Initial training samples selection using clustering algorithm for audio recognition

Abstract

The quantity and quality of training samples are one of the most critical factors for the performance of the classifier, whatever traditional classification method or deep learning algorithm. To improve the quality of the training samples with less manual tagging effort, this paper proposed a representative samples selection method based on clustering algorithm to choose more representative samples supplied to human to annotate. These representative ones can describe class distribution better than samples annotated at random. On the one hand, these selected samples can train a more generalized classifier. On the other hand, this samples selection method can save expensive annotation work which is thought a lot by people now. The results of experiments in melodrama Friends corpus show better classification performance compared to traditional annotation methods.