NDLI: A fast and robust speech/music discrimination approach

Content Provider	IEEE Xplore Digital Library
Author	Wang, W.Q. Gao, W. Ying, D.W.
Copyright Year	2003
Description	Author affiliation: Coll. of Inf. Sci. & Eng., Chinese Acad. of Sci., China (Wang, W.Q.; Gao, W.; Ying, D.W.)
Abstract	This paper presents a simple and effective approach to discriminate speech and music. First, the proposed modified low energy ratio is extracted from each window-level segment as the only feature. Then the system applied the Bayes MAP classifier to decide the audio class of each segment. Last, based on the fact that the audio types of neighboring segments have very strong relevance, a novel context-based post-decision method is designed to refine the classification results. The proposed method is evaluated on about 5 hours of audio data, which involves clean and noisy speech from various speakers, as well as a wide range of musical content. The experimental results are promising, and a classification accuracy of more than 97% has been achieved despite the low computation complexity of the method.
Sponsorship	Microsoft Res. Asia Singapore Exhibition and Convention Bureau
Starting Page	1325
Ending Page	1329
File Size	324956
Page Count	5
File Format	PDF
ISBN	0780381858
DOI	10.1109/ICICS.2003.1292679
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-12-15
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Multiple signal classification Automatic speech recognition Streaming media Frequency modulation Artificial intelligence Educational institutions Power engineering and energy Computers Data mining
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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