By combining multimodal feature extraction, personalized recommendation, and reinforcement learning (RL)-based approach optimization, this study suggests a Smart Interdisciplinary Environment for Adaptive and Optimizing Vocal Music Instruction. Let the action set represent selectable teaching options, and let the student state vector be , which captures pitch, rhythm, tonal quality, and expressivity at time . Personalized recommendations are generated via , where and are embeddings for the user and exercises, and are trainable network parameters. The RL objective maximizes the expected cumulative reward, , where quantifies immediate performance improvement, and is the discount factor. Multimodal fusion of audio, video, and sentiment features is formulated as , and the network parameters are updated using , with denoting cross-entropy or MSE loss. Assessment of DAMP Sing! Performance and VocalSet datasets show that recommendation accuracy , strategy stability , and generation diversity index are significantly higher than baseline models ( 0.60 ), demonstrating improved adaptive guidance. Furthermore, learning enhancement proportions , audio feature extraction reliability , sentiment recognition precision ,, and system efficacy confirm that the proposed framework provides personalized, dependable, and successful vocal music tutorials, outperforming previous approaches in both technical and expressive learning dimensions.
Research Article | Open Access | Download Full Text
Volume 3 | Issue 1 | Year 2026 | Article Id: DSM-V3I1P101 DOI: https://doi.org/10.59232/DSM-V3I1P101
Smart Interdisciplinary Framework for Adaptive and Optimized Vocal Music Instruction
Sushma Jaiswal, Tarun Jaiswal, Payal Sahu, Aditi Gopal, Swapnil Kumar Sahu, Bharat Bhushan Mahilane
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 05 Dec 2025 | 02 Jan 2026 | 28 Jan 2026 | 07 Feb 2026 |
Citation
Sushma Jaiswal, Tarun Jaiswal, Payal Sahu, Aditi Gopal, Swapnil Kumar Sahu, Bharat Bhushan Mahilane. “Smart Interdisciplinary Framework for Adaptive and Optimized Vocal Music Instruction.” DS Journal of Multidisciplinary, vol. 3, no. 1, pp. 1-16, 2026.
Abstract
Keywords
RL, Multimodal Feature Integration, Adaptive Vocal Music Teaching, Hierarchical-Attention Networks, Audio-Visual Emotion Recognition.
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