{"id":10182,"date":"2026-08-17T21:30:54","date_gmt":"2026-08-17T13:30:54","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=10182"},"modified":"2026-08-18T00:00:46","modified_gmt":"2026-08-17T16:00:46","slug":"jase-202611-34-051","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-051","title":{"rendered":"Hybrid Machine Learning for Musical Style Recognition and Composer-Aware Music Generation"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 34<\/a><\/p>\n<\/div>\n\n\n\n

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Xin Xiong<\/i><\/a><\/p>\n\n\n\n

School of Humanities and Education, Wuchang Institute of Technology, Hubei 430065, China<\/p>\n<\/div>\n\n\n\n

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Received: April 24, 2026
Accepted: July 08, 2026
Publication Date: August 17, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Block Diagram of the Proposed Method<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202611_34.051<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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The rapid growth of digital music collections has increased the demand for automatic composer identification and musical style recognition. This study proposes a hybrid framework using the MAESTRO v3.0.0 symbolic MIDI dataset. EB-SMR preprocesses MIDI by extracting pitch, onset, duration, and velocity, followed by tempo, pitch, and duration normalization. The data are converted into piano-roll representations. Handcrafted symbolic features capturing melodic, rhythmic, and dynamic properties are combined with Convolutional Neural Network (CNN)-based deep features, enabling improved feature learning for composer-aware music generation and style recognition tasks. These complementary features are fused into a unified representation and classified using a Hybrid Feature-Based Softmax Neural Network (HFSNN), while the Lion optimizer is employed as a training strategy to improve convergence efficiency during optimization. The framework incorporates composer-aware music generation by sampling musical events from class-conditional distributions to produce stylistically coherent MIDI sequences. Experimental results show that the hybrid framework achieves 0.97, outperforming CNN-only and handcrafted-feature models. Combining symbolic descriptors and CNN features improves style\/composer recognition, enables composer-aware MIDI generation, but is limited to piano MIDI; future work explores multimodal and multi-instrument representations.<\/p>\n\n\n\n

Keywords: Symbolic Music Analysis, Musical Instrument Digital Interface, Hybrid Feature Extraction, Piano-Roll Representation, Composer Classification<\/em><\/p>\n\n\n\n

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