EP1016072B1 - Verfahren und vorrichtung zur rauschunterdrückung eines digitalen sprachsignals - Google Patents

Verfahren und vorrichtung zur rauschunterdrückung eines digitalen sprachsignals Download PDF

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EP1016072B1
EP1016072B1 EP98943999A EP98943999A EP1016072B1 EP 1016072 B1 EP1016072 B1 EP 1016072B1 EP 98943999 A EP98943999 A EP 98943999A EP 98943999 A EP98943999 A EP 98943999A EP 1016072 B1 EP1016072 B1 EP 1016072B1
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Prior art keywords
signal
speech signal
noise
frame
spectral
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EP1016072A1 (de
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Philip Lockwood
Stéphane LUBIARZ
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Nortel Networks France SAS
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Matra Nortel Communications SAS
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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
    • 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
    • G10L21/0264—Noise filtering characterised by the type of parameter measurement, e.g. correlation techniques, zero crossing techniques or predictive techniques

Definitions

  • the present invention relates to techniques digital denoising of speech signals. She relates more particularly to denoising by nonlinear spectral subtraction.
  • This technique allows acceptable denoising to be obtained for strongly voiced signals, but completely distorts the speech signal. Faced with relatively coherent noise, such as that caused by the contact of car tires or the clicking of an engine, the noise may be more easily predictable as the unvoiced speech signal. We then tend to project the speech signal into part of the noise vector space.
  • the method does disregards the speech signal, especially unvoiced speech areas where predictability is scaled down.
  • predict the speech signal from of a reduced set of parameters does not allow taking counts all the intrinsic richness of speech. We understands here the limits of techniques based only on mathematical considerations forgetting the special character of speech.
  • a main object of the present invention is to propose a new denoising technique that takes take into account the characteristics of speech perception through the human ear, allowing denoising effective without deteriorating speech perception.
  • a method as set out in claim 1 and a device as set out in claim 19 are provided.
  • the second subtracted quantity can in particular be limited to the fraction of the estimate increased by the corresponding spectral component of the noise which exceeds the masking curve. This way of proceeding is based on the observation that it is enough to denoise the frequencies of audible noise. Conversely, there is no point in eliminating noise that is masked by speech.
  • the overestimation of the spectral envelope of the noise is generally desirable so that the estimate increased thus obtained is robust to sudden variations noise.
  • this overestimation has usually the downside of distorting the speech signal when it becomes too large. This has the effect to affect the voiced character of the speech signal in removing some of its predictability.
  • This disadvantage is very inconvenient in the conditions of the telephony, because it is during the voicing areas that the speech signal is then the most energetic.
  • the invention allows greatly reduce this drawback.
  • the denoising system shown in FIG. 1 processes a digital speech signal s.
  • the signal frame is transformed in the frequency domain by a module 11 applying a conventional fast Fourier transform (TFR) algorithm to calculate the module of the signal spectrum.
  • TFR fast Fourier transform
  • the frequency resolution available at the output of the fast Fourier transform is not used, but a lower resolution, determined by a number I of frequency bands covering the band [0 , F e / 2] of the signal.
  • a module 12 calculates the respective averages of the spectral components S n, f of the speech signal in bands, for example by a uniform weighting such that:
  • This averaging reduces the fluctuations between the bands by averaging the noise contributions in these bands, which will decrease the variance of the estimator of noise. In addition, this averaging allows a large reduction of the complexity of the system.
  • the averaged spectral components S n, i are addressed to a voice activity detection module 15 and to a noise estimation module 16. These two modules 15, 16 operate jointly, in the sense that degrees of vocal activity ⁇ n, i measured for the different bands by the module 15 are used by the module 16 to estimate the long-term energy of the noise in the different bands, while these long-term estimates B and n, i are used by module 15 to carry out a priori denoising of the speech signal in the different bands to determine the degrees of vocal activity ⁇ n, i .
  • modules 15 and 16 can correspond to the flowcharts represented in the figures 2 and 3.
  • the module 15 proceeds a priori to denoising the speech signal in the different bands i for the signal frame n.
  • This a priori denoising is carried out according to a conventional process of non-linear spectral subtraction from noise estimates obtained during one or more previous frames.
