Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.09846 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910207557238784 |
|---|---|
| author | Liu, Yakun Luan, Hai Liu, Dong Jin, Zhiyu |
| author_facet | Liu, Yakun Luan, Hai Liu, Dong Jin, Zhiyu |
| contents | In new media art creation, the mapping between vision and hearing is often subjective. As a classic carrier of sound visualization, Chladni patterns have great potential in building audio-visual mapping mechanisms. However, existing tools face pain points: high technical barriers for simulation, offline computing failing real-time interaction, and uncontrollable mapping rules in general sonification tools. To address these, this paper proposes ChladniSonify, a real-time visual-acoustic mapping method for Chladni patterns. Based on Kirchhoff-Love plate theory, we build a paired dataset via numerical programming and calibrate it using ANSYS finite element simulation. Focusing on the slender nodal lines of Chladni patterns, we adopt a lightweight CNN with CBAM to achieve high-precision, low-latency pattern classification. Finally, we build an end-to-end system in Python and Max/MSP, mapping recognized patterns to corresponding sine wave frequencies. Results show the system has excellent usability: the classification module achieves 99.33% accuracy on the test set with 7.03 ms inference latency; the mapped frequency matches the theoretical value with zero deviation; the average end-to-end latency is under 50 ms, meeting real-time interactive needs. This work provides a reproducible engineering prototype for Chladni audio-visual art creation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_09846 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ChladniSonify: A Visual-Acoustic Mapping Method for Chladni Patterns in New Media Art Creation Liu, Yakun Luan, Hai Liu, Dong Jin, Zhiyu Sound Artificial Intelligence In new media art creation, the mapping between vision and hearing is often subjective. As a classic carrier of sound visualization, Chladni patterns have great potential in building audio-visual mapping mechanisms. However, existing tools face pain points: high technical barriers for simulation, offline computing failing real-time interaction, and uncontrollable mapping rules in general sonification tools. To address these, this paper proposes ChladniSonify, a real-time visual-acoustic mapping method for Chladni patterns. Based on Kirchhoff-Love plate theory, we build a paired dataset via numerical programming and calibrate it using ANSYS finite element simulation. Focusing on the slender nodal lines of Chladni patterns, we adopt a lightweight CNN with CBAM to achieve high-precision, low-latency pattern classification. Finally, we build an end-to-end system in Python and Max/MSP, mapping recognized patterns to corresponding sine wave frequencies. Results show the system has excellent usability: the classification module achieves 99.33% accuracy on the test set with 7.03 ms inference latency; the mapped frequency matches the theoretical value with zero deviation; the average end-to-end latency is under 50 ms, meeting real-time interactive needs. This work provides a reproducible engineering prototype for Chladni audio-visual art creation. |
| title | ChladniSonify: A Visual-Acoustic Mapping Method for Chladni Patterns in New Media Art Creation |
| topic | Sound Artificial Intelligence |
| url | https://arxiv.org/abs/2605.09846 |