EP0596785A1 - Verfahren zur Sprachunterscheidung bei Geräuschenwesenheit und Vocoder mit einer niedrigen Bitrate zur Ausführung dieses Verfahrens - Google Patents

Verfahren zur Sprachunterscheidung bei Geräuschenwesenheit und Vocoder mit einer niedrigen Bitrate zur Ausführung dieses Verfahrens Download PDF

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Publication number
EP0596785A1
EP0596785A1 EP93402670A EP93402670A EP0596785A1 EP 0596785 A1 EP0596785 A1 EP 0596785A1 EP 93402670 A EP93402670 A EP 93402670A EP 93402670 A EP93402670 A EP 93402670A EP 0596785 A1 EP0596785 A1 EP 0596785A1
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Prior art keywords
autocorrelation
counter
excitation
excitations
periodic
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English (en)
French (fr)
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Pierre André Thomson-CSF SCPI Laurent
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Thales SA
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Thomson CSF SA
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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
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/04—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques
    • G10L19/08—Determination or coding of the excitation function; Determination or coding of the long-term prediction parameters
    • G10L19/09—Long term prediction, i.e. removing periodical redundancies, e.g. by using adaptive codebook or pitch predictor
    • 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
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
    • G10L25/06—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being correlation coefficients

Definitions

  • the present invention relates to a method for discriminating speech in the presence of ambient noise and to a low bit rate vocoder for implementing the method.
  • LPC10 type vocoders order 10 linear prediction
  • the quality of LPC10 type vocoders is often considered insufficient, particularly in terms of listening pleasure, loyalty to the speaker, and resistance to ambient noise, in particular to structured ambient noise of periodic or quasi-periodic.
  • the signal to be coded does not meet this definition, for example a semi-periodic signal, or a mixture of several signals, the reproduction quality is poor.
  • various known methods consist in using, for example, a high speed vocoder of 4800 bits / second and in reducing this bit rate to 2400 bits / second. No assumption is made on the nature of the signal to be coded, the aim of these methods consisting solely in reproducing as faithfully as possible the waveform of the input signal.
  • wavelet method which is a representation of the signal by a combination of waveforms well localized in time and frequency
  • harmonic analysis which is a representation of the signal by a combination of harmonic sinusoids each other
  • CELP abbreviation for Code Excited Linear Prediction or the waveforms used to the input of the synthesis filter are pre-defined, and stored in a "dictionary".
  • the object of the invention is to overcome the aforementioned drawbacks.
  • the subject of the invention is a method of discriminating speech in the presence of ambient noise for a low bit rate vocoder of the type comprising a periodic excitation, an aperiodic excitation and a P-order analysis filter, characterized in what it consists in analyzing a signal S n composed of the sum of a determined number K of periodic excitations and an aperiodic excitation, in calculating the global autocorrelation r m of the signal S n , in calculating the partial sums t m of the short-term autocorrelation s m correlated with the global autocorrelation r m , to initialize a counter k and as long as the counter k does not reach the maximum value K corresponding to the maximum number of periodic excitations, for each incrementation of the counter k, after correcting the calculation of the partial sums t m , in calculating the values of the pitch M k , of the gain ⁇ k and of the slope of the gain ⁇ k of each
  • the main advantage of the method according to the invention is that it makes it possible to reproduce better quality speech than with a standard vocoder at 2400 bits / second and to better resist ambient noise and in particular structured ambient noise. It also has the advantage of using an algorithm of reasonable complexity thus limiting the computational load.
  • the method according to the invention is based on the principle that it is not useful to reproduce the waveform of the input signal and that it is rather necessary to reproduce as best as possible the auditory impression that the signal would have produced.
  • original which is not necessarily the same thing: a standard vocoder at 2400 bits / second which can reproduce a speech signal of excellent quality with certain speakers and in good conditions of sound, although the form of wave produced at synthesis has little to do with the original waveform.
  • the bit rate allocated to the prediction filter is not sufficient to represent the signal with sufficient fidelity, it must be modeled.
  • synthetic speech is considered to give an acoustic impression close to that provided by the original speech signal.
  • Synthetic speech thus considered consists of the superposition of particularly simple waveforms which can be defined with a low bit rate.
