báo cáo hóa học:" Research Article Audio Query by Example Using Similarity Measures between Probability Density Functions of Features"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Audio Query by Example Using Similarity Measures between Probability Density Functions of Features | Hindawi Publishing Corporation EURASIP Journal on Audio Speech and Music Processing Volume 2010 Article ID 179303 12 pages doi 2010 179303 Research Article Audio Query by Example Using Similarity Measures between Probability Density Functions of Features Marko Helen and Tuomas Virtanen EURASIP Member Department of Signal Processing Tampere University of Technology Korkeakoulunkatu 1 33720 Tampere Finland Correspondence should be addressed to Marko Helen Received 22 May 2009 Revised 14 October 2009 Accepted 9 November 2009 Academic Editor Bhiksha Raj Copyright 2010 M. Helen and T. Virtanen. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. This paper proposes a query by example system for generic audio. We estimate the similarity of the example signal and the samples in the queried database by calculating the distance between the probability density functions pdfs of their frame-wise acoustic features. Since the features are continuous valued we propose to model them using Gaussian mixture models GMMs or hidden Markov models HMMs . The models parametrize each sample efficiently and retain sufficient information for similarity measurement. To measure the distance between the models we apply a novel Euclidean distance approximations of Kullback-Leibler divergence and a cross-likelihood ratio test. The performance of the measures was tested in simulations where audio samples are automatically retrieved from a general audio database based on the estimated similarity to a user-provided example. The simulations show that the distance between probability density functions is an accurate measure for similarity. Measures based on GMMs or HMMs are shown to produce better results than that of the existing methods based on simpler statistics or histograms of the features. A good performance .

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