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A study on content-based classification and retrieval of audio database

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2 Author(s)
Liu, M. ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Inst., Singapore ; Wan, C.

Nowadays, available audio corpora are rapidly increasing from fast growing Internet and digitized libraries. How to effectively classify and retrieve such huge databases is a challenging task. Content based technology is studied to automatically classify audio into hierarchy classes. Based on a small set of features selected by the sequential forward selection (SFS) method from 87 extracted ones, four classifiers, namely nearest neighbor (NN), modified k-nearest neighbor (k-NN), Gaussian mixture model (GMM), and probabilistic neural network (PNN) are compared. Experiments were conducted on a common database and a more comprehensive database built by the authors. Finally, the PNN classifier combined with Euclidean distance measurement was chosen for audio retrieval, using query by example

Published in:

Database Engineering and Applications, 2001 International Symposium on.

Date of Conference:

2001