  • ⁇ 1 and ⁇ 2 are delays expressed in number of frames ( ⁇ 1 ⁇ 1, ⁇ 2 ⁇ 0), and ⁇ '/ n, i is a noise overestimation coefficient whose determination will be explained later.
  • the spectral components pp n, i are calculated according to: where ⁇ p i is a floor coefficient close to 0, conventionally used to prevent the spectrum of the denoised signal from taking negative or too low values which would cause musical noise.
  • Steps 17 to 20 therefore essentially consist in subtracting from the spectrum of the signal an estimate, increased by the coefficient ⁇ ' n - ⁇ 1, i , from the spectrum of noise estimated a priori.
  • the module 15 calculates, for each band i (0 ⁇ i ⁇ I), a quantity ⁇ E n, i representing the short-term variation of the energy of the noise-suppressed signal in the band i, as well as long-term value E n, i of the energy of the denoised signal in band i.
  • step 25 the quantity ⁇ E n, i is compared with a threshold ⁇ 1. If the threshold ⁇ 1 is not reached, the counter b i is incremented by one unit in step 26.
  • step 27 the long-term estimator ba i is compared to the value of the smoothed energy E n, i . If ba i ⁇ E n, i , the estimator ba i is taken equal to the smoothed value E n, i in step 28, and the counter b i is reset to zero.
  • the quantity ⁇ i which is taken equal to the ratio ba i / E n, i (step 36), is then equal to 1.
  • step 27 shows that ba i ⁇ E n, i
  • the counter b i is compared with a limit value bmax in step 29. If b i > bmax, the signal is considered to be too stationary to support vocal activity.
  • Bm represents an update coefficient between 0.90 and 1. Its value differs depending on the state of a voice activity detection automaton (steps 30 to 32). This state ⁇ n-1 is that determined during the processing of the previous frame.
  • the coefficient Bm takes a value Bmp very close to 1 so that the noise estimator is very slightly updated in the presence of speech. Otherwise, the coefficient Bm takes a lower value Bms, to allow a more significant update of the noise estimator in silence phase.
  • the difference ba i -bi i between the long-term estimator and the internal noise estimator is compared to a threshold ⁇ 2. If the threshold ⁇ 2 is not reached, the long-term estimator ba i is updated with the value of the internal estimator bi i in step 35. Otherwise, the long-term estimator ba i remains unchanged . This avoids that sudden variations due to a speech signal lead to an update of the noise estimator.
  • the module 15 After having obtained the quantities ⁇ i , the module 15 proceeds to the voice activity decisions in step 37.
  • the module 15 first updates the state of the detection automaton according to the quantity ⁇ 0 calculated for l of the signal band.
  • the new state ⁇ n of the automaton depends on the previous state ⁇ n-1 and on ⁇ 0 , as shown in Figure 4.
  • the module 15 also calculates the degrees of vocal activity ⁇ n, i in each band i ⁇ 1.
  • This function has for example the appearance shown in FIG. 5.
  • Module 16 calculates the band noise estimates, which will be used in the denoising process, using the successive values of the components S n, i and the degrees of voice activity ⁇ n, i . This corresponds to steps 40 to 42 of FIG. 3.
  • step 40 it is determined whether the voice activity detection machine has just gone from the rising state to the speaking state. If so, the last two estimates B and n -1, i and B and n -2, i Previously calculated for each band i ⁇ 1 are corrected according to the value of the previous estimate B and n -3, i .
  • step 42 the module 16 updates the noise estimates per band according to the formulas: where ⁇ B denotes a forgetting factor such as 0 ⁇ B ⁇ 1.
  • Formula (6) shows how the degree of non-binary vocal activity ⁇ n, i is taken into account.
  • the long-term noise estimates B and n, i are overestimated, by a module 45 (FIG. 1), before proceeding to denoising by nonlinear spectral subtraction.
  • Module 45 calculates the overestimation coefficient ⁇ '/ n, i previously mentioned, as well as an increased estimate B and ' / n, i which essentially corresponds to ⁇ '/ n, i . B and n, i .
  • the organization of the overestimation module 45 is shown in FIG. 6.
  • the enhanced estimate B and '/ n, i is obtained by combining the long-term estimate B and n, i and a measure ⁇ B max / n, i the variability of the noise component in band i around its long-term estimate.
