Sparse coding for sound event classification

Mingming Zhang, Weifeng Li, Longbiao Wang, Jianguo Wei, Zhiyong Wu, Qingmin Liao

Abstract: Generally sound event classification algorithms are always based on speech recognition methods: feature-extraction and model-training. In order to improve the classification performance, researchers always pay much attention to find more effective sound features or classifiers, which is obviously difficult. In recent years, sparse coding provides a class of effective algorithms to capture the high-level representation features of the input data. In this paper, we present a sound event classification method based on sparse coding and supervised learning model. Sparse coding coefficients will be used as the sound event features to train the classification model. Experiment results demonstrate an obvious improvement in sound event classification.