Importance of audio feature reduction in automatic music genre classification

Author:

Baniya Babu Kaji,Lee Joonwhoan

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Reference30 articles.

1. Baniya BK, Ghimire D, Lee J (2013) Evaluation of different audio features for musical genre classification. In Proc. IEEE workshop on Signal Processing Systems, Taipei, Taiwan

2. Baniya BK, Ghimire D, Lee J (2014) A novel approach of automatic music genre classification based on timbral texture and rhythmic content features. Int. conference on Advance Communications Technology (ICACT), pp.96–102

3. Belkin M, Niyogi P (2002) “Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering”, Advances in Neural Information Processing Systems 14. Vancouver, British Columbia

4. Benetos E, Kotropoulos C (2008) A tensor-based approach for automatic music genre classification. Proceedings of the European Signal Processing Conference, Lausanne

5. Bergstra J, Casagrande N, Erhan D, Eck D, Kegl B (2006) Aggregate features and AdaBoost for music classification”. Mach Learn 65(2–3):473–484

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