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Audio classification with low-rank matrix representation features

Content Provider	ACM Digital Library
Author	Han, Jiqing Zheng, Tieran Shi, Ziqiang
Copyright Year	2013
Abstract	In this article, a novel framework based on trace norm minimization for audio classification is proposed. In this framework, both the feature extraction and classification are obtained by solving corresponding convex optimization problem with trace norm regularization. For feature extraction, robust principle component analysis (robust PCA) via minimization a combination of the nuclear norm and the $ℓ_{1}-norm$ is used to extract low-rank matrix features which are robust to white noise and gross corruption for audio signal. These low-rank matrix features are fed to a linear classifier where the weight and bias are learned by solving similar trace norm constrained problems. For this linear classifier, most methods find the parameters, that is the weight matrix and bias in batch-mode, which makes it inefficient for large scale problems. In this article, we propose a parallel online framework using accelerated proximal gradient method. This framework has advantages in processing speed and memory cost. In addition, as a result of the regularization formulation of matrix classification, the Lipschitz constant was given explicitly, and hence the step size estimation of the general proximal gradient method was omitted, and this part of computing burden is saved in our approach. Extensive experiments on real data sets for laugh/non-laugh and applause/non-applause classification indicate that this novel framework is effective and noise robust.
Starting Page	1
Ending Page	17
Page Count	17
File Format	PDF
ISSN	21576904
e-ISSN	21576912
DOI	10.1145/2542182.2542197
Volume Number	5
Issue Number	1
Journal	ACM Transactions on Intelligent Systems and Technology (TIST)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-01-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Audio classification Low-rank matrix Matrix classification Online learning Proximal gradient Robust principle component analysis Trace norm minimization
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Theoretical Computer Science

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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
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