  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 proposed method
relies on sinusoidal model of speech production which is integrated inside NMF
framework using linear constraints on dictionary atoms. This method is further
developed to regularize harmonic amplitudes. Simple multiplicative algorithms
are presented. 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.
