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

TL;DR

This paper introduces an online framework using trace norm minimization for robust low-rank feature extraction and classification in audio segments, improving efficiency and noise robustness in large-scale applications.

Contribution

It proposes a novel online trace norm regularized framework for low-rank feature extraction and classification, with explicit step size estimation, suitable for large-scale audio data.

Findings

Effective noise robustness demonstrated in experiments

Reduced memory cost through online processing

Competitive classification accuracy on real datasets

Abstract

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.