Abstract: This paper investigates a non-negative matrix factorization (NMF)-based approach to the semi-supervised single-channel speech enhancement problem where only non-stationary additive noise signals are given. The NMF spectral basis matrices for both speech and noise are obtained in a manner of supervised learning, and thus the performance of their associated NMF speech enhancement degrades as the speaker and/or noise characteristics are not matched for the learning and evaluation environment. The experimental evaluation was made on TIMIT corpus mixed with various types of noise. It has been shown that the proposed method outperforms some of the state-of-the-art noise suppression techniques in terms of signal-to-noise ratio.
Keywords: non-negative matrix factorization (NMF), noise suppression techniques, signal-to-noise ratio.
Title: Non-stationary additive noise signal filtration process in NMF based approach for the single-channel speech enhancement
Author: Ravi Shankar Prasad, Pradeep singh yadav
International Journal of Electrical and Electronics Research
ISSN 2348-6988 (online)
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