Generative Models for Retrieval of Video, Audio and Text Data
Velivelli, Atulya
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https://hdl.handle.net/2142/81157
Description
Title
Generative Models for Retrieval of Video, Audio and Text Data
Author(s)
Velivelli, Atulya
Issue Date
2010
Doctoral Committee Chair(s)
Huang, Thomas S.
Department of Study
Electrical and Computer Engineering
Discipline
Electrical and Computer Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Electronics and Electrical
Language
eng
Abstract
"We propose a general approach to audio segment retrieval, which allows a user to query audio data by an example audio segment of a short duration and to find similar segments. The basic idea of our approach is to first train a hidden Markov model (HMM) using the given example, it is called the theme HMM. The total audio data available is used to train a background HMM. We combine these individual HMMs to form a synthesized ""background-theme-background"" HMM. This synthesized HMM can then be applied to any audio stream as a parser to detect the most likely theme segment. A major advantage of this approach is that it does not assume any predefined segment boundaries as in previous work and thus can be expected to retrieve theme segments with more accurate boundaries. We overcome the problem of only being able to use a short duration query to train a theme HMM by using the maximum a posteriori estimator with the background model as a prior model. Evaluation of the proposed retrieval scheme using short duration example audio clips of narration as queries gives quite promising results."
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