Modeling speech signals in the time-frequency domain using GARCH

Authors:
Israel Cohen
Affiliations:
Department of Electrical Engineering, Technion Israel Institute of Technology, Technion City, Haifai 32000, Israel
Venue:
Signal Processing
Year:
2004

Citing 2
Cited 2

Speech spectral modeling and enhancement based on autoregressive conditional heteroscedasticity models

Signal Processing
Hidden Markov processes

IEEE Transactions on Information Theory

Speech spectral modeling and enhancement based on autoregressive conditional heteroscedasticity models

Signal Processing
Wavelet based speech enhancement technique using median function thresholding

MATH'08 Proceedings of the American Conference on Applied Mathematics

Quantified Score

Hi-index	0.08

Visualization

Abstract

In this paper, we introduce a novel modeling approach for speech signals in the short-time Fourier transform (STFT) domain. We define the conditional variance of the STFT expansion coefficients, and model the one-frame-ahead conditional variance as a generalized autoregressive conditional heteroscedasticity (GARCH) process. The proposed approach offers a reasonable model on which to base the estimation of the variances of the STFT expansion coefficients, while taking into consideration their heavy-tailed distribution.