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| Autori principali: | , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.24603 |
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| _version_ | 1866912614992314368 |
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| author | Wang, Tianle Zhang, Sirui Tong, Xinyi Yu, Peiyang Chen, Jishang Zhao, Liangke Gao, Xinpu Zhu, Yves Ge, Tiezheng Zheng, Bo Xu, Duo Liu, Yang Jin, Xin Yu, Feng Zhu, Songchun |
| author_facet | Wang, Tianle Zhang, Sirui Tong, Xinyi Yu, Peiyang Chen, Jishang Zhao, Liangke Gao, Xinpu Zhu, Yves Ge, Tiezheng Zheng, Bo Xu, Duo Liu, Yang Jin, Xin Yu, Feng Zhu, Songchun |
| contents | This paper presents an unsupervised machine learning algorithm that identifies recurring patterns -- referred to as ``music-words'' -- from symbolic music data. These patterns are fundamental to musical structure and reflect the cognitive processes involved in composition. However, extracting these patterns remains challenging because of the inherent semantic ambiguity in musical interpretation. We formulate the task of music-word discovery as a statistical optimization problem and propose a two-stage Expectation-Maximization (EM)-based learning framework: 1. Developing a music-word dictionary; 2. Reconstructing the music data. When evaluated against human expert annotations, the algorithm achieved an Intersection over Union (IoU) score of 0.61. Our findings indicate that minimizing code length effectively addresses semantic ambiguity, suggesting that human optimization of encoding systems shapes musical semantics. This approach enables computers to extract ``basic building blocks'' from music data, facilitating structural analysis and sparse encoding. The method has two primary applications. First, in AI music, it supports downstream tasks such as music generation, classification, style transfer, and improvisation. Second, in musicology, it provides a tool for analyzing compositional patterns and offers insights into the principle of minimal encoding across diverse musical styles and composers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24603 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Discovering "Words" in Music: Unsupervised Learning of Compositional Sparse Code for Symbolic Music Wang, Tianle Zhang, Sirui Tong, Xinyi Yu, Peiyang Chen, Jishang Zhao, Liangke Gao, Xinpu Zhu, Yves Ge, Tiezheng Zheng, Bo Xu, Duo Liu, Yang Jin, Xin Yu, Feng Zhu, Songchun Sound Computer Vision and Pattern Recognition This paper presents an unsupervised machine learning algorithm that identifies recurring patterns -- referred to as ``music-words'' -- from symbolic music data. These patterns are fundamental to musical structure and reflect the cognitive processes involved in composition. However, extracting these patterns remains challenging because of the inherent semantic ambiguity in musical interpretation. We formulate the task of music-word discovery as a statistical optimization problem and propose a two-stage Expectation-Maximization (EM)-based learning framework: 1. Developing a music-word dictionary; 2. Reconstructing the music data. When evaluated against human expert annotations, the algorithm achieved an Intersection over Union (IoU) score of 0.61. Our findings indicate that minimizing code length effectively addresses semantic ambiguity, suggesting that human optimization of encoding systems shapes musical semantics. This approach enables computers to extract ``basic building blocks'' from music data, facilitating structural analysis and sparse encoding. The method has two primary applications. First, in AI music, it supports downstream tasks such as music generation, classification, style transfer, and improvisation. Second, in musicology, it provides a tool for analyzing compositional patterns and offers insights into the principle of minimal encoding across diverse musical styles and composers. |
| title | Discovering "Words" in Music: Unsupervised Learning of Compositional Sparse Code for Symbolic Music |
| topic | Sound Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.24603 |