180 likes | 356 Views
Reduction of Additive Noise in the Digital Processing of Speech. Avner Halevy AMSC 663 Mid Year Progress Report December 2008 Professor Radu Balan. Brief Reminder. Goal: reduce additive white Gaussian noise degrading a speech signal Use short time analysis (frames) Algorithms:
E N D
Reduction of Additive Noise in the Digital Processing of Speech Avner Halevy AMSC 663 Mid Year Progress Report December 2008 Professor RaduBalan
Brief Reminder • Goal: reduce additive white Gaussian noise degrading a speech signal • Use short time analysis (frames) • Algorithms: • Spectral subtraction (done) • Iterative Wiener filtering (next) • Test objectively in lab conditions
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 • Underlying statistical assumptions
Spectral Subtraction Implemented • Implementation issues • Choice of analysis and synthesis windows • Ensuring nonnegative magnitude • Estimation of noise spectrum • Choice of exponent • Validation • Variations
SS – Preliminary Results Clean Frequency (Hz) Time (sec)
SS – Preliminary Results Clean Noisy
SS – Preliminary Results SS – Preliminary Results Noisy Enhanced
Evaluation of Exponent Values tested: p = .1, .5, 1, 1.5, 2, 3, 4, 5
Testing • TIMIT – database of 6300 sentences for evaluation of speech processing algorithms • Segmental SNR • Segmental “Filtering” SNR • Segmental “Filtering” distortion • Future: Perceptual Evaluation of Speech Quality (PESQ) – telecomm standard
Shortcomings of SS • Using the noisy phase (in low SNR) • “musical noise” artifacts caused by • Error in noise spectrum estimation • Flooring of negative components • Fluctuations in signal spectrum • Solutions: • Over subtraction • Smoothing of signal spectrum
Dealing with “Musical Noise” Over subtraction Smoothing signal spectrum
Next Steps • Test more signals • Evaluate using PESQ • Implement Wiener filtering • Further testing • Compare performance
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