NDLI: Feature Selection in Automatic Music Genre Classification

Content Provider	IEEE Xplore Digital Library
Author	Silla, C.N. Koerich, A.L. Kaestner, C.
Copyright Year	2008
Description	Author affiliation: Fed. Univ. of Technol. of Parana, Curitiba (Kaestner, C.) \|\| Comput. Lab. Canterbury, Univ. of Kent, Canterbury (Silla, C.N.) \|\| Pontifical Catholic, Univ. of Parana, Curitiba (Koerich, A.L.)
Abstract	This paper presents the results of the application of a feature selection procedure to an automatic music genre classification system. The classification system is based on the use of multiple feature vectors and an ensemble approach, according to time and space decomposition strategies. Feature vectors are extracted from music segments from the beginning, middle and end of the original music signal (time decomposition). Despite being music genre classification a multi-class problem, we accomplish the task using a combination of binary classifiers, whose results are merged in order to produce the final music genre label (space decomposition). As individual classifiers several machine learning algorithms were employed: naive-Bayes, decision trees, support vector machines and multi-layer perceptron neural nets. Experiments were carried out on a novel dataset called Latin music database, which contains 3,227 music pieces categorized in 10 musical genres. The experimental results show that the employed features have different importance according to the part of the music signal from where the feature vectors were extracted. Furthermore, the ensemble approach provides better results than the individual segments in most cases.
Starting Page	39
Ending Page	44
File Size	263431
Page Count	6
File Format	PDF
ISBN	9780769534541
DOI	10.1109/ISM.2008.54
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multiple signal classification Feature extraction Classification tree analysis Machine learning algorithms Decision trees Support vector machines Support vector machine classification Multilayer perceptrons Neural networks Multi-layer neural network Pattern classification Music genre classification Audio classification Machine Learning
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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