Learning sparse dictionaries for music and speech classification

Roy, D and Mettu, Srinivas and C, Krishna Mohan (2014) Learning sparse dictionaries for music and speech classification. In: 19th International Conference on Digital Singal Processing, 20-23 August, 2014, Hong Kong.

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The field of music and speech classification is quite mature with researchers having settled on the approximate best discriminative representation. In this regard, Zubair et al. showed the use of sparse coefficients alongwith SVM to classify audio signals as music or speech to get a near-perfect classification. In the proposed method, we go one step further, instead of using the sparse coefficients with another classifier they are directly used in a dictionary which is learned using on-line dictionary learning for music-speech classification. This approach removes the redundancy of using a separate classifier but also produces complete discrimination of music and speech on the GTZAN music/speech dataset. Moreover, instead of the high-dimensional feature vector space which inherently leads to high computation time and complicated decision boundary calculation on the part of SVM, the restricted dictionary size with limited computation serves the same purpose.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Music and Speech Classification, Sparse Repre- sentation, Dictionary Learning
Subjects: Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 22 Jun 2015 07:37
Last Modified: 01 Sep 2017 09:24
URI: http://raiith.iith.ac.in/id/eprint/1589
Publisher URL: https://doi.org/10.1109/ICDSP.2014.6900749
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