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SPEECH ENHANCEMENT WITH LOW-ORDER NON-NEGATIVE MATRIX FACTORIZATION
SPEECH ENHANCEMENT WITH LOW-ORDER NON-NEGATIVE MATRIX FACTORIZATION
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机译：低阶非负矩阵分解的语音增强
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摘要
A system is provided that employs a statistical approach to semi-supervised speech enhancement with a low-order non-negative matrix factorization (“NMF”). The system enhances noisy speech based on multiple dictionaries with dictionary atoms derived from the same clean speech samples and generates an enhanced speech representation of the noisy speech by combining, for each dictionary, a clean speech representation of the noisy speech generated based on a NMF using the dictionary atoms of the dictionary. The system generates frequency-domain (“FD”) clean speech sample representations of the clean speech samples, for example, using a Fourier transform. To generate each dictionary, the system generates a dictionary-unique initialization of the dictionary atoms and the activations and performs a NMF of the FD clean speech samples.
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