Audio-cut detection and audio-segment classification using fuzzy c-means clustering

This paper proposes an audio-cut detection and audio-segment classification method using fuzzy c-means clustering. In the proposed method, the boundaries between two different audio signals, which are called audio-cuts, can be detected by fuzzy c-means clustering. In the fuzzy c-means clustering, the fuzzy number represents the possibility that the audio-cut exists. Therefore, according to the possibility, qualified candidates for audio-cuts can be obtained even if audio effects such as fade-in, fade-out, etc. are included in the audio signal. The audio signal is segmented at the detected audio-cuts, and these segments are classified into the following five classes: silence, music, speech, speech with music background, and speech with noise background. This classification simultaneously deletes wrongly detected audio-cuts. Consequently, we can obtain accurate audio-cuts and classification results.