Recognize and separate approach for speech denoising using nonnegative matrix factorization
Fahad Sohrab, Hakan Erdoğan · 2015
This paper proposes a novel approach for denoising single-channel noisy speech signals. A speech dictionary and multiple noise dictionaries are trained using nonnegative matrix factorization (NMF). After observing the mixed signal, first the type of noise in the mixed signal is identified. The magnitude spectrogram of the noisy signal is decomposed using NMF with the concatenated trained dictionaries of noise and speech. Our results indicate that recognizing the noise type from the mixed signal and using the corresponding specific noise dictionary provides better results than using a general noise dictionary in the NMF approach. We also compare our algorithm with other state-of-the-art denoising methods and show that it has better performance than the competitors in most cases.