Classification of audio signals using Fuzzy c-Means with divergence-based Kernel

A content-based audio retrieval method based Fuzzy c-Means algorithm with divergence kernel (FCM-DK) is proposed in this paper. FCM-DK is based on the Fuzzy c-Means algorithm and employs a kernel method for data transformation. The kernel method adopted in FCM-DK is used to transform the feature data of audio signals into a feature space of a higher dimensionality so that nonlinear problems residing in the input space can be linearly solved in the feature space. In order to deal with Gaussian probability density function (GPDF) data, a divergence-based kernel employing a divergence distance measure for its similarity measure is used for data transformation. The proposed method exploits the statistical nature of the audio data to improve the classification accuracy. Experiments and results on various data sets demonstrate that the proposed classification method outperforms conventional algorithms such as the traditional self-organizing map (SOM) and the Fuzzy c-Means (FCM) 20.83% and 17.5% in terms of accuracy.