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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 eventdoi:10.1109/apsipa.2013.6694199 dblp:conf/apsipa/ZhangLWWWL13 fatcat:2ydzzk6adfhvfi4uyxn44kuwke