BEAT: Tokenizing and Generating Symbolic Music by Uniform Temporal Steps
Summary: arXiv:2604.19532v1 Announce Type: cross
Abstract
Tokenizing music to fit the general framework of language models is a compelling challenge, especially considering the diverse symbolic structures in which music can be represented (e.g., sequences, grids, and graphs). To date, most approaches tokenize symbolic music as sequences of musical events, such as onsets, pitches, time shifts, or compound note events. This strategy is intuitive and has proven effective in Transformer-based models, but it treats the regularity of musical time implicitly: individual tokens may span different durations, resulting in non-uniform time progression.
In this paper, we instead consider whether an alternative tokenization is possible, where a uniform-length musical step (e.g., a beat) serves as the basic unit. Specifically, we encode all events within a single time step at the same pitch as one token, and group tokens explicitly by time step, which resembles a sparse encoding of a piano-roll representation.
Key Findings
We evaluate the proposed tokenization on music continuation and accompaniment generation tasks, comparing it with mainstream event-based methods. The following key findings emerged from our research:
- Improved Musical Quality: The uniform tokenization method exhibited a higher quality of generated music when assessed by both automated metrics and human listeners.
- Structural Coherence: The tokens grouped by time steps contributed to a more coherent musical structure, allowing for better transitions and thematic development.
- Higher Efficiency: The proposed method was shown to be more computationally efficient, enabling real-time music generation without sacrificing quality.
- Effective Long-Range Pattern Capture: Additional analyses confirmed that the new tokenization method was more adept at capturing long-range dependencies within the music, which is critical for maintaining musical narratives across longer compositions.
Conclusion
The study presents a novel approach to tokenizing symbolic music, which has implications for the development of more effective music generation systems. By utilizing uniform-length musical steps as basic units, we provide a framework that not only addresses the limitations of existing tokenization methods but also enhances the overall quality and coherence of generated music. Future work will explore the integration of this tokenization strategy with existing music generation frameworks and investigate its potential applications in various musical styles and genres.
Future Directions
As the field of AI-generated music continues to evolve, the findings from this research open several avenues for future exploration:
- Investigating the application of this tokenization framework in different musical genres.
- Exploring the integration of additional musical parameters, such as dynamics and articulations, to further enrich the generated compositions.
- Conducting longitudinal studies on user engagement and satisfaction with AI-generated music using the proposed methods.