Unsupervised speech/music classification using one-class support vector machines

• Published in: 2007 6th International Conference on Information, Communications and Signal Processing pp. 1 - 5
• Main Authors: Sadjadi, S.O., Ahadi, S.M., Hazrati, O.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2007
• Subjects: audio feature extraction; Clustering algorithms; Clustering methods; Feature extraction; Iterative algorithms; Multiple signal classification; oneclass SVM; Pattern classification; Spatial databases; Speech; speech/music discrimination; Support vector machine classification; Support vector machines; unsupervised clustering
• ISBN: 1424409829; 9781424409822
• DOI: 10.1109/ICICS.2007.4449839

Audio classification is an important issue in current audio processing and content analysis researches. Speech/music classification is one of the most interesting branches of audio signal classification. In this paper we present an unsupervised clustering method, based on one-class support vector machines (OCSVM) and inspired by the classical K-means algorithm, which effectively classifies speech/music signals. First, relevant features are extracted from audio files. Then in an iterative K- means like algorithm, after initializing centers, each cluster is refined using a one-class support vector machine. The experimental results show that the clustering method, which can be easily implemented, performs better than other methods implemented on the same database.