arXiv:2605.24291v1 Announce Type: cross Abstract: We consider the conversion of musical recordings into human-readable sheet music annotated with timestamps. Such output lets a listener clearly visualize rubato (temporally expressive playing), a learner diagnose ensemble precision and timing choices against the written music, and a musicology scholar compare performance styles across recordings of the same work. We introduce (1) a prompt-conditioned encoder-decoder model, named Rubato, trained to output (2) a new textual representation for polyphonic music, named InterMo, which we designed for
The continuous advancements in AI and natural language processing are enabling more sophisticated interpretations of complex data types, such as polyphonic music.
This development could significantly enhance the accessibility and analytical depth of musical recordings, providing new tools for education, research, and creative industries.
The ability to automatically transcribe expressive musical performances with high fidelity and temporal accuracy changes how musical data can be processed, analyzed, and visualized.
- · Musicology researchers
- · Music educators
- · Musicians/Composers
- · AI/ML developers
- · Traditional manual transcribers
More precise and automated tools for analyzing musical performance will become available.
This could lead to new insights into performance practices and historical musical styles across vast datasets.
The technology might enable new forms of interactive music education or AI-driven musical composition and performance generation.
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