Chang Jie, Wang Zhenmeng, Yan Chao
School of Music, Sangmyung University, Seoul, Republic of Korea.
School of Music, Qufu Normal University, Rizhao, China.
Front Neurorobot. 2024 Nov 21;18:1479694. doi: 10.3389/fnbot.2024.1479694. eCollection 2024.
In recent years, with the rapid development of artificial intelligence technology, the field of music education has begun to explore new teaching models. Traditional music education research methods have primarily focused on single-modal studies such as note recognition and instrument performance techniques, often overlooking the importance of multimodal data integration and interactive teaching. Existing methods often struggle with handling multimodal data effectively, unable to fully utilize visual, auditory, and textual information for comprehensive analysis, which limits the effectiveness of teaching.
To address these challenges, this project introduces MusicARLtrans Net, a multimodal interactive music education agent system driven by reinforcement learning. The system integrates Speech-to-Text (STT) technology to achieve accurate transcription of user voice commands, utilizes the ALBEF (Align Before Fuse) model for aligning and integrating multimodal data, and applies reinforcement learning to optimize teaching strategies.
This approach provides a personalized and real-time feedback interactive learning experience by effectively combining auditory, visual, and textual information. The system collects and annotates multimodal data related to music education, trains and integrates various modules, and ultimately delivers an efficient and intelligent music education agent. Experimental results demonstrate that MusicARLtrans Net significantly outperforms traditional methods, achieving an accuracy of on the LibriSpeech dataset and on the MS COCO dataset, with marked improvements in recall, F1 score, and AUC metrics. These results highlight the system's superiority in speech recognition accuracy, multimodal data understanding, and teaching strategy optimization, which together lead to enhanced learning outcomes and user satisfaction. The findings hold substantial academic and practical significance, demonstrating the potential of advanced AI-driven systems in revolutionizing music education.
近年来，随着人工智能技术的迅速发展，音乐教育领域开始探索新的教学模式。传统音乐教育研究方法主要集中在诸如音符识别和乐器演奏技巧等单模态研究上，常常忽视多模态数据整合和互动教学的重要性。现有方法在有效处理多模态数据方面往往存在困难，无法充分利用视觉、听觉和文本信息进行综合分析，这限制了教学效果。
为应对这些挑战，本项目引入了MusicARLtrans Net，这是一个由强化学习驱动的多模态交互式音乐教育智能体系统。该系统集成了语音转文本（STT）技术以实现用户语音命令的准确转录，利用ALBEF（融合前对齐）模型对齐和整合多模态数据，并应用强化学习来优化教学策略。
这种方法通过有效结合听觉、视觉和文本信息，提供了个性化的实时反馈交互式学习体验。该系统收集并标注与音乐教育相关的多模态数据，训练并整合各个模块，最终提供一个高效且智能的音乐教育智能体。实验结果表明，MusicARLtrans Net显著优于传统方法，在LibriSpeech数据集上的准确率达到 ，在MS COCO数据集上的准确率达到 ，在召回率、F1分数和AUC指标上有显著提升。这些结果凸显了该系统在语音识别准确率、多模态数据理解和教学策略优化方面的优越性，共同带来了学习效果和用户满意度的提升。这些发现具有重要的学术和实践意义，证明了先进的人工智能驱动系统在变革音乐教育方面的潜力。
