Modeling and classification of audio signals using gradient-based fuzzy C-means algorithm with a Mercer Kernel

Authors:
Dong-Chul Park;Chung Nguyen Tran;Byung-Jae Min;Sancho Park
Affiliations:
Dept. of Information Engineering, Myong Ji University, Korea;Dept. of Information Engineering, Myong Ji University, Korea;Dept. of Information Engineering, Myong Ji University, Korea;Davan Tech Co., Seongnam, Korea
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

Citing 4
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Abstract

In this paper, we propose a noble classification algorithm for content-based audio signal retrieval. The algorithm uses the Gradient-Based Fuzzy C-Means with a Mercer Kernel (GBFCM(MK)) to perform clustering of Gaussian Probability Density Function (GPDF) data of a Gaussian Mixture Model (GMM). The GBFCM(MK) algorithm incorporates a kernel method into the GBFCM to implicitly perform nonlinear mapping of the input data into a high-dimensional feature space. Experiments and results for several audio data sets have shown that the GBFCM(MK)-based classification algorithm has accuracy improvements of 3.14%-7.49% over classification algorithms employing the traditional k-means and the Fuzzy C-Mean (FCM), respectively.