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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Bibliografiske detaljer
Main Authors:	Diego de Benito-Gorron, Alicia Lozano-Diez, Doroteo T. Toledano, Joaquin Gonzalez-Rodriguez
Format:	Artigo
Sprog:	Inglês
Udgivet:	SpringerOpen 2019-06-01
Serier:	EURASIP Journal on Audio, Speech, and Music Processing
Fag:	Acoustic event detection Speech activity detection Music activity detection Neural networks Convolutional networks LSTM
Online adgang:	http://link.springer.com/article/10.1186/s13636-019-0152-1
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Exploring convolutional, recurrent, and hybrid deep neural networks for speech and music detection in a large audio dataset

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