  • the standard vocoder at 2400 bits / second is assumed to give satisfactory quality in simple cases, for example, in cases where the signal to be coded can be represented as the superposition of background noise. continuous, and one or more periodic or quasi-periodic signals ; the same assumption is made in harmonic vocoders.
  • bit rate granted to the prediction filter of a standard vocoder can be reduced according to known techniques, used in vocoders at 800 bits / second, thus making it possible to free up bits allocated to the too richly described prediction filter.
  • the bits thus recovered are used to define the K periodic excitations each having a determined period or "pitch" and a gain that can be modulated over time.
  • a first embodiment of the method according to the invention consists in determining the excitation signal representative of the speech extracted from the ambient noises, by giving the period of the "pitch" and the level of the signal from the sum of the K periodic signals. and an aperiodic signal.
  • a standard 2400 bit / second vocoder it is mandatory to determine a single pitch without error and a voicing indicator also without error.
  • the first periodic generator materializing the excitation, does not have the "true" pitch, that is to say the pitch of the speech signal to be extracted, there are still K-1 generators to find it.
  • there is no voicing decision but rather a distribution of the gains between a noise source and K periodic sources there is no risk of voicing error.
  • the method according to the invention is not concerned with the true waveform, or with a residual, but with its composition in terms of periods or "pitch", relative levels, and proportion of noise.
  • the determination of the excitation is therefore made from a signal where the phase information does not appear.
  • the method is based on an autocorrelation calculation, the result of which gives a quantity representative of this signal with certain precautions to detect the periodic components and the gain variations.
  • FIG. 1 represents the flow diagram of the steps of the method according to the invention.
  • the first step 1 calculates the global autocorrelation r m of the signal consisting of the sum of the K + 1 signals.
  • S n and S nm are amplitudes of signal samples and N (m) denotes a number of samples multiple of m, the largest of which is less than or equal to a value N max . This arrangement improves the subsequent detection of the periodicities.
  • the global autocorrelation r m is then recomposed from the sum of the periodic excitations M1, M2, ..., M k and the values of the short-term autocorrelation r -p .. ., r p duplicated at positions 0, M1, 2 M1 , ..., 0, M2, 2 M2 , ..., 0, M k , 2M k , ..., the aftershocks according to the evolution of the level of signal components.
  • the first diagram represents the short-term autocorrelation, the following two diagrams, the contribution of the periodic signals M1 to M K , and the last diagram the global autocorrelation r m obtained from the values of the short-term autocorrelation r - p , ..., r p , convoluted with K pulse trains.
  • the train of pulses relating to the kth periodic excitation is defined by the following formula:
  • the coefficient ⁇ k represents a gain
  • the coefficient ⁇ k a variation of gain, or slope of gain which must be linear to be able to continue the calculations
  • INT (M max / M k ) is a function which retains only the whole part of the ratio M max / M k is the ratio between the value of the maximum pitch and the value of the pitch of the kth periodic excitation.
  • step 2 in FIG. 1 consists in calculating the partial sums t m which in fact correspond calculating the autocorrelation of the global autocorrelation r m limited to its short-term value.
  • the calculation is given by the following formula: and s -p ..., s p are the autocorrelation values r -p , ..., r p limited to its short-term value.
  • an iterative sub-optimal search algorithm to find the K values of M k , ⁇ k and ⁇ k corresponding respectively to the period of the pitch, the slope of the gain and the gain of the kth excitation is implemented in step 5. It consists in calculating the values of M k , ⁇ k and ⁇ k which minimize the following quantity d, for example, by a least squares method: Steps 3 and 4 correspond respectively to the initialization of a counter k and to the incrementation of this counter k as long as the value of counter k has not reached the value K. This test is carried out by step 6 of the method according to the invention.
  • d min
  • the search for a given excitation consists in finding the value M k which minimizes this quantity, knowing that R does not vary during the search and that the quantities S0, S1 and S2 are easily calculable for a given value of M k .
  • equation (10) gives the value of ⁇ k
  • equation (10) gives the value of ⁇ k
  • ⁇ k S2T0-S1T1 t0 (S0S2-S12)
  • the vector R of the autocorrelations r m is only partially modeled by the vector Sl k multiplied by the gain ⁇ k .
  • the autocorrelations r m should therefore be replaced by their modified values r ' m by subtraction of the quantities ⁇ k if k, m according to the following equation:
  • step 7 consists in subtracting from the partial sums t m the values of the autocorrelation c m from the samples s m from the short-term autocorrelation and replacing the partial sums t m by their modified values t m ' . This is done according to the following relationship: with
  • the coefficients c m are calculated only once, since s m does not change during iterations.
  • the level of aperiodic excitation to be used is deduced from the autocorrelation r m .
