Abstract
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.
Highlights
Initial classification of audio segments into broad categories such as speech, non-speech, and silence provides useful information for audio content understanding and analysis [1], and it has been used in a variety of commercial, forensic, and military applications [2]
We propose a novel semi-supervised filter method called constraint compensated Laplacian score (CCLS), which is similar to Laplacian score
It can be seen that the performance of Constraint Compensated Laplacian score (CCLS) is significantly better than that of Spec, Laplacian score, constraint score, and constrained Laplacian score
Summary
Initial classification of audio segments into broad categories such as speech, non-speech, and silence provides useful information for audio content understanding and analysis [1], and it has been used in a variety of commercial, forensic, and military applications [2]. Most audio classification systems involve two processing stages: feature extraction and classification. There is a considerable amount of literature on audio classification regarding different features [3] or classification methods [4]. Many features [5] have been developed to improve classification accuracy. Using all of these features in a classification system may not enhance but instead degrade the performance. The underlying reason is that there can be irrelevant, redundant, and even contradictory information among these features. Choosing the most relevant features to improve the classification accuracy is a challenging problem [6]
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