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    Audio classification based on sinusoidal model: a new feature

    , Article 2008 IEEE Region 10 Conference, TENCON 2008, Hyderabad, 19 November 2008 through 21 November 2008 ; 2008 ; 1424424089 (ISBN); 9781424424085 (ISBN) Shirazi, J ; Ghaemmaghami, S ; Sharif University of Technology
    2008
    Abstract
    In this paper, a new feature set is presented and evaluated based on sinusoidal modeling of audio signals. Duration of the longest sinusoidal model frequency track, as a measure of the harmony, is used and compared to typical features as input into an audio classifier. The performance of this sinusoidal model feature is evaluated through classification of audio to speech and music using both the GMM and the SVM classifiers. Classification results show the proposed feature, which could be used for the first time in such an audio classification, is quite successful in speech/music classification. Experimental comparisons with popular features for audio classification, such as HZCRR and LSTER,... 

    Birth-death frequencies variance of sinusoidal model a new feature for audio classification

    , Article SIGMAP 2008 - International Conference on Signal Processing and Multimedia Applications, Porto, 26 July 2008 through 29 July 2008 ; 2008 , Pages 139-144 ; 9789898111609 (ISBN) Ghaemmaghami, S ; Shirazi, J ; Sharif University of Technology
    2008
    Abstract
    In this paper, a new feature set for audio classification is presented and evaluated based on sinusoidal modeling of audio signals. Variance of the birth-death frequencies in sinusoidal model of signal, as a measure of harmony, is used and compared to typical features as the input into an audio classifier. The performance of this sinusoidal model feature is evaluated through classification of audio to speech and music using both the GMM and the SVM classifiers. Classification results show that the proposed feature is quite successful in speech/music classification. Experimental comparisons with popular features for audio classification, such as HZCRR and LSTER, are presented and discussed....