HMM-Based audio keyword generation

  • Authors:

  • Min Xu;Ling-Yu Duan;Jianfei Cai;Liang-Tien Chia;Changsheng Xu;Qi Tian

  • Affiliations:

  • School of Computer Engineering, Nanyang Technological University, Singapore;Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore;Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore

  • Venue:
  • PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part III
  • Year:
  • 2004

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Abstract

With the exponential growth in the production creation of multimedia data, there is an increasing need for video semantic analysis. Audio, as a significant part of video, provides important cues to human perception when humans are browsing and understanding video contents. To detect semantic content by useful audio information, we introduce audio keywords which are sets of specific audio sounds related to semantic events. In our previous work, we designed a hierarchical Support Vector Machine (SVM) classifier for audio keyword identification. However, a weakness of our previous work is that audio signals are artificially segmented into 20 ms frames for frame-based SVM identification without any contextual information. In this paper, we propose a classification method based on Hidden Markov Modal (HMM) for audio keyword identification as an improved work instead of using hierarchical SVM classifier. Choosing HMM is motivated by the successful story of HMM in speech recognition. Unlike the frame-based SVM classification followed by major voting, our proposed HMM-based classifiers treat specific sound as a continuous time series data and employ hidden states transition to capture context information. In particular, we study how to find an effective HMM, i.e., determining topology, observation vectors and statistical parameters of HMM. We also compare different HMM structures with different hidden states, and adjust time series data with variable length. Experimental data includes 40 minutes basketball au-dio which comes from real-time sports games. Experimental results show that, for audio keyword generation, the proposed HMM-based method outperforms the previous hierarchical SVM.