3D-DCDAE: Unsupervised Music Latent Representations Learning Method Based on a Deep 3D Convolutional Denoising Autoencoder for Music Genre Classification

With unlabeled music data widely available, it is necessary to build an unsupervised latent music representation extractor to improve the performance of classification models. This paper proposes an unsupervised latent music representation learning method based on a deep 3D convolutional denoising a...

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Bibliographic Details
Published in:	Mathematics (Basel) Vol. 9; no. 18; p. 2274
Main Authors:	Qiu, Lvyang, Li, Shuyu, Sung, Yunsick
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.09.2021
Subjects:	3D CNN Algorithms autoencoder model Classification Classifiers Datasets Deep learning Genre Learning Mathematics MIDI Multilayer perceptrons Music music genre classification Musicology Noise Noise reduction Performance enhancement Performance evaluation Representations Training unsupervised learning
ISSN:	2227-7390, 2227-7390
Online Access:	Get full text
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