A Wavelet Packet representation of audio signals for music genre classification using different ensemble and feature selection techniques

2003 ◽  
Author(s):  
Marco Grimaldi ◽  
Pádraig Cunningham ◽  
Anil Kokaram
Keyword(s):  
Feature Selection ◽  
Wavelet Packet ◽  
Audio Signals ◽  
Genre Classification ◽  
Music Genre ◽  
Feature Selection Techniques ◽  
Music Genre Classification

scholarly journals Feature Selection in Automatic Music Genre Classification

2008 ◽  
Author(s):  
Carlos N. Silla Jr. ◽  
Alessandro L. Koerich ◽  
Celso A. A. Kaestner
Keyword(s):  
Feature Selection ◽  
Genre Classification ◽  
Music Genre ◽  
Music Genre Classification

Machine Learning Evaluation for Music Genre Classification of Audio Signals

2020 ◽  
Vol 12 (3) ◽  
pp. 57-67
Author(s):  
Chetna Dabas ◽  
Aditya Agarwal ◽  
Naman Gupta ◽  
Vaibhav Jain ◽  
Siddhant Pathak
Keyword(s):  
Machine Learning ◽  
Audio Signals ◽  
Python Language ◽  
Audio Features ◽  
Genre Classification ◽  
Proposed Model ◽  
Spectral Coefficients ◽  
Music Genre ◽  
Music Genre Classification

Music genre classification has its own popularity index in the present times. Machine learning can play an important role in the music streaming task. This research article proposes a machine learning based model for the classification of music genre. The evaluation of the proposed model is carried out while considering different music genres as in blues, metal, pop, country, classical, disco, jazz and hip-hop. Different audio features utilized in this study include MFCC (Mel Frequency Spectral Coefficients), Delta, Delta-Delta and temporal aspects for processing the data. The implementation of the proposed model has been done in the Python language. The results of the proposed model reveal an accuracy SVM accuracy of 95%. The proposed algorithm has been compared with existing algorithms and the proposed algorithm performs better than the existing ones in terms of accuracy.

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