Music Classification by Automated Relevance Feedbacks
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Music recognition systems help users and music platform developers analyze what genre a music piece belongs to. In this paper, we propose an effective automatic music recognition system to help developers effectively tag music with genres. First, we extract Mel-Frequency Cepstral Coefficients (MFCCs) as the basic features. We then transform MFCCs into a set of conceptual features by Support Vector Machine (SVM). By the conceptual features, a automatic relevance feedback method is performed to generate a navigation model, which can be viewed as a recognition model. In the recognition phase, the proposed approach, called music classification by navigation paths (MCNP) uses these conceptual features to recognize the unknown music. The experimental results show that the proposed method is more promising than the state-of-the-arts on music classification.
KeywordsMusic recognition Acoustic features Conceptual features Relevance feedback
This research was supported by Ministry of Science and Technology, Taiwan, R.O.C. under grant no. MOST 107-2221-E-230-010.
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