HIGHLIGHTS
- who: Yang Xiang from the (UNIVERSITY) have published the research work: A speech enhancement algorithm based on a non-negative hidden Markov model and Kullback-Leibler divergence, in the Journal: (JOURNAL)
- what: The authors propose supervised single-channel method that combines (KL) non-negative matrix factorization (NMF) and hidden Markov model (NMF-HMM). In , an HMM-DNN NMF speech enhancement algorithm was proposed, which applied a clustering method to acquire the HMM-based basis matrix and used the Viterbi algorithm to obtain the ideal state label for the DNN training. The authors propose a novel . . .
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