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Random fourier feature based music-speech classification

Publication Type : Journal Article

Publisher : Journal of Intelligence and Fuzzy Systems

Source : Journal of Intelligence and Fuzzy Systems, 2020

Url :

Keywords : feature vector, GTZAN database, Music/speech, random kitchen sink, S&S database, spectral features

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Center for Computational Engineering and Networking (CEN), Electronics and Communication

Year : 2020

Abstract : The present paper proposes Random Kitchen Sink based music/speech classification. The temporal and spectral features such as spectral centroid, Spectral roll-off, spectral flux, Mel-frequency cepstral coefficients, entropy, and Zero-crossing rate are extracted from the signals. In order to show the competence of the proposed approach, experimental evaluations and comparisons are performed. Even though both speech and music signals differ in their production mechanisms, those share many common characteristics such as a common spectrum of frequency and are comparatively non-stationary which makes the classification difficult. The proposed approach explicitly maps the data to a feature space where it is linearly separable. The evaluation results shows that the proposed approach provides competing scores with the methods in the available literature.

Cite this Research Publication : Vyshnav MT, Sachin Kumar S, Neethu Mohan, KP Soman, Random Fourier Feature Based Music-Speech Classification, Journal of Intelligence and Fuzzy Systems, 2020 (Scopus)

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