Music Genre Classification via Compressive Sampling

Compressive sampling (CS) is a new research topic in signal processing that has piqued the interest of a wide range of researchers in different fields recently. In this paper, we present a CS-based classifier for music genre classification, with two sets of features, including short-time and long-time features of audio music. The proposed classifier generates a compact signature to achieve a significant reduction in the dimensionality of the audio music signals. The experimental results demonstrate that the computation time of the CS-based classifier is only about 20% of SVM on GTZAN dataset, with an accuracy of 92.7%. Several experiments were conducted in this study to illustrate the feasibility and robustness of the proposed methods as compared to other approaches.

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