  • this combination is essentially a simple sum made by an adder 46. It could also be a weighted sum.
  • the measure ⁇ B max / n, i of the noise variability reflects the variance of the noise estimator. It is obtained as a function of the values of S n, i and of B and n, i calculated for a certain number of previous frames on which the speech channel does not present any vocal activity in the band i. It is a function of deviations S nk, i - B nk, i calculated for a number K of frames of silence (nk ⁇ n). In the example shown, this function is simply the maximum (block 50).
  • the degree of voice activity ⁇ n, i is compared to a threshold (block 51) to decide whether the difference S or - B or , calculated in 52-53, may or may not be loaded into a queue 54 of K locations organized in first-in-first-out (FIFO) mode. If ⁇ n, i does not exceed the threshold (which can be equal to 0 if the function g () has the form of FIG. 5), the FIFO 54 is not supplied, while it is in the opposite case. The maximum value contained in FIFO 54 is then provided as a measure of variability ⁇ B max / n, i .
  • the measure of variability ⁇ B max / n, i can, as a variant, be obtained as a function of the values S n, f (and not S n, i ) and B and n, i .
  • FIFO 54 does not contain S nk, i - B nk, i for each of the bands i, but rather
  • the enhanced estimator B and '/ n, i provides excellent robustness to the musical noises of the denoising process.
  • a first phase of the spectral subtraction is carried out by the module 55 shown in FIG. 1.
  • This phase provides, with the resolution of the bands i (1 i i I I), the frequency response H 1 / n, i of first denoising filter, as a function of the components S n, i and B and n, i and the overestimation coefficients ⁇ '/ n, i .
  • the coefficient ⁇ 1 / i represents, like the coefficient ⁇ p i of formula (3), a floor conventionally used to avoid negative or too low values of the denoised signal.
  • the overestimation coefficient ⁇ ' n, i could be replaced in formula (7) by another coefficient equal to a function of ⁇ ' n, i and an estimate of the signal-to-noise ratio (for example S n, i / B and n, i ), this function decreasing according to the estimated value of the signal-to-noise ratio.
  • This function is then equal to ⁇ ' n, i for the lowest values of the signal-to-noise ratio. Indeed, when the signal is very noisy, it is a priori not useful to reduce the overestimation factor.
  • this function decreases to zero for the highest values of the signal / noise ratio. This protects the most energetic areas of the spectrum, where the speech signal is most significant, the amount subtracted from the signal then tending towards zero.
  • This strategy can be refined by applying it selectively to frequency harmonics pitch of the speech signal when it has voice activity.
  • a second denoising phase is carried out by a module 56 for protecting harmonics.
  • the module 57 can apply any known method of analysis of the speech signal of the frame to determine the period T p , expressed as an integer or fractional number of samples, for example a linear prediction method.
  • the protection provided by the module 56 may consist in carrying out, for each frequency f belonging to a band i:
  • H 2 / n, f 1
  • the quantity subtracted from the component S n, f will be zero.
  • the floor coefficients ⁇ 2 / i express the fact that certain harmonics of the tone frequency f p can be masked by noise, so that n protecting them is useless.
  • This protection strategy is preferably applied for each of the frequencies closest to the harmonics of f p , that is to say for any arbitrary integer.
  • condition (9) the difference between the ⁇ -th harmonic of the real tonal frequency is its estimate ⁇ ⁇ f p (condition (9)) can go up to ⁇ ⁇ ⁇ ⁇ f p / 2.
  • this difference can be greater than the spectral half-resolution ⁇ f / 2 of the Fourier transform.
  • the corrected frequency response H 2 / n, f can be equal to 1 as indicated above, which corresponds to the subtraction of a zero quantity in the context of spectral subtraction, that is to say ie full protection of the frequency in question. More generally, this corrected frequency response H 2 / n, f could be taken equal to a value between 1 and H 1 / n, f depending on the degree of protection desired, which corresponds to the subtraction of an amount less than which would be subtracted if the frequency in question was not protected.
  • S 2 / n, f H 2 n, f .
  • S n, f H 2 n, f .
  • This signal S 2 / n, f is supplied to a module 60 which calculates, for each frame n, a masking curve by applying a psychoacoustic model of auditory perception by the human ear.
