Data processing: speech signal processing – linguistics – language – Speech signal processing – For storage or transmission
Reexamination Certificate
1997-01-09
2001-11-27
Hudspeth, David R. (Department: 2741)
Data processing: speech signal processing, linguistics, language
Speech signal processing
For storage or transmission
C704S228000
Reexamination Certificate
active
06324502
ABSTRACT:
BACKGROUND
The present invention relates to a noisy speech parameter enhancement method and apparatus that may be used in, for example noise suppression equipment in telephony systems.
A common signal processing problem is the enhancement of a signal from its noisy measurement. This can for example be enhancement of the speech quality in single microphone telephony systems, both conventional and cellular, where the speech is degraded by colored noise, for example car noise in cellular systems.
An often used noise suppression method is based on Kalman filtering, since this method can handle colored noise and has a reasonable numerical complexity. The key reference for Kalman filter based noise suppressors is Reference [
1
]. However, Kalman filtering is a model based adaptive method, where speech as well as noise are modeled as, for example, autoregressive (AR) processes. Thus, a key issue in Kalman filtering is that the filtering algorithm relies on a set of unknown parameters that have to be estimated. The two most important problems regarding the estimation of the involved parameters are that (i) the speech AR parameters are estimated from degraded speech data, and (ii) the speech data are not stationary. Thus, in order to obtain a Kalman filter output with high audible quality, the accuracy and precision of the estimated parameters is of great importance.
SUMMARY
An object of the present invention is to provide an improved method and apparatus for estimating parameters of noisy speech. These enhanced speech parameters may be used for Kalman filtering noisy speech in order to suppress the noise. However, the enhanced speech parameters may also be used directly as speech parameters in speech encoding.
The above object is solved by a method of enhancing noisy speech parameters that includes the steps of determining a background noise power spectral density estimate at M frequencies, where M is a predetermined positive integer, from a first collection of background noise samples; estimating p autoregressive parameters, where p is a predetermined positive integer significantly smaller than M, and a first residual variance from a second collection of noisy speech samples; determining a noisy speech power spectral density estimate at said M frequencies from said p autoregressive parameters and said first residual variance; determining an enhanced speech power spectral density estimate by subtracting said background noise spectral density estimate multiplied by a predetermined positive factor from said noisy speech power spectral density estimate; and determining r enhanced autoregressive parameters, where r is a predetermined positive integer, and an enhanced residual variance from said enhanced speech power spectral density estimate.
The above object also is solved by an apparatus for enhancing noisy speech parameters that includes a device for determining a background noise power spectral density estimate at M frequencies, where M is a predetermined positive integer, from a first collection of background noise samples; a device for estimating p autoregressive parameters, where p is a predetermined positive integer significantly smaller than M, and a first residual variance from a second collection of noisy speech samples; a device for determining a noisy speech power spectral density estimate at said M frequencies from said p autoregressive parameters and said first residual variance; a device for determining an enhanced speech power spectral density estimate by subtracting said background noise spectral density estimate multiplied by a predetermined factor from said noisy speech power spectral density estimate; and a device for determining r enhanced autoregressive parameters, where r is a predetermined positive integer, and an enhanced residual variance from said enhanced speech power spectral density.
REFERENCES:
patent: 4618982 (1986-10-01), Horvath
patent: 4628529 (1986-12-01), Borth et al.
patent: 5295225 (1994-03-01), Kane et al.
patent: 5319703 (1994-06-01), Drory
patent: 5579435 (1996-11-01), Jansson
patent: WO95/15550 (1995-06-01), None
Patent Abstracts of Japan, vol. 14, No. 298, P-1068, JP, A, 2-93697 (Apr. 4, 1990).
S.A. Dimino et al., “Estimating the Energy Contour of Noise-Corrupted Speech Signals by Autocorrelation Extrapolation,” IEEE Robotics, Vision and Sensors, Signal Processing and Control, pp. 2015-2018 (Nov. 15-19, 1993).
W. Du et al., “Speech Enhancement Based on Kalman Filtering and EM Algorithm,” IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, vol. 1, pp. 142-145 (May 9-10, 1991).
D.K. Freeman et al., “The Voice Activity Detector for the Pan-European Digital Cellular Mobile Telephone Service,” 1989 IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 1, pp. 489-502 (May 23-26, 1989).
J.D. Gibson et al., “Filtering of Colored Noise for Speech Enhancement and Coding,” IEEE Transactions on Signal Processing, vol. 39, No. 8, pp. 1732-1742 (Aug. 1991).
B-G Lee et al., “A Sequential Algorithm for Robust Parameter Estimation and Enhancement of Noisy Speech,” Proceedings of the International Symposium on Circuits and Systems (ISCS), vol. 1, pp. 243-246 (May 3-6, 1993).
K.Y. Lee et al., “Robust Estimation of AR Parameters and Its Application for Speech Enhancement,” IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 1, pp. I-309 through I-312 (Mar. 23-26, 1992).
J.S. Lim et al., “All-Pole Modeling of Degraded Speech,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-26, No. 3, pp. 197-210 (Jun. 1978).
T. Söderström et al., “An Indirect Prediction Error Method for System Identification,” Automatica, vol. 27, No. 1, pp. 183-188 (Jan. 1991).
Boll “Suppression of Acoustic Noise In Speech Using Spectral Subtraction” IEEE, transactions vol. 2, Apr. 1979.*
Hansen et al “Constrained Iterative Speech Enhancement with Application to Speech Recognition” IEEE transactions vol. 39, Apr. 1991.*
Deller et al. “Discrete-Time Processing of Speech Signals” Prentice Hall, pp. 231, 273, 285, 297-298, 342, 343, 507-513, 521, 527, 1993.*
Deller et al, Discrete-Time Processing of Speech Signals, Prentice Hall, pp. 511-513, 1987.
Handel Peter
Sörqvist Patrik
Burns Doane Swecker & Mathis L.L.P.
Hudspeth David R.
Telefonaktiebolaget LM Ericsson (publ)
Zintel Harold
LandOfFree
Noisy speech autoregression parameter enhancement method and... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Noisy speech autoregression parameter enhancement method and..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Noisy speech autoregression parameter enhancement method and... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2601512