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Music Classification by Automated Relevance Feedbacks

  • Ja-Hwung Su
  • Tzung-Pei HongEmail author
  • Hsuan-Hao Yeh
Conference paper
  • 222 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1178)

Abstract

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.

Keywords

Music recognition Acoustic features Conceptual features Relevance feedback 

Notes

Acknowledgement

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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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Information ManagementCheng Shiu UniversityKaohsiungTaiwan
  2. 2.Department of Computer Science and Information EngineeringNational University of KaohsiungKaohsiungTaiwan
  3. 3.Department of Computer Science and EngineeringNational Sun Yat-sen UniversityKaohsiungTaiwan

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