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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...
As a promising technique, sparse representation has been extensively investigated in signal processing community. Recently, sparse representation is widely used for speech processing in noisy environments; however, many problems need to be solved because of the particularity of speech. One assumption for speech denoising with sparse representation is that the representation of speech over the dictionary...
We present a novel noise reduction strategy that is inspired by the physiology of the auditory brainstem. Following the hypothesis that neurons code sound based on fractional derivatives we develop a model in which sound is transformed into a ‘neural space’. In this space sound is represented by various fractional derivatives of the envelopes in a 22 channel filter bank. We demonstrate that noise...
We present a semi-supervised source separation methodology to denoise speech by modeling speech as one source and noise as the other source. We model speech using the recently proposed non-negative hidden Markov model, which uses multiple non-negative dictionaries and a Markov chain to jointly model spectral structure and temporal dynamics of speech. We perform separation of the speech and noise using...
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