Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation (2210.10349v2)

Abstract: Symbolic music generation aims to generate music scores automatically. A recent trend is to use Transformer or its variants in music generation, which is, however, suboptimal, because the full attention cannot efficiently model the typically long music sequences (e.g., over 10,000 tokens), and the existing models have shortcomings in generating musical repetition structures. In this paper, we propose Museformer, a Transformer with a novel fine- and coarse-grained attention for music generation. Specifically, with the fine-grained attention, a token of a specific bar directly attends to all the tokens of the bars that are most relevant to music structures (e.g., the previous 1st, 2nd, 4th and 8th bars, selected via similarity statistics); with the coarse-grained attention, a token only attends to the summarization of the other bars rather than each token of them so as to reduce the computational cost. The advantages are two-fold. First, it can capture both music structure-related correlations via the fine-grained attention, and other contextual information via the coarse-grained attention. Second, it is efficient and can model over 3X longer music sequences compared to its full-attention counterpart. Both objective and subjective experimental results demonstrate its ability to generate long music sequences with high quality and better structures.

• The paper presents Museformer’s dual attention mechanism, combining fine- and coarse-grained strategies for detailed structure and efficiency in music generation.
• The model achieves lower perplexity and similarity error scores, demonstrating improved prediction capabilities and structural fidelity compared to previous approaches.
• Evaluations reveal that Museformer produces aesthetically pleasing compositions, offering new insights and practical tools for AI-driven music creation.

An Expert Analysis of "Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation"

The paper presents Museformer, a novel architecture focused on enhancing symbolic music generation by addressing two primary challenges inherent in music modeling: the handling of long sequences and the generation of structured repetition. Traditional Transformer models present limitations due to the quadratic complexity of full attention, which is ill-suited for the extensive sequences typical in music compositions. Furthermore, existing models struggle to effectively recreate the repetitive and variational structures characteristic of human-composed music.

Key Innovations of Museformer

Museformer introduces a dual-attention mechanism—fine- and coarse-grained attention—that optimizes the balance between capturing detailed musical structure and managing computational efficiency:

1. Fine-Grained Attention: This attention type targets the structure-related bars. These bars, identified based on statistical similarity measures derived from human-composed music, encompass musical segments likely to be repeated. The model selects the previous 1st, 2nd, 4th, 8th bars, among others, as targets for detailed attention using similarity patterns observed in real music compositions.
2. Coarse-Grained Attention: For less critical contexts which do not contribute directly to the structural repetition pattern, Museformer implements coarse-grained attention. Here, instead of focusing on individual tokens, a summary of these tokens is used, enabling a significant reduction in computational load without sacrificing the contextual richness needed for music generation.

Performance and Evaluation

Museformer's efficacy is underscored through both objective and subjective evaluations. The model excels in generating high-quality, full-length music sequences:

• Perplexity: Museformer demonstrates superior perplexity scores across varying sequence lengths, indicating improved predictive capabilities for token sequences in music generation.
• Similarity Error (SE): The reduced SE showcases Museformer’s effectiveness in emulating structured patterns akin to human-composed music.

Subjective assessments further validate these findings, with Museformer receiving higher ratings for musicality and structure from human evaluators against key benchmarks like Music Transformer and Linear Transformer. This demonstrates Museformer's ability to produce compositions that are both aesthetically pleasing and structurally robust.

Implications and Future Directions

The development of Museformer highlights significant implications both in practical and theoretical realms of music AI. Practically, it equips composers and producers with a tool capable of generating intricate and realistic musical sequences, potentially influencing genres that rely heavily on structural repetition and development, such as classical and electronic music.

Theoretically, Museformer opens avenues for further explorations into context-aware sequence modeling and efficient attention mechanisms in highly structured data types. Future work could extend into enhanced control during generation, offering users the ability to guide structural patterns actively, thus increasing the creativity and applicability of AI-generated music.

Additionally, while the current focus is on music, the methodologies innovated through Museformer could inform other domains requiring nuanced structure recognition, such as multi-paragraph text generation or long-form video content synthesis.

In summary, Museformer's contributions demonstrate a significant stride in addressing the complexities of musical structure generation, paving the way for more sophisticated and contextually aware AI systems in creative fields.