Multiple change-point audio segmentation and classification using an MDL-based Gaussian model

Chung Hsien Wu, Chia Hsin Hsieh

研究成果: Article同行評審

33 引文 斯高帕斯(Scopus)

摘要

This study presents an approach for segmenting and classifying an audio stream based on audio type. First, a silence deletion procedure is employed to remove silence segments in the audio stream. A minimum description length (MDL)-based, Gaussian model is then proposed to statistically characterize the audio features. Audio segmentation segments the audio stream into a sequence of homogeneous subsegments using the MDL-based Gaussian model. A hierarchical threshold-based classifier is then used to classify each subsegment into different audio types. Finally, a heuristic method is adopted to smooth the subsegment sequence and provide the final segmentation and classification results. Experimental results indicate that for TDT-3 news broadcast, a missed detection rate (MDR) of 0.1 and a false alarm rate (FAR) of 0.14 were achieved for audio segmentation. Given the same MDR and FAR values, segment-based audio classification achieved a better classification accuracy of 88% compared to a clip-based approach.

原文English
頁(從 - 到)647-657
頁數11
期刊IEEE Transactions on Audio, Speech and Language Processing
14
發行號2
DOIs
出版狀態Published - 2006

All Science Journal Classification (ASJC) codes

  • 聲學與超音波
  • 電氣與電子工程

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