  In this paper, a novel framework based on trace norm minimization for audio
segment 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 $\ell_1$-norm is used to extract low-rank features which
are robust to white noise and gross corruption for audio segments. These
low-rank features are fed to a linear classifier where the weight and bias are
learned by solving similar trace norm constrained problems. For this
classifier, most methods find the weight and bias in batch-mode learning, which
makes them inefficient for large-scale problems. In this paper, we propose an
online framework using accelerated proximal gradient method. This framework has
a main advantage in 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 general proximal gradient
method was omitted in our approach. Experiments on real data sets for
laugh/non-laugh and applause/non-applause classification indicate that this
novel framework is effective and noise robust.
