Modified safia utilizing aggregated microphones
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
Noise estimation using mean square cross prediction error for speech enhancement
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Speech enhancement in short-wave channel based on empirical mode decomposition
CSR'06 Proceedings of the First international computer science conference on Theory and Applications
Speech enhancement in short-wave channel based on ICA in empirical mode decomposition domain
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
| Hi-index | 0.01 |
An improved spectral subtraction algorithm for enhancing speech corrupted by additive wideband noise is described. The artifactual noise introduced by spectral subtraction that is perceived as musical noise is 7 dB less than that introduced by the classical spectral subtraction algorithm of Berouti et al. (1979). Speech is decomposed into voiced and unvoiced sections. Since voiced speech is primarily stochastic at high frequencies, the voiced speech is high-pass filtered to extract its stochastic component. The cut-off frequency is estimated adaptively. Multi-window spectral estimation is used to estimate the spectrum of stochastically voiced and unvoiced speech, thereby reducing the spectral variance. A low-pass filter is used to extract the deterministic component of voiced speech. Its spectrum is estimated with a single window. Spectral subtraction is performed with the classical algorithm using the estimated spectra. Informal listening tests confirm that the new algorithm creates significantly less musical noise than the classical algorithm.