Filtering of colored noise for speech enhancement and coding

Gibson, J.D.; Koo, Boneung; Gray, Steven D.

doi:10.1109/78.91144

Cited by 275 publications

(222 citation statements)

References 25 publications

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“…Several approaches were studied for speech enhancement in additive noise (Boll, 1979;Gibson et al, 1991;Cheng and O'Shaughnessy, 1991;Ephraim, 1992;Ephraim and Van Trees, 1995;Le Bouquin, 1996;Lee and Shirai, 1996). Many of these studies have focussed on enhancement based www.elsevier.nl/locate/specom Speech Communication 28 (1999) 25±42 on attempts to suppress noise (Boll, 1979;Ephraim and Van Trees, 1995;Le Bouquin, 1996).…”

Section: Introductionmentioning

confidence: 99%

Speech enhancement using linear prediction residual

Yegnanarayana

Avendaño

Heřmanský

et al. 1999

Speech Communication

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In this paper we propose a method for enhancement of speech in the presence of additive noise. The objective is to selectively enhance the high signal-to-noise ratio (SNR) regions in the noisy speech in the temporal and spectral domains, without causing signi®cant distortion in the resulting enhanced speech. This is proposed to be done at three di erent levels. (a) At the gross level, by identifying the regions of speech and noise in the temporal domain. (b) At the ®ner level, by identifying the regions of high and low SNR portions in the noisy speech. (c) At the short-time spectrum level, by enhancing the spectral peaks over spectral valleys. The basis for the proposed approach is to analyze linear prediction (LP) residual signal in short (1±2 ms) segments to determine whether a segment belongs to a noise region or speech region. In the speech regions the inverse spectral¯atness factor is signi®cantly higher than in the noisy regions. The LP residual signal enables us to deal with short segments of data due to uncorrelatedness of the samples. Processing of noisy speech for enhancement involves mostly weighting the LP residual signal samples. The weighted residual signal samples are used to excite the time-varying all-pole ®lter to produce enhanced speech. As the additive noise level in the speech signal is increased, the quality of the resulting enhanced speech decreases progressively due to loss of speech information in the low SNR, high noise regions. Thus the degradation in performance of enhancement is graceful as the overall SNR of the noisy speech is decreased. Ó

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Section: Introductionmentioning

confidence: 99%

Speech enhancement using linear prediction residual

Yegnanarayana

Avendaño

Heřmanský

et al. 1999

Speech Communication

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“…However, variations of measurement noise should be evaluated from empirical noise data, as it is difficult to obtain data in most cases. If the measurement noise statistics are unknown, the Kalman filter can not guarantee optimal resolution of the problem [3]. To solve this problem, we propose an adaptive filtering method based on the Kalman filter using neural networks.…”

Section: Introductionmentioning

confidence: 99%

Noise Reduction in Multiple Rfid Sensor Systems Used in Aerospace Engineering

LUCHIAN¹,

Boșcoianu²,

BOŞCOIANU³

2017

AFASES 2017

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“…The inclusion of colored noise and impulsive noise is an extension to the system we presented in [8]. Our system does not require an extended Kalman filter as used in [7] or an iterated parameter estimation procedure [6]. It is computationally efficient and may be implemented for real-time operation in the audio frequency range using integrated digital signal processors.…”

Section: Introductionmentioning

confidence: 99%

“…Likewise, the noise model (3) can be written in state-space form v(n + 1 ) = B(n)v(n) + ; w(n) 0 : : : 0 T (6) with the q q noise state matrix…”

Section: Kalman Fixed-lag Smoother For Ar Signal and Noise Modelsmentioning

confidence: 99%

“…A stalling phenomenon may be observed resulting in intermittent signal segments and in slow convergence (as compared to the convergence speed at higher SNRs). The second approach is based on an iterated parameter estimation and is suited for colored noise disturbances [6]. It is an extension of an algorithm described in [1].…”

Section: Adaptive Kalman Filter and Ar Parameter Estimationmentioning

confidence: 99%

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Adaptive Kalman smoothing of AR signals disturbed by impulses and colored noise

Doblinger

1998 IEEE Symposium on Advances in Digital Filtering and Signal Processing. Symposium Proceedings (Cat. No.98EX185)

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In this paper, we describe a new and computationally efficient adaptive system for the enhancement of autoregressive (AR) signals which are disturbed by additive white or colored noise and impulsive noise. The system is comprised of an adaptive Kalman filter operating as a fixed lag smoother and a subsystem for AR parameter estimation. A superior performance is achieved by implementing a feedback loop between the Kalman filter output and the parameter estimation. Accordingly, the AR parameters are obtained from the enhanced signal and the influence of the disturbing noise on the parameter estimation is damped down. Impulsive noise is suppressed by an outlier detection scheme and by freezing the Kalman filter update during presence of impulses. The subsystem for AR parameter estimation can operate in a block processing mode or on a sample per sample basis. Another advantage of the adaptive Kalman filter is its tracking capability for short-time stationary signals.

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Filtering of colored noise for speech enhancement and coding

Cited by 275 publications

References 25 publications

Speech enhancement using linear prediction residual

Speech enhancement using linear prediction residual

Noise Reduction in Multiple Rfid Sensor Systems Used in Aerospace Engineering

Adaptive Kalman smoothing of AR signals disturbed by impulses and colored noise

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