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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.24404 |
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| _version_ | 1866912614827687936 |
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| author | Yu, Song-Ze |
| author_facet | Yu, Song-Ze |
| contents | This project presents an AI-based system for tone replication in music production, focusing on predicting EQ parameter settings directly from audio features. Unlike traditional audio-to-audio methods, our approach outputs interpretable parameter values (e.g., EQ band gains) that musicians can further adjust in their workflow. Using a dataset of piano recordings with systematically varied EQ settings, we evaluate both regression and neural network models. The neural network achieves a mean squared error of 0.0216 on multi-band tasks. The system enables practical, flexible, and automated tone matching for music producers and lays the foundation for extensions to more complex audio effects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24404 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Sound to Setting: AI-Based Equalizer Parameter Prediction for Piano Tone Replication Yu, Song-Ze Sound Machine Learning Audio and Speech Processing This project presents an AI-based system for tone replication in music production, focusing on predicting EQ parameter settings directly from audio features. Unlike traditional audio-to-audio methods, our approach outputs interpretable parameter values (e.g., EQ band gains) that musicians can further adjust in their workflow. Using a dataset of piano recordings with systematically varied EQ settings, we evaluate both regression and neural network models. The neural network achieves a mean squared error of 0.0216 on multi-band tasks. The system enables practical, flexible, and automated tone matching for music producers and lays the foundation for extensions to more complex audio effects. |
| title | From Sound to Setting: AI-Based Equalizer Parameter Prediction for Piano Tone Replication |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.24404 |