Non-negative Matrix Factorization with Linear Constraints for Single-Channel Speech Enhancement
This work addresses speech enhancement for audio processing applications, but it is incremental as it builds on existing NMF frameworks with specific constraints.
The paper tackled single-channel speech enhancement by integrating a sinusoidal speech model with non-negative matrix factorization using linear constraints, and it showed improved signal-to-noise ratio compared to state-of-the-art methods in experiments on TIMIT corpus with various noises.
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.