DocumentCode :
1797572
Title :
Music genre classification using On-line Dictionary Learning
Author :
Srinivas, M. ; Roy, Didier ; Mohan, Chilukuri K.
Author_Institution :
Dept. of Comput. Sci. & Eng., Indian Inst. of Technol., Hyderabad, Hyderabad, India
fYear :
2014
fDate :
6-11 July 2014
Firstpage :
1937
Lastpage :
1941
Abstract :
In this paper, an approach for music genre classification based on sparse representation using MARSYAS features is proposed. The MARSYAS feature descriptor consisting of timbral texture, pitch and beat related features is used for the classification of music genre. On-line Dictionary Learning (ODL) is used to achieve sparse representation of the features for developing dictionaries for each musical genre. We demonstrate the efficacy of the proposed framework on the Latin Music Database (LMD) consisting of over 3000 tracks spanning 10 genres namely Axé, Bachata, Bolero, Forró, Gaúcha, Merengue, Pagode, Salsa, Sertaneja and Tango.
Keywords :
database management systems; learning (artificial intelligence); music; LMD; Latin music database; MARSYAS feature descriptor; ODL; beat related features; music genre classification; online dictionary learning; pitch related features; sparse representation; Accuracy; Databases; Dictionaries; Feature extraction; Support vector machines; Training; Vectors; Dictionary Learning; Music Genre Classification; Sparse Representation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
Type :
conf
DOI :
10.1109/IJCNN.2014.6889516
Filename :
6889516
Link To Document :
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