NDLI: Audio feature reduction and analysis for automatic music genre classification

Content Provider	IEEE Xplore Digital Library
Author	Baniya, B.K. Joonwhoan Lee Ze-Nian Li
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Chonbuk Nat. Univ., Jeonju, South Korea (Baniya, B.K.; Joonwhoan Lee) \|\| Sch. of Comput. Sci., Simon Fraser Univ., Vancouver, BC, Canada (Ze-Nian Li)
Abstract	Multimedia database retrieval is growing at a fast rate thereby subsequent increase in the popularity of online retrieval system. The large datasets are major challenges for searching, retrieving, and organizing the music content. Therefore, there is a need of robust automatic music genre classification method for organizing these music data into different classes according to the certain viable information. There are two fundamental components to be considered for genre classification namely audio feature extraction and classifier design. In this paper, diverse audio features set have been proposed to characterize the music contents precisely. The feature sets belong to four different groups, i.e. dynamic, rhythm, spectral, and harmony. From the features, five different statistical parameters are considered as representatives, including up to the $4^{th}$ order central moments of each feature, and covariance components. Ultimately, significant numbers of representative attributes are controlled by MRMR algorithm. The algorithm calculates the score level of all feature attributes and orders them. The high score feature attributes are only considered for genre classification. Moreover, we can visualize that which audio features and which of the different statistical parameters derived from them are important for genre classification. Among them, mel frequency cepstral coefficients (MFCCs) have higher scored level than other feature attributes. Furthermore, MRMR does not transform the feature value like as principal component analysis (PCA). Besides these, the comparison has been made based on classification accuracy between two-dimensionality reduction methodologies using support vector machine (SVM). The classification accuracy of MRMR feature reduction set outperforms than PCA. The overall classification is also higher than other existing state-of-the-art of frame base methods.
Starting Page	457
Ending Page	462
File Size	149630
Page Count	6
File Format	PDF
ISBN	9781479938407
DOI	10.1109/SMC.2014.6973950
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Accuracy Music Principal component analysis Mel frequency cepstral coefficient Classification algorithms Support vector machines MRMR music genre dimensionality feature reduction statistical parameters
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in