Exploring convolutional, recurrent, and hybrid deep neural networks for speech and music detection in a large audio dataset

Exploring convolutional, recurrent, and hybrid deep neural networks for speech and music detection in a large audio dataset

Abstract Audio signals represent a wide diversity of acoustic events, from background environmental noise to spoken communication. Machine learning models such as neural networks have already been proposed for audio signal modeling, where recurrent structures can take advantage of temporal dependenc...

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Autors principals: Diego de Benito-Gorron, Alicia Lozano-Diez, Doroteo T. Toledano, Joaquin Gonzalez-Rodriguez
Format: Article
Idioma:English
Publicat: SpringerOpen 2019-06-01
Col·lecció:EURASIP Journal on Audio, Speech, and Music Processing
Matèries:
Acoustic event detection
Speech activity detection
Music activity detection
Neural networks
Convolutional networks
LSTM
Accés en línia:http://link.springer.com/article/10.1186/s13636-019-0152-1

Internet

http://link.springer.com/article/10.1186/s13636-019-0152-1

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