An Unsupervised Audio Segmentation and Classification Approach

Authors:
Wenjuan Pan;Yong Yao;Zhijing Liu
Affiliations:
Xidian University,Xi'an 710071,China;Xidian University,Xi'an 710071,China;Xidian University,Xi'an 710071,China
Venue:
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Year:
2007

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Abstract

This paper presents an unsupervised audio segmentation and classification approach. First, the multiple change-point segmentation is adopted, and a new feature named Mel-ICA is introduced to improve it. An audio type "uncertain" is proposed to represent mixed type. Three features of each sub-segment are extracted using Fourier and wavelet transform. Then, classification is performed over each sub-segment based on feature threshold, and the majority rule is applied to determine the final type. The experimental results have shown that the false alarm rate decreased using Mel-ICA, and high accuracy of classification achieved.