Sound Effects Classification and Retrieval

  • Tong Zhang
  • C.-C. Jay Kuo
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 606)


The core of the fine-level classification and retrieval of environmental sound (including sound effects) is to build the hidden Markov model (HMM) for each class or clip of sound(s). Currently, two types of information are contained in the model, i.e. timbre and rhythm. Each kind of timbre is modeled as one state of the HMM, and represented with the Gaussian mixture density. The rhythm information is included in the transition parameters of HMM. For sound effects classification, HMM serves as the classifier; while for sound effects retrieval, it is the similarity measure.


Feature Vector Hide Markov Model Gaussian Mixture Model Viterbi Algorithm Sound Effect 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Tong Zhang
    • 1
  • C.-C. Jay Kuo
    • 2
  1. 1.Integrated Media Systems CenterUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Department of Electrical Engineering — SystemsUniversity of Southern CaliforniaLos AngelesUSA

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