  • the masking phenomenon is a principle known from functioning of the human ear. When two frequencies are heard simultaneously, it is possible that one of the two is no longer audible. We say then that it is hidden.
  • the masking curve is seen as the convolution of the spectral spreading function of the basilar membrane in the bark domain with the excitatory signal, constituted in the present application by the signal S 2 / n, f .
  • the spectral spreading function can be modeled as shown in Figure 7.
  • R q depends on the more or less voiced character of the signal.
  • ⁇ designates a degree of voicing of the speech signal, varying between zero (no voicing) and 1 (strongly voiced signal).
  • the denoising system also includes a module 62 which corrects the frequency response of the denoising filter, as a function of the masking curve M n, q calculated by the module 60 and of the increased estimates B and '/ n, i calculated by the module 45.
  • Module 62 decides the level of denoising which must really be reached.
  • the new response H 3 / n, f for a frequency f belonging to the band i defined by the module 12 and to the bark band q, thus depends on the relative difference between the increased estimate B and '/ n, i of the corresponding spectral component of the noise and the masking curve M n, q , as follows:
  • the quantity subtracted from a spectral component S n, f , in the process of spectral subtraction having the frequency response H 3 / n, f is substantially equal to the minimum between on the one hand the quantity subtracted from this spectral component in the spectral subtraction process having the frequency response H 2 / n, f , and on the other hand the fraction of the increased estimate B and '/ n, i of the corresponding spectral component of the noise which, if if necessary, exceeds the masking curve M n, q .
  • FIG. 8 illustrates the principle of the correction applied by the module 62. It schematically shows an example of masking curve M n, q calculated on the basis of the spectral components S 2 / n, f of the noise-suppressed signal, as well as the estimation plus B and '/ n, i of the noise spectrum.
  • the quantity finally subtracted from the components S n, f will be that represented by the hatched areas, that is to say limited to the fraction of the increased estimate B and '/ n, i of the spectral components of the noise which exceeds the curve masking.
  • This subtraction is carried out by multiplying the frequency response H 3 / n, f of the denoising filter by the spectral components S n, f of the speech signal (multiplier 64).
  • TFRI inverse fast Fourier transform
  • FIG. 9 shows a preferred embodiment of a denoising system implementing the invention.
  • This system comprises a certain number of elements similar to corresponding elements of the system of FIG. 1, for which the same reference numbers have been used.
  • modules 10, 11, 12, 15, 16, 45 and 55 provide in particular the quantities S n, i , B and n, i , ⁇ '/ n, i,, B and ' / n, i and H 1 / n, f to perform selective denoising.
  • the frequency resolution of the fast Fourier transform 11 is a limitation of the system of FIG. 1.
  • the frequency causing protection by the module 56 is not necessarily the precise tonal frequency f p , but the frequency closest to it in the discrete spectrum. In some cases, it is then possible to protect harmonics relatively far from that of the tone frequency.
  • the system of FIG. 9 overcomes this drawback thanks to an appropriate conditioning of the speech signal.
  • the sampling frequency of the signal is modified so that the period 1 / f p covers exactly an integer number of sample times of the conditioned signal.
  • This size N is usually a power of 2 for putting implementation of the TFR. It is 256 in the example considered.
  • This choice is made by a module 70 according to the value of the delay T p supplied by the harmonic analysis module 57.
  • the module 70 provides the ratio K between the sampling frequencies to three frequency change modules 71, 72, 73 .
  • the module 71 is used to transform the values S n, i , B and n, i , ⁇ '/ n, i,, B and ' / n, i and H 1 / n, f , relating to the bands i defined by the module 12, in the scale of modified frequencies (sampling frequency f e ). This transformation consists simply in dilating the bands i in the factor K. The values thus transformed are supplied to the module 56 for protecting harmonics.
  • the module 72 performs the oversampling of the frame of N samples provided by the windowing module 10.
  • the conditioned signal frame supplied by the module 72 includes KN samples at the frequency f e . These samples are sent to a module 75 which calculates their Fourier transform.
  • the two blocks therefore have an overlap of (2-K) ⁇ 100%.
