Reduction of Additive Noise in the Digital Processing of Speech

Avner HalevyAMSC 664

Final Presentation May 2009

Dr. Radu BalanDepartment of Mathematics

Center for Scientific Computation and Math. Modeling

1

Background and Goal

• Digital speech signals

• Clean signals are degraded by noise– From environment

– From processing (recording, transmitting, etc.)

• Speech enhancement: reduce noise without distorting original (clean) signal

Scope and Goal

• Focus on additive white Gaussian noise

• Improve quality and intelligibility

• Use short time analysis (frames)

• Explore two algorithms:– Spectral Subtraction

– Iterative Wiener Filtering

• Test objectively using TIMIT

3

Spectral Subtraction

• Estimate the magnitude of the noise spectrum when speech is absent

• Subtract the estimate from the magnitude of the spectrum of the noisy signal

• Keep the noisy phase

• Inverse Fourier to obtain enhanced signal in time domain

4

Spectral Subtraction

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Wiener Filter - Theory

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Iterative Wiener - Theory

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Implementation, Validation, Testing

• Both algorithms were implemented in MATLAB on a standard PC

• Implementation validated using zero noise, stability ensured using small nonzero noise

• Testing based on TIMIT

• Main objective measure used: segmental SNR

TestingC

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Comparison of SS and IW

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Comparison of SS and IW

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Spec

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Comparison of Results

12

Comparison of Results

Comparison of Results

Convergence of IW: Female, 1st sent.

Convergence of IW: Female, 2nd sent.

Convergence of IW: Male, 1st sent.

Summary of Iteration Results

Conclusions

• Objectively, IW produced better results

• Subjectively, both methods suffer from severe artifacts (musical noise)

• Possible improvement by more sophisticated noise estimation

Questions?

Bibliography

• [1] Deller, J., Hansen, J., and Proakis, J. (2000) Discrete Time Processing of Speech Signals, New York, NY: Institute of Electrical and Electronics Engineers

• [2] Quatieri, T. (2002) Discrete Time Speech Signal Processing, Upper Saddle River, NJ: Prentice Hall

• [3] Loizou, P. (2007) Speech Enhancement: Theory and Practice, Boca Raton, FL: Taylor & Francis Group

• [4] Rabiner, L., Schafer, R. (1978) Digital Processing of Speech Signals, Englewood Cliffs, NJ: Prentice Hall

• [5] Vaseghi, S. (1996) Advanced Signal Processing and Digital Noise Reduction, Stuttgart, Germany: B.G. Teubner

• [6] Lim, J. and Oppenheim, A.V. (1978) All-pole modeling of degraded speech, IEEE Trans. Acoust. Speech Signal Process., 26(3), 197-200

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