Paper Info
Title
Intelligent query by humming system based on score level fusion of multiple classifiers
Abstract
Recently, the necessity for content-based music retrieval that can return results even if a user does not know information such as the title or singer has increased. Query-by-humming (QBH) systems have been introduced to address this need, as they allow the user to simply hum snatches of the tune to find the right song. Even though there have been many studies on QBH, few have combined multiple classifiers based on various fusion methods. Here we propose a new QBH system based on the score level fusion of multiple classifiers. This research is novel in the following three respects: three local classifiers [quantized binary (QB) code-based linear scaling (LS), pitch-based dynamic time warping (DTW), and LS] are employed; local maximum and minimum point-based LS and pitch distribution feature-based LS are used as global classifiers; and the combination of local and global classifiers based on the score level fusion by the PRODUCT rule is used to achieve enhanced matching accuracy. Experimental results with the 2006 MIREX QBSH and 2009 MIR-QBSH corpus databases show that the performance of the proposed method is better than that of single classifier and other fusion methods.
Year
DOI
Venue
2011
10.1186/1687-6180-2011-21
EURASIP J. Adv. Sig. Proc.
Keywords
Field
DocType
dynamic time warping,score level fusion.,multiple classifiers,linear scaling,query-by-humming
Dynamic time warping,Random subspace method,Computer science,Fusion,Query by humming,Artificial intelligence,Classifier (linguistics),Binary number,Pattern recognition,Product rule,Linear scale,Speech recognition,Machine learning
Journal
Volume
Issue
ISSN
2011
1
1687-6180
Citations 
PageRank 
References 
13
0.55
12
Authors
5
Name
Order
Citations
PageRank
Gi Pyo Nam1736.69
Thi Thu Trang Luong2130.55
Hyun Ha Nam3171.33
Kang Ryoung Park41325104.82
Sung-Joo Park5372.20