  • the autocorrelations A (k) are calculated by a module 76, for example according to the formula:
  • a module 77 then calculates the normalized entropy H, and supplies it to module 60 for the calculation of the masking curve (see SA McClellan et al: “Spectral Entropy: an Alternative Indicator for Rate Allocation?”, Proc. ICASSP'94 , pages 201-204):
  • the normalized entropy H constitutes a measurement of voicing very robust to noise and variations in the tonal frequency.
  • the correction module 62 operates in the same way as that of the system in FIG. 1, taking into account the overestimated noise B and '/ n, i rescaled by the frequency change module 71. It provides the response in frequency H 3 / n, f of the final denoising filter, which is multiplied by the spectral components S n, f of the signal conditioned by the multiplier 64. The components S 3 / n, f which result therefrom are brought back into the time domain by the TFRI 65 module. At the output of this TFRI 65, a module 80 combines, for each frame, the two signal blocks resulting from the processing of the two overlapping blocks delivered by the TFR 75. This combination can consist of a weighted sum Hamming of samples, to form a denoised conditioned signal frame of KN samples.
  • the management module 82 controls the windowing module 10 so that the overlap between the current frame and the next one corresponds to NM. This recovery of NM samples will be required in the recovery sum carried out by the module 66 during the processing of the next frame.
  • the tone frequency is estimated in an average way on the frame.
  • the tonal frequency can vary some little over this period. It is possible to take into account these variations in the context of the present invention, in conditioning the signal so as to obtain artificially a constant tone frequency in the frame.
  • the analysis module 57 harmonic provides the time intervals between the consecutive breaks in speech signal due to closures of the glottis of the intervening speaker for the duration of the frame.
  • Usable methods to detect such micro-ruptures are well known in the area of harmonic signal analysis lyrics.
  • the principle of these methods is to perform a statistical test between two models, one in the short term and the other in the long term. Both models are adaptive linear prediction models.
  • the value of this statistical test w m is the cumulative sum of the posterior likelihood ratio of two distributions, corrected by the Kullback divergence. For a distribution of residuals having a Gaussian statistic, this value w m is given by: where e 0 / m and ⁇ 2/0 represent the residue calculated at the time of the sample m of the frame and the variance of the long-term model, e 1 / m and ⁇ 2/1 likewise representing the residue and the variance of the short term model. The closer the two models are, the more the value w m of the statistical test is close to 0. On the other hand, when the two models are distant from each other, this value w m becomes negative, which indicates a break R of the signal.
  • FIG. 10 thus shows a possible example of evolution of the value w m , showing the breaks R of the speech signal.
  • FIG. 11 shows the means used to calculate the conditioning of the signal in the latter case.
  • the harmonic analysis module 57 is produced so as to implement the above analysis method, and to provide the intervals t r relative to the signal frame produced by the module 10.
  • These oversampling reports K r are supplied to the frequency change modules 72 and 73, so that the interpolations are carried out with the sampling ratio K r over the corresponding time interval t r .
  • the largest T p of the time intervals t r supplied by the module 57 for a frame is selected by the module 70 (block 91 in FIG. 11) to obtain a torque p, ⁇ as indicated in table I.
  • This embodiment of the invention also involves an adaptation of the window management module 82.
  • the number M of samples of the denoised signal to be saved on the current frame here corresponds to an integer number of consecutive time intervals t r between two glottal breaks (see FIG. 10). This arrangement avoids the problems of phase discontinuity between frames, while taking into account the possible variations of the time intervals t r on a frame.

