Audio Segmentation and Classification Using a Temporally Weighted Fuzzy C-Means Algorithm

Abstract

In this paper, we present a noble method to segment and classify audio stream using a temporally weighted fuzzy c-means algorithm (TWFCM). The proposed algorithm is utilized to determine the boundaries between different kinds of sounds in an audio stream; and then classify the audio segments into five classes of sound such as music, speech, speech with music background, speech with noise background, and silence. This is an enhancement on conventional fuzzy c-means algorithm, applied in audio segmentation and classification domain, by addressing and reflecting the matter of temporal correlations between the audio signals in the current and previous time. A 3-elements feature vector is utilized in segmentation and a 5-elements feature vector is utilized in classification by using TWFCM. The audio-cuts can be detected accurately by this method, and mistakes caused by audio effects can be eliminated in segmentation. Improved classification performance is also achieved. The application of this method is demonstrated in segmenting and classifying real-world audio data such as television news, radio signals, etc. Experimental results indicate that the proposed method outperforms the conventional FCM.KeywordsAudio segmentation and classificationfuzzy c-means algorithmdatabase retrieval