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How to Infer Repeat Structures in MIDI Performances

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arxiv 2505.05055 v1 pith:VAJXSSGS submitted 2025-05-08 cs.SD eess.AS

How to Infer Repeat Structures in MIDI Performances

classification cs.SD eess.AS
keywords performancealignmentscoregainrepeatstructuralinferlocal
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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MIDI performances are generally expedient in performance research and music information retrieval, and even more so if they can be connected to a score. This connection is usually established by means of alignment, linking either notes or time points between the score and the performance. The first obstacle when trying to establish such an alignment is that a performance realizes one (out of many) structural versions of the score that can plausibly result from instructions such as repeats, variations, and navigation markers like 'dal segno/da capo al coda'. A score needs to be unfolded, that is, its repeats and navigation markers need to be explicitly written out to create a single timeline without jumps matching the performance, before alignment algorithms can be applied. In the curation of large performance corpora this process is carried out manually, as no tools are available to infer the repeat structure of the performance. To ease this process, we develop a method to automatically infer the repeat structure of a MIDI performance, given a symbolically encoded score including repeat and navigation markers. The intuition guiding our design is: 1) local alignment of every contiguous section of the score with a section of a performance containing the same material should receive high alignment gain, whereas local alignment with any other performance section should accrue a low or zero gain. And 2) stitching local alignments together according to a valid structural version of the score should result in an approximate full alignment and correspondingly high global accumulated gain if the structural version corresponds to the performance, and low gain for all other, ill-fitting structural versions.

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  1. Score-Agnostic Structure Analysis in Large-Scale Performance Datasets

    cs.SD 2026-05 unverdicted novelty 5.0

    A score-agnostic pipeline of pairwise alignments followed by clustering on alignment cost and sequence length features groups ~1500 transcriptions of 88 pieces by structural interpretation.