Audio segmentation and classification using a temporally weighted fuzzy C-means algorithm

  • Authors:

  • Ngoc Thi Thu Nguyen;Mohammad A. Haque;Cheol-Hong Kim;Jong-Myon Kim

  • Affiliations:

  • School of Computer Engineering and Information Technology, University of Ulsan, Ulsan, South Korea;School of Computer Engineering and Information Technology, University of Ulsan, Ulsan, South Korea;School of Electronics and Computer Engineering, Chonnam National University, Kwangju, South Korea;School of Computer Engineering and Information Technology, University of Ulsan, Ulsan, South Korea

  • Venue:
  • ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

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.