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Unsupervised Learning for Monaural Source Separation Using Maximization–Minimization Algorithm with Time–Frequency Deconvolution †
This paper presents an unsupervised learning algorithm for sparse nonnegative matrix factor time–frequency deconvolution with optimized fractional [Formula: see text]-divergence. The [Formula: see text]-divergence is a group of cost functions parametrized by a single parameter [Formula: see text]. T...
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| Publicado no: | Sensors (Basel) |
|---|---|
| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
MDPI
2018
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5982401/ https://ncbi.nlm.nih.gov/pubmed/29702629 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s18051371 |
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