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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2509.14053 |
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| _version_ | 1866910048850018304 |
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| author | Rosselló, Lluc Bono Jankowski, Robert Bersini, Hugues Boguñá, Marián Serrano, M. Ángeles |
| author_facet | Rosselló, Lluc Bono Jankowski, Robert Bersini, Hugues Boguñá, Marián Serrano, M. Ángeles |
| contents | Music is a structured and perceptually rich sequence of sounds in time, whose perception is shaped by the interplay of expectation and uncertainty about what comes next. Yet the uncertainty we infer from music depends on how the musical piece is encoded as an event sequence. In this work, we use network representations, in which event types are nodes and observed transitions are directed edges, to compare how different feature encodings shape the transition structure we recover and what this implies for both the descriptive uncertainty expectation under imperfect memory and noise. We systematically analyse eight encodings of piano music, from single-feature vocabularies to richer multi-feature combinations. These representational choices reorganize the state space and fundamentally reshape network topology, shifting how uncertainty is distributed across transitions. To connect these descriptive differences to perception, we adopt a perceptual-constraint model that captures imperfect access to transition statistics. Overall, compressed single-feature representations yield dense transition structures with higher entropy rates, corresponding to higher average uncertainty per step, yet low model error, indicating that the constrained estimate stays close to the corpus transitions. In contrast, richer multi-feature representations preserve finer distinctions but expand the state space, sharpen transition profiles, lower entropy rates, and increase model error. Finally, across representations, uncertainty concentrates in diffusion-central nodes while model error remains low there, suggesting an informational landscape in which predictable flow coexists with localized surprise. Overall, our results show that feature choice shapes not only the networks we reconstruct, but also whether their resulting uncertainty is a plausible proxy for the expectations listeners can realistically learn and use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14053 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Trade-offs between structural richness and communication efficiency in music network representations Rosselló, Lluc Bono Jankowski, Robert Bersini, Hugues Boguñá, Marián Serrano, M. Ángeles Physics and Society Sound Audio and Speech Processing Neurons and Cognition Music is a structured and perceptually rich sequence of sounds in time, whose perception is shaped by the interplay of expectation and uncertainty about what comes next. Yet the uncertainty we infer from music depends on how the musical piece is encoded as an event sequence. In this work, we use network representations, in which event types are nodes and observed transitions are directed edges, to compare how different feature encodings shape the transition structure we recover and what this implies for both the descriptive uncertainty expectation under imperfect memory and noise. We systematically analyse eight encodings of piano music, from single-feature vocabularies to richer multi-feature combinations. These representational choices reorganize the state space and fundamentally reshape network topology, shifting how uncertainty is distributed across transitions. To connect these descriptive differences to perception, we adopt a perceptual-constraint model that captures imperfect access to transition statistics. Overall, compressed single-feature representations yield dense transition structures with higher entropy rates, corresponding to higher average uncertainty per step, yet low model error, indicating that the constrained estimate stays close to the corpus transitions. In contrast, richer multi-feature representations preserve finer distinctions but expand the state space, sharpen transition profiles, lower entropy rates, and increase model error. Finally, across representations, uncertainty concentrates in diffusion-central nodes while model error remains low there, suggesting an informational landscape in which predictable flow coexists with localized surprise. Overall, our results show that feature choice shapes not only the networks we reconstruct, but also whether their resulting uncertainty is a plausible proxy for the expectations listeners can realistically learn and use. |
| title | Trade-offs between structural richness and communication efficiency in music network representations |
| topic | Physics and Society Sound Audio and Speech Processing Neurons and Cognition |
| url | https://arxiv.org/abs/2509.14053 |