Data processing: speech signal processing – linguistics – language – Speech signal processing – Recognition
Reexamination Certificate
2000-06-20
2003-11-04
Chawan, Vijay (Department: 2654)
Data processing: speech signal processing, linguistics, language
Speech signal processing
Recognition
C704S240000, C704S226000, C704S227000, C704S210000, C704S214000, C381S094300, C381S094100
Reexamination Certificate
active
06643619
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to a method for reducing interference in acoustic signals using of an adaptive filtering method involving spectral subtraction.
RELATED TECHNOLOGY
Use of an adaptive filtering method involving spectral subtraction for reducing interference is described, for example, in Boll, “Suppression of Acoustic Noise in Speech using Spectral Subtraction”; IEEE Trans. Acoust. Speech a. Signal Processing, Vol. ASSP-27, No. 2, p. 113-120, 1979.
The improvement of speech signals is a central part of the current research in the field of communications technology, for example, also in fields of application such as handsfree talking in vehicles or in automatic speech recognition. For the improvement of speech signals, it is above all essential to reduce the disturbing noises.
A method frequently used for reducing noise is the “spectral subtraction” whose basic principles are described, for example, Boll supra.
The spectral subtraction is an adaptive filter which ascertains (learns) an average value of the noise spectrum during speech pauses, and continually subtracts this spectrum from the disturbed speech signal. The exact embodiment of the subtraction of the interference spectrum can be varied depending on the requirement. Individual examples are depicted in the following.
As a rule, the filtering method of spectral subtraction is carried out within the frequency range. The signals a transformed segmentwise into the frequency range by an FFT (Fast Fourier Transform). The corresponding segments of the signal in the time range are half overlapped, and are previously multiplied by a Hanning window. The synthesis is carried out after the filtering (multiplication) and subsequent inverse transformation by the “overlap-add method”.
In Linhard, “Adaptive Gerauschreduktion im Frequenzbereich bei Sprachutbertragung”; Dissertation Universitat Karslruhe, 1988 [Adaptive Noise Reduction within the Frequency Range During Speech Transmission; dissertation, University of Karlsruhe, 1988] three standard filter curves are depicted as exemplary embodiments for the spectral subtraction:
Power Subtraction:
H
(
k,i
)=max(
b
, {square root over (1
−&agr;·NIR
)})
(1)
Wiener Filter:
H
(
k,i
)=max(
b
, (1
−&agr;·NIR
)) (2)
Magnitude Subtraction:
H
(
k,i
)=max(
b
, (1
−&agr;·{square root over (NIR)}
)) (3)
k and i designate the discrete time and the discrete frequency. NIR is the noise-input ratio.
NIR=E[N
(
i
)
2
]/(
S
(
k,i
)+
N
(
k,i
))
2
(4)
S and N designate the speech signal or the interference, respectively; a is an overestimation factor by which the noise can be overestimated, and b is the “spectral floor” which represents the minimum of the filtering function. Here, it is assumed that the speech pauses can be detected sufficiently accurately. Consequently, it is possible to calculate estimation value E[N(i)
2
] and, from that, NIR. Simple standard methods use a value 1<=a<4 and 0.1<b<0.3 for reducing the remaining residual noise, the so-called “musical tones”. A disadvantage in doing this, however, is always an undesired but inevitable compromise between residual noise suppression and speech distortion. A suppression of the ‘musical tones’ which is markedly improved compared to the method depicted in to Linhard, supra, is proposed in Ephraim, Malah, “Speech Enhancement using a Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator”; IEEE Trans. Acoust. Speech a. Signal Processing, Vol. ASSP-32, No. 6, p. 1109-1121, 1984, which is hereby incorporated by reference herein. There, information on an a priori (earlier) and an a posteriori (later) signal-to-noise ratio is utilized for modifying the filter curves, here Bessel functions. A priori and a posteriori signal-to-noise ratios Rprio and Rpost are here calculated as
X
(
k,i
)=
S
(
k,i
))+
N
(
k,i
) (5)
Rpost
(
k,i
)=|
X
(
k,i
)|
2
/E[N
(
i
)
2
]−1 (6)
Rprio
(
k,i
)=(1
−d
)
P[Rpost
(
k,i
)]+
d\H
(
k
−1
,i
)
X
(
k
−1
,i
)|
2
/E[N
(
i
)
2
] (7)
Where d is a smoothing constant, and 0.99<d<1.P[ ] is a projection by which negative components are set to zero. By selecting d close to value one, the transient oscillation into a beginning, high-energy speech signal is slowed down. Projection P results in a smoothing out of the residual noise during speech pauses. However, this is not required for preventing musical tones, and may have an unnatural effect. Moreover, the outlay required for implementing this method is considerable and, in the case of speech signals, an audible reverberation characteristic may occur. The reverberation characteristic ensues from the fact that H(k−1,i) und X(k−1,i) enter into the current filter curve from previous segment k−1 via Rprio at instant k.
SUMMARY OF THE INVENTION
Therefore, an object of the present invention is to provide a method which, on one hand, allows interferences in acoustic signals, particularly in speech signals to be markedly reduced using the adaptive filtering method of spectral subtraction without causing an essential corruption of the signal such as reverberation, and which, on the other hand, allows the computational requirement to be considerably reduced relative to already known and, with regard to the quality of the achieved signal improvement, comparable methods.
The present invention provides method for reducing interference in acoustic signals by using an adaptive filtering method involving spectral subtraction, in which achieved according to the present invention in that the calculation of an, in each case current characteristic value H(k,i) of the used filtering function considering information on an a priori signal-to-noise ratio is carried out in such a manner that characteristic values H(k−j,i), j=1, . . . , N of the filtering function from preceding time segments k−j are used as the sole information on the a priori signal-to-noise ratio, however, at least one characteristic value H(k−j
0
,i), j
0
&egr;1, . . . , N of the filtering function from a preceding time segment k−j
0
is used; and that the characteristic curve of the filtering function is split into two parts and has a break edge such
that the filtering for heavily disturbed signals X(k,i) having a high noise-input ratio NIR(k,i) results in a signal-independent strong damping; and
that the filtering for slightly disturbed signals X(k,i) having a low noise-input ratio NIR(k,i) results in a signal-dependent low damping.
The advantages of such an embodiment are that, first of all, the acoustic quality of the noise-suppressed signal is improved to a greater extent than in the method described under Ephraim, supra, namely by feeding back one or a plurality of characteristic values H(k−j,i) alone for considering information preceding in time in contrast to the feeding back of characteristic value H(k−1,i) and disturbed signal X(k−1,i) proposed in Ephraim, supra; and, by decoupling or decorrelating H and X by considering H(k−j,i) and X(k, i) at different instants k−j and k according to the present invention, as a result of which reverberation and echos are minimized; and in that, during time segments having a high noise-input ratio NIR(k,i), for example, background noises during speech pauses, the signals are damped only independently of the signal but reproduced naturally whereas in Ephraim, supra, they are smoothed and corrupted in a manner that they are unnatural; and in that the transient oscillation of the characteristic curve into a beginning signal takes place markedly faster than in Ephraim, supra, where the transient oscillation is strongly slowed down by introducing smoothing constant d and setting its value close to 1; and that, secondly, the computational requirement is considerably smaller t
Haulick Tim
Linhard Klaus
Chawan Vijay
Kenyon & Kenyon
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