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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Detalles Bibliográficos
Autores principales: Woo, Wai Lok, Gao, Bin, Bouridane, Ahmed, Ling, Bingo Wing-Kuen, Chin, Cheng Siong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5982401/
https://www.ncbi.nlm.nih.gov/pubmed/29702629
http://dx.doi.org/10.3390/s18051371

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5982401/
https://www.ncbi.nlm.nih.gov/pubmed/29702629
http://dx.doi.org/10.3390/s18051371

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