfeld
PCT/AT2000/000230 WO2001020598A1 (fr) 1999-09-10 2000-08-28 Procede d'attenuation de bruits parasites dans un champ de signal

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EP1212751B1 true EP1212751B1 (fr) 2004-10-27

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US (1) US20020173276A1 (fr)
EP (1) EP1212751B1 (fr)
JP (1) JP2003509730A (fr)
AT (1) AT408286B (fr)
DE (1) DE50008440D1 (fr)
WO (1) WO2001020598A1 (fr)

Cited By (1)

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CN111344966A (zh) * 2017-11-13 2020-06-26 洛昂有限责任公司 波束成形校准

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US6718316B1 (en) * 2000-10-04 2004-04-06 The United States Of America As Represented By The Secretary Of The Navy Neural network noise anomaly recognition system and method
US7492814B1 (en) 2005-06-09 2009-02-17 The U.S. Government As Represented By The Director Of The National Security Agency Method of removing noise and interference from signal using peak picking
US7676046B1 (en) 2005-06-09 2010-03-09 The United States Of America As Represented By The Director Of The National Security Agency Method of removing noise and interference from signal
KR100745977B1 (ko) * 2005-09-26 2007-08-06 삼성전자주식회사 음성 구간 검출 장치 및 방법
US11176642B2 (en) * 2019-07-09 2021-11-16 GE Precision Healthcare LLC System and method for processing data acquired utilizing multi-energy computed tomography imaging

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US3718117A (en) * 1971-04-26 1973-02-27 Armstrong Cork Co Grooved rod coater
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Publication number Priority date Publication date Assignee Title
CN111344966A (zh) * 2017-11-13 2020-06-26 洛昂有限责任公司 波束成形校准

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JP2003509730A (ja) 2003-03-11
WO2001020598A1 (fr) 2001-03-22
EP1212751A1 (fr) 2002-06-12
ATA155999A (de) 2001-02-15
AT408286B (de) 2001-10-25
DE50008440D1 (de) 2004-12-02
US20020173276A1 (en) 2002-11-21

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