Transient noise reduction using nonnegative matrix factorization

Reducing highly non-stationary transient noise, such as keyboard typing noise, remains a challenging problem for many singlechannel speech enhancement algorithms. This paper proposes two approaches based on nonnegative matrix factorization (NMF) and probabilistic latent component analysis for transient noise reduction using a pre-trained transient noise dictionary and a universal speaker-independent speech dictionary. In addition, we develop an NMF-based speech enhancement scheme to simultaneously reduce transient and non-transient background noise, in which a low-dimensional dictionary is learnt from the noisy observations to model the background noise. We exploit the temporal dependencies of speech and background noise to design and apply informative priors via a probabilistic framework, while ignoring the temporal dynamics of the transient noise. Experimental results show that the proposed algorithms can improve the perceptual evaluation of speech quality (PESQ) up to 1.2 MOS for the keyboard typing noise.