Content-based retrieval of audio data using a Centroid Neural Network

  • Authors:

  • Dong-Chul Park

  • Affiliations:

  • Dept. of Electronics Engineering, Myong Ji University, Yong In, KOREA

  • Venue:
  • ISSPIT '10 Proceedings of the The 10th IEEE International Symposium on Signal Processing and Information Technology
  • Year:
  • 2010

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Abstract

A classification scheme for content-based audio signal retrieval is proposed in this paper. The proposed scheme uses the Centroid Neural Networks (CNN) with a Divergence Measure called Divergence-based Centroid Neural Network (DCNN)to perform clustering of Gaussian Probability Density Function (GPDF) data. In comparison with other conventional algorithms, the DCNN designed for probability data has the robustness advantages of utilizing a audio data representation method in which each audio data is represented by a Gaussian distribution feature vector. Experiments and results for several audio data sets have shown that the DCNN-based classification algorithm has accuracy improvements over models employing the conventional k-means and Self Organizing Map (SOM) algorithms.