IEEE Transactions on Information Theory
Source separation using single channel ICA
Signal Processing
The role of high frequencies in convolutive blind source separation of speech signals
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Bayesian marginal statistics for speech enhancement using log Gabor wavelet
International Journal of Speech Technology
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We address the problem of Speech Enhancement in a setting where parts of the time-frequency content of the speech signal are missing. In telephony, speech is band-limited and the goal is to reconstruct a wide-band version of the observed data. Quite differently, in Blind Source Separation scenarios, information about a source can be masked by noise or other sources. These masked components are “gaps” or missing source values to be “filled in”. We propose a framework for unitary treatment of these problems, which is based on a relatively simple “spectrum restoration” procedure. The main idea is to use Independent Component Analysis as an adaptive, data-driven, linear representation of the signal in the speech frame space, and then apply a vector-quantization-based matching procedure to reconstruct each frame. We analyze the performance of the reconstruction with objective quality measures such as log-spectral distortion and Itakura-Saito distance.