Audio Feature Extraction and Analysis for Scene Segmentation and Classification
Journal of VLSI Signal Processing Systems - special issue on multimedia signal processing
Content-Based Classification, Search, and Retrieval of Audio
IEEE MultiMedia
Acoustic environment classification
ACM Transactions on Speech and Language Processing (TSLP)
Audio similarity measure by graph modeling and matching
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Using earth mover's distance for audio clip retrieval
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Dominant feature vectors based audio similarity measure
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
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Although statistical characteristics of audio features are widely used for similarity measure in most of current audio analysis systems and have been proved to be effective, they only utilized the averaged feature variations over time, and thus lead to inaccuracy in some cases. In this paper, structure pattern, which describes the representative structure characteristics of both temporal and spectral features, is proposed to improve the similarity measure for audio effects. Three kind structure patterns are proposed and utilized in current work, including energy contour pattern, harmonicity pattern and pitch contour pattern. Evaluations on a content-based audio retrieval system indicate that structure patterns can improve the performance pretty much.