Paper Info
Title
Music Auto-Tagging Using Scattering Transform And Convolutional Neural Network With Self-Attention
Abstract
As a branch of machine learning, deep learning has been used for tackling with the music auto-tagging problem. Deep learning methods, especially those with convolutional neural network (CNN) architecture, have exhibited good performance on this multi-label classification task. However, the feature extracting part and preprocessing part of this architecture need to be improved. In this paper, we propose a deep-learning model based on CNN with scattering transform and self-attention mechanism for music automatic tagging. To get a balance between information integrity and feature extraction in the preprocessing phase, we employ the scattering transform. Then, a multi-layer CNN is used to extract higher-level features from the scattering coefficients. In order to select better receptive fields of the CNN, self-attention sub-network is appended at the last layer of CNN. Experimental results on the MagnaTagATune dataset and Million Song Dataset (MSD) show the proposed model is a good choice for music auto-tagging task, since the scores of the area under the receiver operating characteristic curve (ROC-AUC) and the area under the precision-recall curve (PR-AUC) obtained in this paper surpass the state-of-the-art models. Furthermore, we visualize the distributions of attention weights, activations of the CNN and ROC-AUC scores on each tag for better understanding of the model. (C) 2020 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2020
10.1016/j.asoc.2020.106702
APPLIED SOFT COMPUTING
Keywords
DocType
Volume
Music auto-tagging, Convolutional neural network, Attention mechanism, Scattering transform, Deep learning
Journal
96
ISSN
Citations 
PageRank 
1568-4946
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Guangxiao Song162.10
Zhijie Wang28911.14
Han, Fang343.51
Shenyi Ding400.34
Xiaochun Gu500.34