| Beide Seiten der vorigen Revision Vorhergehende Überarbeitung Nächste Überarbeitung | Vorhergehende Überarbeitung |
| en:start [2021/09/10 15:18] – martin | en:start [2025/12/18 15:53] (aktuell) – martin |
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| {{ :pianoroll.png?300|}} | ====== New Toolkit for Sheet Music Analysis ====== |
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| ===== Welcome to the "Computational Music Analysis Fellowship Project"! ===== | Starting in September 2025, the CAMAT toolbox will be redeveloped and substantially extended within the research project //A Comprehensive Cloud-Based Toolbox for Sheet Music Analysis//.\\ |
| | You can follow the project’s progress, including alpha releases and datasets, on our GitHub project website: |
| | [[https://github.com/analyse-hfm-weimar|https://github.com/analyse-hfm-weimar]] |
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| * [[modules|Teaching units and modules]] | |
| * [[documentation|Resources and Documentation]] | |
| * [[research|The project]] | |
| * [[composers|Link to sheet music database]] | |
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| Courses on musical analysis are an integral part of both musicology courses and the training of music teachers and musicians at universities and conservatories. The goal of the fellowship project is to design, test, evaluate, and teach several flexibly applicable teaching modules on music analysis, with recourse to various computer-based analysis tools. The teaching modules are dedicated to computer-based annotation and visualization of musical texts and audio files, statistical analysis of music corpora, and search for musical patterns (melodies, rhythms etc.). They are intended to complement conventional analysis courses, have been and evaluated within several courses at the HfM Weimar, and will are available free of charge to a wider circle of interested parties via an Internet platform. | The goal of this project is to develop an open-source, Python-based toolkit for interactive sheet music analysis. Built on |
| | [[https://www.verovio.org/|Verovio]], |
| | [[https://mei-friend.mdw.ac.at/|mei-friend]], and |
| | [[https://jupyter.org/|Jupyter]]—in particular via the cloud-based [[https://nfdi-jupyter.de/|Jupyter4NFDI]] platform—the toolbox enables comprehensive exploration, annotation, visualization, and statistical analysis of symbolic music data as well as pattern-search methods for melodic, harmonic, rhythmic, and textural analysis. Supported formats include MEI, kern, and MusicXML, also supporting established parsers such as [[https://web.mit.edu/music21/|music21]] and [[https://github.com/CPJKU/partitura|Partitura]]. |
| | The toolbox allows interactive score rendering and customizable corpus creation from validated OMR-based sources. Reproducible Jupyter workflows are combined with openly available datasets, in particular data derived from the |
| | [[https://www.musiconn.de/|musiconn]] project, including the //Denkmäler der deutschen Tonkunst// (Series I & II), as well as selected works from the early 20th century. |
| | By integrating and producing openly accessible corpora in MEI format, the project aims to lower barriers to computational music analysis and to foster the broader adoption of digital methods in musicology, music theory, and music pedagogy. |
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| Computers can be used as aids in the analysis of musical texts and recordings. Computer programs can be used to quickly and reliably | |
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| * visualize musical sequences and structures, | Project runtime: September 2025 until August 2028\\ |
| * statistically describe musical characteristics of the pieces in question (e.g. frequencies of pitches) | Funded by the //German Research Foundation// (DFG), program //Library and Information Services - E-Research Technologies// (LIS) (PF 669/18-1)\\ \\ |
| * and searched for specific patterns (e.g. melodic motifs). | {{:dfg_logo_schriftzug_blau-768x98.jpg?200|}} |
| | \\ |
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| This extends conventional approaches to analysis and opens up and explores new perspectives of musical analysis in musicology and music theory. On the one hand, the computer tools can be used to pursue specific analytical questions, and on the other hand, a playful approach to the tools and note files enables the discovery of unexpected relationships - which can then lead to new analytical questions. | Research team: |
| | * Prof. Dr. Martin Pfleiderer (applicant) |
| | * Dr. Egor Polyakov (scientific assistant) |
| | * Pia Steuck (student assistant) |
| | \\ |
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| All software used in the teaching units is freely accessible and license-free. Thus, the project follows the principle of open access - open access to publicly funded project results and independence from commercially oriented IT corporations. | |
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| **[[modules|Teaching units and modules]]** | ====== Fellowship project Computer-Assisted Music Analysis ====== |
