Semi-supervised feature selection for audio classification based on constraint compensated Laplacian score

Audio classification, classifying audio segments into broad categories such as speech, non-speech, and silence, is an important front-end problem in speech signal processing. Dozens of features have been proposed for audio classification. Unfortunately, these features are not directly complementary and combining them does not improve classification performance. Feature selection provides an effective mechanism for choosing the most relevant and least redundant features for classification. In this paper, we present a semi-supervised feature selection algorithm named Constraint Compensated Laplacian score (CCLS), which takes advantage of the local geometrical structure of unlabeled data as well as constraint information from labeled data. We apply this method to the audio classification task and compare it with other known feature selection methods. Experimental results demonstrate that CCLS gives substantial improvement.