We propose an algorithm to classify low-rank matrix representative audio data. Conventionally, the low-rank matrix data can be represented by a vector in high dimensional space. Some learning algorithms are then applied in such a vector space for matrix data classification. Particularly, maximum margin classifiers, such as support vector machine (SVM) etc. have received a lot of attentions due to their effectiveness. In this paper, we classify the data directly in the matrix space. Our methodology is built on recent studies about matrix classification with the trace norm constrained weight matrix and SVM's large-margin linear discrimination principle. The resulting low-rank SVM is then designed to maximize the margin between classes whilst minimizing the complexity of the classifier in both original and low-rank space. We compared our proposed algorithm with SVM and other state-of-the-art matrix classification methods. Experimental studies on real life audio signal classification show the effectiveness of our algorithm.

Index Terms: speech/non-speech, matrix classification, maximum margin, trace norm regularization, low-rank feature