Ensemble of convolutional neural networks to improve animal audio classification

Abstract In this work, we present an ensemble for automated audio classification that fuses different types of features extracted from audio files. These features are evaluated, compared, and fused with the goal of producing better classification accuracy than other state-of-the-art approaches witho...

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Auteurs principaux: Loris Nanni, Yandre M. G. Costa, Rafael L. Aguiar, Rafael B. Mangolin, Sheryl Brahnam, Carlos N. Silla
Format: Artigo
Langue:Inglês
Publié: SpringerOpen 2020-05-01
Collection:EURASIP Journal on Audio, Speech, and Music Processing
Sujets:
Audio classification
Texture
Deep learning
Handcrafted features
Ensemble of classifiers
Pattern recognition
Accès en ligne:http://link.springer.com/article/10.1186/s13636-020-00175-3
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