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| Two teaching units are provided that introduce various possibilities of computer-assisted analysis of sheet music or audio files on the basis of music-analytical issues. Each teaching unit consists of a basic module (//Basics Sheet Music// or //Basics Audio//) and a specialization (//Advanced//). The teaching units can be used in self-study or within courses. The duration of the teaching units is approximately three sessions of 90-minutes, with additional preparation, homework and optional specializations. | * [[en:audio|Audio Analysis]] |
| | * [[en:noten|Sheet Music Analysis]] |
| | * [[en:datenbank|Sheet Music Database]] |
| | * [[en:installation|Software Installation]] |
| | * [[en:dokumentation|Resources and Documentation]] |
| | * [[en:forschung|The project]] |
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| **[[documentation|Resources and Documentation]]** | |
| |
| These pages contain information on installing the required software, a [[composers|database]] with score files of several 1000 sheet music files, a comprehensive documentation of score analysis programs developed in the project, links to similar research projects and publications and more materials. | Courses on musical analysis are an integral part of both musicology courses and the training of music teachers and musicians at universities and conservatories. The goal of the fellowship project is to design, test, evaluate, and teach several flexibly applicable teaching modules on music analysis, with recourse to various computer-based analysis tools. The teaching modules are dedicated to computer-based annotation and visualization of musical texts and audio files, statistical analysis of music corpora, and search for musical patterns (melodies, rhythms etc.). They are intended to complement conventional analysis courses, have been and evaluated within several courses at the HfM Weimar, and will are publicly available to a wider circle of interested parties via this Internet platform. |
| | |
| | Computers can be used as aids in the analysis of musical texts and recordings. Computer programs can be used to quickly and reliably |
| | |
| | * visualize musical sequences and structures, |
| | * statistically describe musical characteristics of the pieces in question (e.g. frequencies of pitches) |
| | * and searched for specific patterns (e.g. melodic motifs). |
| | |
| | This extends conventional approaches to analysis and opens up and explores new perspectives of musical analysis in musicology and music theory. On the one hand, the computer tools can be used to pursue specific analytical questions, and on the other hand, a playful approach to the tools and note files enables the discovery of unexpected relationships - which can then lead to new analytical questions. |
| |
| | The [[tutorials|Teaching modules and tutorials]] are provided that introduce various possibilities of computer-assisted analysis of sheet music or audio files on the basis of music-analytical issues. Each teaching unit consists of a basic module (//Basics Sheet Music// or //Basics Audio//) and a specialization (//Advanced//). The teaching units can be used in self-study or within courses. The duration of the teaching units is approximately three sessions of 90-minutes, with additional preparation, homework and optional specializations. |
| |
| **[[research|The project]]** | All [[en:installation|software]] used in the teaching units is freely accessible and license-free. Thus, the project follows the principle of open access - open access to publicly funded project results and independence from commercially oriented IT corporations. |
| |
| Further information about the project's objectives, staff etc. can be found on the project page. | The project //Computergestützte Musikanalyse in der digitalen Hochschullehre// (computer-aided music analysis within digital higher education) is located at the Institute of Musicology Weimar-Jena of the Franz Liszt University of Music Weimar. It was funded in 2021 by the Thuringian Ministry for Economy, Science and Digital Change and the //Stifterverband//. The project sees itself as a contribution to Computational Musicology or Digital Musicology within Digital Humanities. Further information about the project's objectives, staff etc. can be found on [[research|The project]]. |
| The project //Computergestützte Musikanalyse in der digitalen Hochschullehre// (computer-aided music analysis within digital higher education) is located at the Institute of Musicology Weimar-Jena of the Franz Liszt University of Music Weimar. It is funded by the Thuringian Ministry for Economy, Science and Digital Change and the //Stifterverband//. The project sees itself as a contribution to Computational Musicology or Digital Musicology within Digital Humanities. | |
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| The project is currently in the test phase. Feedback is welcome: [[analyse@hfm-weimar.de]] | Feedback is welcome: [[analyse@hfm-weimar.de]] |
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| **Imprint:** \\ | **Imprint:** \\ |
| 99425 Weimar \\ | 99425 Weimar \\ |
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| To the [[https://www.hfm-weimar.de/datenschutz|Privacy Policy]] of the HfM Weimar. | [[https://www.hfm-weimar.de/footer-navigation/privacy-policy/?L=1&cHash=b96a2b0841243026ea890f589de24a8f&dt=1%3Fphlfkfcjekfcjmop%3Fophdjmophlfcjmoh|Privacy Policy]] of the HfM Weimar. |
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