Papers for

digital music publishers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Music score comparison made easier with new tree-based algorithm

TSM diff: A new Parallel-Tree-Traversal Algorithm for the Comparison of Music Score Files

Abstract: Music score files are more difficult to compare than their text-based equivalents, as their content is more complex and organised into hierarchical structures. This explains the lack of tools for processing music scores similar to the widely used old Unix diff command. We propose a new algorithm and tool for comparing digital music scores in various encodings, such as MusicXML or MEI, based on a graph-structured abstract representation called Tree Score Model (TSM). Given two scores, after conversion into TSM, our procedure calculates jointly: A list of differences, localised by time positions in the input scores; A time distance, cumulated duration of time-spans where scores differ; An edit distance, called surface distance, based on notational attributes not related to time. The two later measures are normalised and combined into a similarity distance between scores. Our procedure has been implemented in C++ and is available as a Python module. Our first experiments have shown a very good efficiency even on challenging data. As a proof of concept, we propose two demos based on different versions of some Mozart's piano sonata, and transcriptions of monophonic soli on the same jazz standards by different performers. https://tsm-diff-lbd-7a7e2a.gitlab.io/demo.html

Wed 28 OctMultimedia

The gist

Comparing digital music scores is tricky because they have complex structures, unlike plain text files. The authors developed an algorithm that converts music scores into a graph-like model, then finds differences based on timing and notation. Their method also gives scores telling how much two versions differ, combining both timing and notation changes. They built a tool implementing this algorithm and tested it on Mozart sonatas and jazz transcriptions, showing it works efficiently.

Open → 2610.38545v1

Musical-time coordinates improve piano audio transcription of full pieces

A Learned Musical-Time Coordinate for Piano Audio-to-Score Transcription

Abstract: End-to-end piano audio-to-score (A2S) transcription is the task of reconstructing a notated score from an audio recording in a single system. Existing A2S approaches usually rely on pre-segmented excerpts, and few handle whole pieces without external segmentation. This raises the question of whether a musical-time coordinate can guide score transcription while also helping the system segment whole pieces. Previous work on jazz saxophone A2S uses an external beat tracker to obtain such a coordinate for score prediction. We introduce downbeat phase, a musical-time coordinate learned jointly with score transcription that represents each audio frame's relative position within a notated bar. With this coordinate, our system can not only condition its score decoder on downbeat phase but also self-segment at bar boundaries and stitch the segments into a whole piece. Evaluation suggests that the coordinate improves metrical structure and supports whole-piece transcription with comparable notation quality.

Wed 21 OctSound

The gist

Turning a full piano recording into written music notes is hard because computer systems usually need the music broken into small parts first. The authors created a new way to teach a system when each note happens in the musical bar without extra tools. This helps the system find bar boundaries by itself and write down notes for whole pieces, not just small clips. Their approach makes the resulting music scores keep the rhythm structure better and works for entire performances.

Open → 2610.29928v1