NDLI: Novel approach for detecting applause in continuous meeting speech

Content Provider	IEEE Xplore Digital Library
Author	Manoj, C. Magesh, S. Sankaran, A.S. Manikandan, M.S.
Copyright Year	2011
Description	Author affiliation: Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India (Manoj, C.; Magesh, S.; Sankaran, A.S.; Manikandan, M.S.)
Abstract	This paper proposes a robust and automated applause detection algorithm for meeting speech. The features used in the proposed algorithm are the short-time autocorrelation features such as autocorrelation energy decay factor, amplitude and lag values of first local minimum and zero-crossing points extracted from the autocorrelation sequence of a windowed audio signal. We apply decision thresholds for the above acoustic features to identify applause and non-applause segments from the audio stream. The performance of the proposed algorithm is compared with the conventional method using mel frequency cepstral coefficients (MFCC) feature vectors and Gaussian mixture model (GMM) as classifier. We have also analyzed the performance of these algorithms by varying the number of mixtures in GMM (2, 4, 8, 16 and 32) and various thresholds in the proposed method. The methods are tested with a multimedia database of 4 hours 37 minutes of meeting speech and the results are compared. The precision rate, recall rate and F1 score of the proposed method are 94.40%, 90.75% and 92.54% respectively while those of conventional method are 67.47%, 96.13% and 79.29% respectively.
Starting Page	182
Ending Page	186
File Size	270403
Page Count	5
File Format	PDF
ISBN	9781424486786
e-ISBN	9781424486793
DOI	10.1109/ICECTECH.2011.5941827
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-08
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Noise Audio classification Hidden Markov models sports highlight extraction semantic video analysis Speech Feature extraction Finite impulse response filter Mel frequency cepstral coefficient video summarization audio content analysis
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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