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Proceedings Paper

Content-based audio retrieval: a comparative study of various features and similarity measures
Author(s): Pingying Wan; Lie Lu
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Paper Abstract

Audio retrieval is an important research topic in audio field. A good audio retrieval system is very helpful to facilitate users to find the target audio materials. In such a system, while audio features are fundamental in representing an audio clip, similarity measure is also an important fact affecting the performance of audio retrieval. In previous research works, there are many proposed audio features and used distance measures. However, there is not yet a good study about the effectiveness of different features and distance measures. Therefore, in this paper, we perform a comparative study on various audio features and various distance measures (similarity measures). The compared audio features include Mel-frequency cepstral coefficients (MFCC), Linear Predictive Coding Coefficients (LPC), sub-band energy distribution and some other temporal/spectral features, while the compared distances include Euclidean distance, Kullback-Leibler (K-L) divergence, Mahalanobis distance and Bhattacharyya distance. The study is expected to be helpful in the further design of audio retrieval system.

Paper Details

Date Published: 24 October 2005
PDF: 8 pages
Proc. SPIE 6015, Multimedia Systems and Applications VIII, 60151H (24 October 2005); doi: 10.1117/12.629147
Show Author Affiliations
Pingying Wan, Beijing Union Univ. (China)
Lie Lu, Microsoft Research Asia (China)


Published in SPIE Proceedings Vol. 6015:
Multimedia Systems and Applications VIII
Anthony Vetro; Chang Wen Chen; C.-C. J. Kuo; Tong Zhang; Qi Tian; John R. Smith, Editor(s)

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