Unsupervised Classification of Sound for Multimedia Indexing
- Bruce Matichuk
- Osmar R. Zaiane, University of Alberta (Database)
Segmenting audio streams in a significant manner and clustering sound segments objectively, is a significant challenge due to the nature of audio data. This paper presents some preliminary work on clustering sound segments based on frequency and harmonic characteristics. New metrics for comparing the similarity of sound segments are also devised.
Citation
B. Matichuk, O. Zaiane. "Unsupervised Classification of Sound for Multimedia Indexing". International ACM SIGKDD Workshop on Multimedia Data Mining, pp 31-36, August 2000.Keywords: | Multimedia Data Mining, Sound Processing, Classification, Clustering, Similarity comparison |
Category: | In Workshop |
BibTeX
@misc{Matichuk+Zaiane:MDM/KDD00, author = {Bruce Matichuk and Osmar R. Zaiane}, title = {Unsupervised Classification of Sound for Multimedia Indexing}, Pages = {31-36}, booktitle = {International ACM SIGKDD Workshop on Multimedia Data Mining}, year = 2000, }Last Updated: February 05, 2020
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