  • the value of the autocorrelation r m or of the sums t m would be zero after the last correction according to equations (13) and (14).
  • FIG. 3 A new flow diagram of the steps of a second embodiment of the method according to the invention is shown in FIG. 3. In this figure, the steps homologous to those of FIG. 1 are designated by the same references.
  • Step 8 of the method consists of a preprocessing of the input signal.
  • This preprocessing transforms, for example, the raw input signal S m into a signal whose autocorrelation approximates a dirac pulse, therefore a signal whose spectrum is flattened, for example, by an auto predictor filter -adaptive. This pretreatment thus achieves a whitening of the spectrum before analysis.
  • preprocesses such as for example, elimination of the DC component and very low frequencies from the input signal, automatic gain control, and pre-emphasis, are also possible.
  • step 9 consists in weight the autocorrelation which has just been calculated by a simple weighting window which can be represented for example by a non-increasing envelope as a function of time and the width of which is chosen to be wider than the maximum analysis interval.
  • the purpose of this weighting window is more to stabilize the signal rather than to format it by avoiding discontinuities in the continuation of the calculations due to the variable number of replicas of the short-term autocorrelation which the vectors Sl k may include. .
  • Steps 2, 3 and 4 are identical to Figure 1, and step 5 is practically identical with a limitation on the values of ⁇ k and ⁇ k :
  • the method according to the invention is capable of determining the K pitch sought.
  • the only difference with a partially or totally voiced sound lies in the value of the coefficients ⁇ and ⁇ .
  • the calculation according to step 5 retains only the periodic excitations for which the coefficients ⁇ and ⁇ are included in restricted ranges of values: for example, positive values less than 1 for ⁇ ⁇ 0.3 and ⁇ ⁇ 1, and values close to 1 for ⁇ ,
  • 0.2.
  • Limiting the values of ⁇ also makes it possible to avoid pulses of negative diracs representative of the autocorrelation.
  • the coefficient ⁇ can respond for example to the following relationship: (16)
  • Step 10 of the method consists of an additional test on the value of the counter k after the calculation of the coefficients M k , ⁇ k and ⁇ k carried out by step 5 of the method.
  • step 10 is looped back to the incrementation of the counter k represented by step 4.
  • step 11 of the method recalculates the coefficients calculated by step 5: the algorithm used by the method according to the invention is sub-optimal, that is to say that 'he searches for the K periodic excitations one after the other, whereas in all rigor he should seek them all at once.
  • the vectors Sl k are not orthogonal, they share all the autocorrelations r -p to r p creating interference between the various autocorrelations.
  • step 11 recalculates the coefficients ⁇ 1, ⁇ 2, ..., ⁇ k-1 and ⁇ 1, ⁇ 2, ..., ⁇ k-1 in addition to ⁇ k and ⁇ k at the kth iteration, keeping the pitch values M k previously calculated; which amounts to a resolution of a system of K linear equations.
  • a final correction is made to the first embodiment of the method according to the invention by a step 12 which, taking into account the sub-optimality of the algorithm, consists in correcting the pitch values M k :
  • step 12 optimizes the calculation beyond the effective number K of excitations sought and chooses and / or groups among the K '(K' ⁇ K) excitations those which give the best acoustic result. For example, two excitations whose values of M are too close to be discerned are grouped into a single excitation.
  • the determination of the aperiodic excitation level remains identical in the two embodiments of the method according to the invention.
  • FIG. 4 An embodiment of a vocoder allowing the implementation of the method according to the invention is shown in FIG. 4.
  • This device comprises a noise generator 13 delivering a random wave form, or aperiodic excitation, K generators 141 to 14 k each delivering a train of periodic waves where each period of the fundamental, "pitch", is denoted respectively M1 to M k .
  • the aperiodic excitation corresponds to unvoiced sounds like most consonants and the K periodic wave trains correspond to voiced sounds like vowels.
  • the aperiodic excitation and the K aperiodic excitations thus defined are affected respectively by a gain G0 to G k which can be modulated over time represented respectively by the circles 150 to 15 k .
  • the K + 1 excitations are then injected simultaneously at the input of a summator 16.
  • a summator 16 At the output of the summator 16, we obtain the k + 1 superimposed excitations which are injected on a first operand input of a multiplication operator 17
  • the second operand entry allows you to adjust the overall level of the k + 1 excitations.
  • the output signal of the operator 17 is injected at the input of a analysis filter 18, for example, a P-order prediction filter which, using the analysis method according to the invention, outputs a synthetic speech signal free of ambient noise.
  • a quantification method usable with such a vocoder is given by way of example:
  • the overall level of the energy of the frame is quantified semi-logarithmically on 5 bits.
  • the bit rate obtained is 2400 bits / second at the most for frames of 25 ms at least.

