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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.07184 |
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| _version_ | 1866908014892548096 |
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| author | Ni-Hahn, Stephen Xu, Weihan Yin, Jerry Zhu, Rico Mak, Simon Jiang, Yue Rudin, Cynthia |
| author_facet | Ni-Hahn, Stephen Xu, Weihan Yin, Jerry Zhu, Rico Mak, Simon Jiang, Yue Rudin, Cynthia |
| contents | Schenkerian Analysis (SchA) is a uniquely expressive method of music analysis, combining elements of melody, harmony, counterpoint, and form to describe the hierarchical structure supporting a work of music. However, despite its powerful analytical utility and potential to improve music understanding and generation, SchA has rarely been utilized by the computer music community. This is in large part due to the paucity of available high-quality data in a computer-readable format. With a larger corpus of Schenkerian data, it may be possible to infuse machine learning models with a deeper understanding of musical structure, thus leading to more "human" results. To encourage further research in Schenkerian analysis and its potential benefits for music informatics and generation, this paper presents three main contributions: 1) a new and growing dataset of SchAs, the largest in human- and computer-readable formats to date (>140 excerpts), 2) a novel software for visualization and collection of SchA data, and 3) a novel, flexible representation of SchA as a heterogeneous-edge graph data structure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07184 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A New Dataset, Notation Software, and Representation for Computational Schenkerian Analysis Ni-Hahn, Stephen Xu, Weihan Yin, Jerry Zhu, Rico Mak, Simon Jiang, Yue Rudin, Cynthia Sound Artificial Intelligence Schenkerian Analysis (SchA) is a uniquely expressive method of music analysis, combining elements of melody, harmony, counterpoint, and form to describe the hierarchical structure supporting a work of music. However, despite its powerful analytical utility and potential to improve music understanding and generation, SchA has rarely been utilized by the computer music community. This is in large part due to the paucity of available high-quality data in a computer-readable format. With a larger corpus of Schenkerian data, it may be possible to infuse machine learning models with a deeper understanding of musical structure, thus leading to more "human" results. To encourage further research in Schenkerian analysis and its potential benefits for music informatics and generation, this paper presents three main contributions: 1) a new and growing dataset of SchAs, the largest in human- and computer-readable formats to date (>140 excerpts), 2) a novel software for visualization and collection of SchA data, and 3) a novel, flexible representation of SchA as a heterogeneous-edge graph data structure. |
| title | A New Dataset, Notation Software, and Representation for Computational Schenkerian Analysis |
| topic | Sound Artificial Intelligence |
| url | https://arxiv.org/abs/2408.07184 |