NDLI: Instrumental/song classification of music signal using RANSAC

Content Provider	IEEE Xplore Digital Library
Author	Ghosal, A. Chakraborty, R. Dhara, B.C. Saha, S.K.
Copyright Year	2011
Description	Author affiliation: Indian Statistical, Institute Kolkata, India (Chakraborty, R.) \|\| Dept. of IT, Jadavpur University, Kolkata, India (Dhara, B.C.) \|\| CSE Dept, Inst. of Tech. & Marine Engg., 24 Pgs(S), West Bengal, India (Ghosal, A.) \|\| CSE Dept, Jadavpur University, Kolkata, India (Saha, S.K.)
Abstract	In a music retrieval system, classification of music data serves as the fundamental step for organizing the database to support faster access of desired data. In this context, it is very important to classify the music signal into the two sub-categories namely instrumental and song. A robust system for such classification will enable to go for further classification based on instrument type or music genre. In this work, we have presented a simple but novel scheme using Mel Frequency Cepstral Co-efficients (MFCC) as the signal descriptors. A classifier based on Random Sample and Consensus (RANSAC), capable of handling wide variety of data has been used. Experimental result indicates that proposed scheme works fine for a wide variety of music signals.
Starting Page	269
Ending Page	272
File Size	442920
Page Count	4
File Format	PDF
ISBN	9781424486786
e-ISBN	9781424486793
DOI	10.1109/ICECTECH.2011.5941603
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-08
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines RANSAC Instrumental/song classification Instruments Hidden Markov models Speech Data models Multiple signal classification Mel frequency cepstral coefficient MFCC audio classification
Content Type	Text
Resource Type	Article

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