Convolutional Neural Networks Approach for Music Genre Classification

2020 ◽  
Author(s):  
Yu-Huei Cheng ◽  
Pang-Ching Chang ◽  
Che-Nan Kuo
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Genre Classification ◽  
Music Genre ◽  
Music Genre Classification

scholarly journals Musical Genre Classification using Convolutional Neural Networks

2020 ◽  
Vol 10 (1) ◽  
pp. 228-230
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Musical Genre ◽  
Genre Classification ◽  
Music Genre ◽  
Music Genre Classification

Music has likewise been separated into Genres and sub sorts on the premise on music. To show that, we contrast the outcomes acquired and a Convolutional Neural Network (CNN). Experiments were conducted on Marsyas databases with distinct characteristics for genre classification. The proposed CNN results in better accuracy in music genre classification.

scholarly journals Music Genre Classification using Deep Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 66-71
Author(s):  
Sheeba Fathima
Keyword(s):  
Neural Networks ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Music Production ◽  
Genre Classification ◽  
Learning Techniques ◽  
Network Topologies ◽  
Classification Tasks ◽  
Music Genre ◽  
Music Genre Classification

Many subjects are affected by digital music production., including music genre prediction. Machine learning techniques were used to classify music genres in this research. Deep neural networks (DNN) have recently been demonstrated to be effective in a variety of classification tasks. Including music genre classification. In this paper, we propose two methods for boosting music genre classification with convolutional neural networks: 1) using a process inspired by residual learning to combine peak- and average pooling to provide more statistical information to higher level neural networks; and 2) To bypass one or more layers, use shortcut connections. To perform classification, the KNN output is fed into another deep neural network. Our preliminary experimental results on the GTZAN data set show that the above two methods, especially the second one, can effectively improve classification accuracy when compared to two different network topologies.

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