Abstract
Recently, content-based music information retrieval has been proposed as a support to facilitate music recommendation, music recognition and music retrieval. For music recognition, although it has been investigated by many studies, it is still a challenging issue for how to effectively learn from music contents. Actually, effective music recognition is achieved by considering two factors, namely feature content and learning strategy. Therefore, in this paper, a content-based music classifier named Progressive-Learning- based Music Classifier (PLMC) is proposed to aim at issues of feature content and learning strategy. In terms of feature content, the audio features are upgraded as the advanced features to enhance quality of features. In terms of learning strategy, a progressive learning strategy is proposed by fusing K-Nearest-Neighbor learning and Support Vector Machine learning. Through the proposed progressive learning, the better classification precision can be reached. The experimental results on real music data show the proposed idea performs better than the state-of-the-arts methods in classifying music.
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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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Su, JH., Chin, CY., Hong, TP., Su, JJ. (2019). Content-Based Music Classification by Advanced Features and Progressive Learning. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11432. Springer, Cham. https://doi.org/10.1007/978-3-030-14802-7_10
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