Audio signal segmentation and classification using fuzzy c-means clustering

This paper proposes an audio signal segmentation and classification method using fuzzy c-means clustering. Recently, high performance of the audio signal segmentation and classification is required for audio-visual indexing because of the popular use of the Internet, higher bandwidth access to the network, widespread of digital recording and storage; and several methods have been proposed. They segment the audio signal at boundaries between two different audio signals, which are called audio-cuts, and then classify the audio signal into basic audio classes such as speech, music, etc. However, since most of the methods utilize thresholding for the audio-cut detection, they cannot provide high accuracy because of several audio effects, such as fade-in, fade-out, cross-fade, etc. To overcome this problem, we utilize the fuzzy c-means clustering. The possibility that the audio-cut exists is represented by the fuzzy number, and thus we can detect audio-cuts accurately. After the segmentation, the audio signal is classified into audio classes. This classification results are utilized for verification processing of the audio-cuts, so that segmentation and classification errors are reduced. Experimental results performed by applying the proposed method to real audio signals are shown to verify its high performance.