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In this paper, a novel framework based on trace norm minimization for audiosegment is proposed. In this framework, both the feature extraction andclassification are obtained by solving corresponding convex optimizationproblem with trace norm regularization. For feature extraction, robustprinciple component analysis (robust PCA) via minimization a combination of thenuclear norm and the $\ell_1$-norm is used to extract low-rank features whichare robust to white noise and gross corruption for audio segments. Theselow-rank features are fed to a linear classifier where the weight and bias arelearned by solving similar trace norm constrained problems. For thisclassifier, most methods find the weight and bias in batch-mode learning, whichmakes them inefficient for large-scale problems. In this paper, we propose anonline framework using accelerated proximal gradient method. This framework hasa main advantage in memory cost. In addition, as a result of the regularizationformulation of matrix classification, the Lipschitz constant was givenexplicitly, and hence the step size estimation of general proximal gradientmethod was omitted in our approach. Experiments on real data sets forlaugh/non-laugh and applause/non-applause classification indicate that thisnovel framework is effective and noise robust.

Online Learning for Classification of Low-rank Representation Features and Its Applications in Audio Segment Classification