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Many speech enhancement algorithms that deal with noise reduction are based on a binary masking decision (termed as the hard decision), which may cause some regions of the synthesized speech to be discarded. In view of the problem, a soft decision is often used as an optimal technique for speech restoration. In this paper, considering a new fashion of speech and noise models, we present two model-based...
In this paper, we propose a novel noise masking method based on Computational Auditory Scene Analysis by using an adaptive factor. Although it has succeeded in the field of speech separation and speech enhancement to some extent, the usage of fixed thresholds used for segregation and labeling heavily affects the processing performance. Focusing on this issue, the proposed method utilizes the Normalized...
Speech enhancement based on hidden Markov model (HMM) and the minimum mean square error (MMSE) criterion in Mel-frequency domain is generally considered as a weighted-sum filtering of the noisy speech. The weights of filters are often estimated by the HMM of noisy speech, and the estimation of filters usually requires an inverse operation from the Mel-frequency to the spectral domain which often causes...
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