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Year : 2012 Volume : 20

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Audio Segment Classification Using Online Learning Based Tensor Representation Feature Discrimination

Content Provider	IEEE Xplore Digital Library
Author	Ziqiang Shi Jiqing Han Tieran Zheng Shiwen Deng
Copyright Year	2006
Abstract	In order to naturally combine audio information from different dimensions and build robust audio processing system, a novel framework based on low-rank tensor representation features for audio segment classification is proposed in this paper. The audio signal is first transformed into tensor format data, and then these tensor data are mapped to a low-rank space which is insensitive under certain noises, especially white Gaussian noise and gross corruptions. For these low-rank tensor based features, tensor classification via a linear classifier based on minimization a smooth loss function regularized by the trace norm proposed recently is used. Most previous methods find the weight tensor and bias in batch-mode learning, which makes them inefficient for large-scale problems. In this paper, we propose to address this problem with an online learning algorithm based on the accelerated proximal gradient (APG) method, which scales up gracefully to large data sets. Experiments on simulation and real audio data demonstrate the efficiency of the methods.
Sponsorship	IEEE Signal Processing Society
Starting Page	186
Ending Page	196
Page Count	11
File Size	3316553
File Format	PDF
ISSN	15587916
Volume Number	21
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tensile stress Speech Equations Speech processing Robustness Noise Feature extraction trace norm regularization Accelerated proximal gradient (APG) audio segment classification low-rank approximation online learning tensor classification
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering

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