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Claims (19)

  1. Verfahren zur Rauschunterdrückung eines in aufeinanderfolgenden Blöcken behandelten digitalen Sprachsignals (s), wobei:
    Spektralkomponenten (Sn,f,Sn,i) des Sprachsignals an jedem Block berechnet werden;
    für jeden Block majorierte Schätzungen (B and ' / n,i) von Spektralkomponenten von in dem Sprachsignal enthaltenem Rauschen berechnet werden;
    eine spektrale Subtraktion durchgeführt wird, die mindestens einen ersten Subtraktionsschritt aufweist, in dem jeweils von jeder Spektralkomponente (Sn,f) des Sprachsignals an dem Block eine erste Größe subtrahiert wird, die von Parametern abhängt, welche die majorierte Schätzung (B and ' / n,i) der dem Rauschen für den Block entsprechenden Spektralkomponenten beinhalten, so daß Spektralkomponenten (S2 n,f) eines ersten rauschunterdrückten Signals erhalten werden,
    dadurch gekennzeichnet, daß die spektrale Subtraktion des weiteren die folgenden Schritte aufweist:
    Berechnung einer Maskierungskurve (Mn,q) unter Anwendung eines Modells der auditiven Wahrnehmung ausgehend von den Spektralkomponenten (S2 n,f) des ersten rauschunterdrückten Signals;
    Vergleich der majorierten Schätzungen (B and ' / n,i) der Spektralkomponenten des Rauschens für den Block mit der berechneten Maskierungskurve (Mn,q); und
    einen zweiten Subtrahierschritt, in dem jeweils von jeder Spektralkomponente (Sn,f) des Sprachsignals an dem Block eine zweite Größe subtrahiert wird, die von Parametern abhängt, welche einen Abstand zwischen der majorierten Schätzung der entsprechenden Spektralkomponente des Rauschens und der berechneten Maskierungskurve beinhalten.
  2. Verfahren nach Anspruch 1, bei dem die zweite Größe bezüglich einer Spektralkomponente (Sn,f) des Sprachsignals an dem Block im wesentlichen gleich dem Minimum zwischen der entsprechenden ersten Größe und dem Anteil der majorierten Schätzung (B and ' / n,i) der entsprechenden Spektralkomponente des Rauschens ist, welcher die Maskierungskurve (Mn,q) übersteigt.
  3. Verfahren nach einem der Ansprüche 1 oder 2, bei dem eine harmonische Analyse des Sprachsignals durchgeführt wird, um eine Tonfrequenz (fp) des Sprachsignals an jedem Block zu schätzen, wo es eine Stimmaktivität aufweist.
  4. Verfahren nach Anspruch 3, bei dem die Parameter, von denen die ersten zu subtrahierenden Größen abhängen, die geschätzte Tonfrequenz (fp) beinhalten.
  5. Verfahren nach Anspruch 4, bei dem die erste von einer gegebenen Spektralkomponente (Sn,f) des Sprachsignals zu subtrahierende Größe geringer ist, wenn die Spektralkomponente derjenigen Frequenz entspricht, die einem ganzzahligen Vielfachen der geschätzten Tonfrequenz (fp) am nächsten ist, als wenn die Spektralkomponente nicht der Frequenz entspricht, die einem ganzzahligen Vielfachen der geschätzten Tonfrequenz am nächsten ist.
  6. Verfahren nach Anspruch 4 oder 5, bei dem die jeweils von den Spektralkomponenten (Sn,f) des Sprachsignals zu subtrahierenden Größen, welche den Frequenzen entsprechen, die den ganzzahligen Vielfachen der geschätzten Tonfrequenz (fp) am nächsten sind, im wesentlichen Null sind.
  7. Verfahren nach einem der Ansprüche 3 bis 6, bei dem, nach der Schätzung der Tonfrequenz (fp) des Sprachsignals an einem Block das Sprachsignal des Blocks konditioniert wird, indem es bei einer Überabtastfrequenz (fe) überabgetastet wird, die ein Mehrfaches der geschätzten Tonfrequenz ist, und die Spektralkomponenten (Sn,f) des Sprachsignals an dem Block auf der Grundlage des konditionierten Signals (s') berechnet werden, um diese Größen von ihnen zu subtrahieren.
  8. Verfahren nach Anspruch 7, bei dem Spektralkomponenten (Sn,f) des Sprachsignals berechnet werden, indem das konditionierte Signal (s') auf Blöcke von N Abtastproben verteilt wird, welche einer Transformation im Frequenzbereich unterzogen werden, und bei dem das Verhältnis (p) zwischen der Überabtastfrequenz (fe) und der geschätzten Tonfrequenz ein Teiler mit der Zahl N ist.
  9. Verfahren nach Anspruch 7 oder 8, bei dem ein Grad der Stimmhaftigkeit (χ) des Sprachsignals an dem Block ausgehend von einer Berechnung der Entropie (H) der Autokorrelation der auf der Grundlage des konditionierten Signals berechneten Spektralkomponenten geschätzt wird.
