Audio Segment Classification Using Online Learning Based Tensor Representation Feature Discrimination

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.