做 AI 音乐生成或音频研究的团队可以关注——这个机制直接解决了 Transformer 生成音乐时“重复啰嗦”的痛点，用元信息让旋律更自然，值得在自家模型上试试。
该研究提出 Musical Attention 机制，通过将小节号、调号、拍号和速度等元信息融入注意力计算，解决 Transformer 生成音乐时常见的重复和音符冗余问题。每个音符被表示为音高、小节号、起始时间、时长、力度及三个元信息共八个特征，注意力机制据此调整相关性权重。实验表明，该方法在音乐连贯性、多样性和整体质量上优于 Full Attention 和 Strided Attention，显著减少重复并增强和声一致性。这项工作是 AI 音乐生成领域的重要进展，有助于生成更自然、富有表现力的旋律。
Musical Attention Transformer: Music Generation Using a Music-Specific Attention Model
This study aims to enhance the quality of music generation using Transformers by incorporating meta-information. While Transformer-based approaches are effective at capturing long-term dependencies in musical compositions, the music they generate often suffers from issues such as excessive repetition or duplication of notes, leading to unnatural melodies. To address these limitations, we propose Musical Attention, a mechanism that incorporates meta-information such as bar numbers, key, signatures, and tempos into the attention process. Musical Attention explicitly leverages both the structural properties of music and its associated metadata, enabling the Transformer's attention mechanism to operate more effectively and thereby improving the quality of the generated output. In our framework, each musical note is represented as a combination of five events-pitch, bar number, onset, duration, and velocity in addition to the three metadata elements. The attention mechanism is then modified to reflect the correlations among these eight features, allowing the model to better capture the inherent characteristics of musical composition. Experimental results demonstrate that the model incorporating Musical Attention outperforms prior methods, such as Full Attention and Strided Attention, in terms of musical coherence, variation, and overall quality. Notably, it significantly reduces repetition and enhances the model's ability to generate diverse, harmonically consistent melodies. Musical Attention thus represents a meaningful advancement in AI-driven music generation, facilitating the creation of more natural and expressive compositions.