  10. Verfahren nach Anspruch 9, bei dem die Spektralkomponenten (S2 n,f), deren Autokorrelation (H) berechnet wird, die auf der Grundlage des konditionierten Signals (s') nach Subtraktion der ersten Größen berechneten sind.
  11. Verfahren nach Anspruch 9 oder 10, bei dem der Grad der Stimmhaftigkeit (χ) ausgehend von einer normalisierten Entropie H mit der Form
    Figure 00420001
    gemessen wird, wobei N die Anzahl von Abtastproben ist, die zur Berechnung der Spektralkomponenten (Sn,f) auf der Grundlage des konditionierten Signals (s') verwendet werden, und A(k) die normalisierte Autokorrelation ist, die definiert ist durch:
    Figure 00420002
    wobei S2 n,f die auf der Grundlage des konditionierten Signals berechnete Spektralkomponente mit Rang f ist.
  12. Verfahren nach Anspruch 11, wobei die Berechnung der Maskierungskurve (Mn,q) den mittels der normalisierten Entropie H gemessenen Grad der Stimmhaftigkeit (χ) einsetzt.
  13. Verfahren nach einem der Ansprüche 3 bis 12, bei dem nach der Behandlung eines jeden Blockes von den durch diese Behandlung zur Verfügung gestellten Abtastproben des rauschunterdrückten Sprachsignals eine Anzahl von Abtastproben (M) aufbewahrt wird, die gleich einem ganzzahligen Vielfachen von Malen des Verhältnisses (Tp) aus der Abtastfrequenz (Fe) und der geschätzten Tonfrequenz (fp) ist.
  14. Verfahren nach einem der Ansprüche 3 bis 12, bei dem die Schätzung der Tonfrequenz des Sprachsignals an einem Block die folgenden Schritte aufweist:
    Schätzen der Zeitintervalle (tr) zwischen zwei aufeinanderfolgenden, während der Dauer des Blocks auftretenden Unterbrechungen (R) des Signals, welche Schließungen der Glottis des Sprechers zuzuordnen sind, wobei die geschätzte Tonfrequenz zu den Zeitintervallen umgekehrt proportional ist;
    Interpolieren des Sprachsignals in den Zeitintervallen, damit das aus dieser Interpolation hervorgehende konditionierte Signal (s') zwischen zwei aufeinanderfolgenden Unterbrechungen ein konstantes Zeitintervall aufweist.
  15. Verfahren nach Anspruch 14, bei dem nach Behandlung eines jeden Blockes von den durch diese Behandlung zur Verfügung gestellten Abtastproben des rauschunterdrückten Sprachsignals eine Anzahl von Abtastproben (M) aufbewahrt wird, welche einer ganzzahligen Anzahl von geschätzten Zeitintervallen (tr) entspricht.
  16. Verfahren nach einem der vorhergehenden Ansprüche, bei dem im Spektralbereich Werte eines Rauschabstandes geschätzt werden, den das Sprachsignal (s) an jedem Block aufweist, und bei dem die Parameter, von denen die ersten zu subtrahierenden Größen abhängen, die geschätzten Werte des Rauschabstandes beinhalten, wobei die von jeder Spektralkomponenten (Sn,f) des Sprachsignals an dem Block zu subtrahierende erste Größe eine abnehmende Funktion des entsprechenden geschätzten Werts des Rauschabstandes ist.
  17. Verfahren nach Anspruch 16, bei dem die Funktion für die höchsten Werte des Rauschabstandes nach Null hin abnimmt.
  18. Verfahren nach einem der vorhergehenden Ansprüche, bei dem auf das Ergebnis der spektralen Subtraktion eine Transformation in den Zeitbereich angewendet wird, um ein rauschunterdrücktes Sprachsignal (s3) zu erstellen.
  19. Vorrichtung zur Rauschunterdrückung eines Sprachsignals, mit Behandlungseinrichtungen, die dazu konzipiert sind, ein Verfahren nach einem der vorhergehenden Ansprüche durchzuführen.
EP98943999A 1997-09-18 1998-09-16 Verfahren und vorrichtung zur rauschunterdrückung eines digitalen sprachsignals Expired - Lifetime EP1016072B1 (de)

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