Abstract
This paper presents an approach to extracting dominant feature vectors from an individual audio clip and then proposes a new similarity measure based on the dominant feature vectors. Instead of using the mean and standard deviation of frame features in most conventional methods, the most salient characteristics of an audio clip are represented in the form of several dominant feature vectors. These dominant feature vectors give a better description of the fundamental properties of an audio clip, especially when frame features change a lot along the time line. Evaluations on a content-based audio retrieval system indicate an obvious improvement after using the proposed similarity measure, compared with some other conventional methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lu, L., Zhang, H.-J., Jiang, H.: Content Analysis for Audio Classification and Segmentation. IEEE Trans. on Speech and Audio Processing 10(7), 504–516 (2002)
Wold, E., Blum, T., Keislar, D., Wheaton, J.: Content-based Classification, Search, and Retrieval of Audio. IEEE Multimedia 3(3), 27–36 (1996)
Cai, R., Lu, L., Zhang, H.-J., Cai, L.-H.: Using Structure Patterns of Temporal and Spectral Feature in Audio Similarity Measure. In: Proc. of 11th ACM Multimedia, pp. 219–222 (2003)
Basseville, M.: Distance Measure for Signal Processing and Pattern Recognition. Signal Processing 18(4), 349–369 (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gu, J., Lu, L., Cai, R., Zhang, HJ., Yang, J. (2004). Dominant Feature Vectors Based Audio Similarity Measure. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds) Advances in Multimedia Information Processing - PCM 2004. PCM 2004. Lecture Notes in Computer Science, vol 3332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30542-2_110
Download citation
DOI: https://doi.org/10.1007/978-3-540-30542-2_110
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23977-2
Online ISBN: 978-3-540-30542-2
eBook Packages: Computer ScienceComputer Science (R0)