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In this letter, we propose a tensor factorization approach for multichannel speech enhancement, which is very successful even when the noise level is high. Specifically, we extend the well-known subspace approach to arbitrary orders and present the higher order subspace approach for multichannel speech enhancement. Unlike previous algorithms, the proposed(More)
In this paper, we propose a robust time-frequency decomposition (RTFD) model to restore audio signals degraded by sparse impulse noise mixed with small dense Gaussian noise. This kind of noise is very common especially in old-time recordings. The proposed RTFD model is based on the observation that these degraded audio signals mainly contain four parts,(More)
Recently, researchers have proposed to represent the observed multichannel speech data as a 3-D tensor and then directly reduce the noise level in the time domain. For example, a higher order subspace algorithm (HOSA) was proposed for the reduction of spatially white noise (i.e., the noise added to different sensors is mutually uncorrelated) and yielded an(More)
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