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A Study on the Efficient Feature Vector Extraction for Music Information Retrieval System  

윤원중 (단국대학교 정보ㆍ컴퓨터학부 컴퓨터과학 전공)
이강규 (단국대학교 정보ㆍ컴퓨터학부 컴퓨터과학 전공)
박규식 (단국대학교 정보ㆍ컴퓨터학부 컴퓨터과학 전공)
Publication Information
The Journal of the Acoustical Society of Korea / v.23, no.7, 2004 , pp. 532-539 More about this Journal
Abstract
In this Paper, we propose a content-based music information retrieval (MIR) system base on the query-by-example (QBE) method. The proposed system is implemented to retrieve queried music from a dataset where 60 music samples were collected for each of the four genres in Classical, Hiphop. Jazz. and Reck. resulting in 240 music files in database. From each query music signal, the system extracts 60 dimensional feature vectors including spectral centroid. rolloff. flux base on STFT and also the LPC. MFCC and Beat information. and retrieves queried music from a trained database set using Euclidean distance measure. In order to choose optimum features from the 60 dimension feature vectors, SFS method is applied to draw 10 dimension optimum features and these are used for the Proposed system. From the experimental result. we can verify the superior performance of the proposed system that provides success rate of 84% in Hit Rate and 0.63 in MRR which means near 10% improvements over the previous methods. Additional experiments regarding system Performance to random query Patterns (or portions) and query lengths have been investigated and a serious instability problem of system Performance is Pointed out.
Keywords
Music information retrieval; SFS; SFS; QBE; Random query pattern; Query length;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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