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  • Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
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EP93402670A 1992-11-06 1993-10-29 Verfahren zur Sprachunterscheidung bei Geräuschenwesenheit und Vocoder mit einer niedrigen Bitrate zur Ausführung dieses Verfahrens Withdrawn EP0596785A1 (de)

Applications Claiming Priority (2)

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FR9213397 1992-11-06
FR9213397A FR2697937A1 (fr) 1992-11-06 1992-11-06 Procédé de discrimination de la parole en présence de bruits ambiants et vocodeur à faible débit pour la mise en Óoeuvre du procédé.

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005015953A1 (en) * 2003-08-12 2005-02-17 Sony Ericsson Mobile Communications Ab Method and electronic device for detecting noise in a signal based on autocorrelation coefficient gradients
US7130429B1 (en) 1998-04-08 2006-10-31 Bang & Olufsen Technology A/S Method and an apparatus for processing auscultation signals

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
ISHIKAWA IKEDA: "Narrow to medium bands speech codec family based on LPC technique", NEC RESEARCH AND DEVELOPMENT, no. 85, April 1987 (1987-04-01), TOKYO JP, pages 112 - 121, XP000796772 *
JAIN, XU: "Autocorrelation distortion function for improved AR modeling", INTERNATIONAL CONFERENCE ON ACOUSTICS SPEECH AND SIGNAL PROCESSING, vol. 1, 6 April 1987 (1987-04-06), DALLAS TEXAS, pages 356 - 359 *
LIENARD: "Speech analysis and reconstruction using short time, elementary waveforms", INTERNATIONAL CONFERENCE ON ACOUSTICS SPEECH AND SIGNAL PROCESSING, vol. 2, 6 April 1987 (1987-04-06), DALLAS TEXAS, pages 948 - 951 *
SAGAYAMA, ITAKURA: "Duality theory of composite sinusoidal modeling and linear prediction", INTERNATIONAL CONFERENCE ON ACOUSTICS SPEECH AND SIGNAL PROCESSING, vol. 2, 7 April 1986 (1986-04-07), TOKYO JAPAN, pages 1261 - 1264 *
SUKKAR ET AL: "LPC excitation based on zinc function decomposition", IEEE GLOBAL TELECOMMUNICATION CONFERENCE, vol. 1, 28 November 1988 (1988-11-28), FLORIDA USA, pages 285 - 289, XP010071574 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7130429B1 (en) 1998-04-08 2006-10-31 Bang & Olufsen Technology A/S Method and an apparatus for processing auscultation signals
WO2005015953A1 (en) * 2003-08-12 2005-02-17 Sony Ericsson Mobile Communications Ab Method and electronic device for detecting noise in a signal based on autocorrelation coefficient gradients
US7305099B2 (en) 2003-08-12 2007-12-04 Sony Ericsson Mobile Communications Ab Electronic devices, methods, and computer program products for detecting noise in a signal based on autocorrelation coefficient gradients
US7499554B2 (en) 2003-08-12 2009-03-03 Sony Ericsson Mobile Communications Ab Electronic devices, methods, and computer program products for detecting noise in a signal based on autocorrelation coefficient gradients
CN1868236B (zh) * 2003-08-12 2012-07-11 索尼爱立信移动通讯股份有限公司 根据自相关系数梯度检测信号中噪音的方法和电子设备

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