A New Hybrid Audio Classification Algorithm Based on SVM Weight Factor and Euclidean Distance

The text-based classification dominates in the conventional audio classification systems, in which tedious manual work is used to notate the name, class, or sample rate. However, on most occasions, this method is not satisfying due to its opaque to real content. In order to retrieve the audio files effectively and efficiently, content-based audio classification becomes more and more necessary. In this paper, a new hybrid approach of audio classification algorithm is proposed to improve the performance of some mis-classified audio data. The proposed method has been compared with the traditional Euclidean-based K-Nearest Neighbor classifier. As to improve the accuracy for specific problems, a weight factor based on Supporting Vector will apply to the Euclidean distance and K-NN rule to achieve better accuracy. The proposed new weighted Euclidean algorithm has been proved to be more sensitive to the classification criteria. The experimental results show that it can improve the audio classification accuracy by 28% at the maximum and 7% in the overall performance. By using the new proposed algorithm, some mis-classified audio data from a conventional Euclidean distance classifier can be classified. Key-Words: audio classification, K-Nearest Neighbor, SVM, Euclidean distance