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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Bibliographic Details
Main Authors: Diego de Benito-Gorron, Alicia Lozano-Diez, Doroteo T. Toledano, Joaquin Gonzalez-Rodriguez
Format: Article
Language:English
Published: SpringerOpen 2019-06-01
Series:EURASIP Journal on Audio, Speech, and Music Processing
Subjects:
Acoustic event detection
Speech activity detection
Music activity detection
Neural networks
Convolutional networks
LSTM
Online Access: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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