NDLI: Improvements in audio classification based on sinusoidal modeling

Content Provider	IEEE Xplore Digital Library
Author	Shirazi, J. Ghaemmaghami, S. Razzazi, F.
Copyright Year	2008
Description	Author affiliation: Islamic Azad Univ., Gonabad (Shirazi, J.)
Abstract	In this paper, a set of features is presented and evaluated based on sinusoidal modeling of audio signals. Amplitude, frequency, and phase parameters of the sinusoidal model are used and compared as input features into an audio classifier system. The performance of sinusoidal model features is evaluated for classification of audio into speech and music classes using both the Gaussian and the GMM (Gaussian mixture model) classifiers. Experimental results show superiority of the amplitude parameters of the sinusoidal model, which could be used for the first time for such an audio classification, as compared to the popular cepstral features. By using a set of 40 sinusoidal features, we achieved 95.06% accuracy in the audio classification at frame level, as compared to 92.26% accuracy obtained with the MFCC coefficients, as tested over the same audio corpus.
Starting Page	1485
Ending Page	1488
File Size	183407
Page Count	4
File Format	PDF
ISBN	9781424425709
DOI	10.1109/ICME.2008.4607727
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech Music Classification algorithms Mel frequency cepstral coefficient Support vector machine classification Accuracy Feature extraction
Content Type	Text
Resource Type	Article

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