{"ID":2863385,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.24404","arxiv_id":"2509.24404","title":"From Sound to Setting: AI-Based Equalizer Parameter Prediction for Piano Tone Replication","abstract":"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.","short_abstract":"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 wo...","url_abs":"https://arxiv.org/abs/2509.24404","url_pdf":"https://arxiv.org/pdf/2509.24404v1","authors":"[\"Song-Ze Yu\"]","published":"2025-09-29T07:50:28Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.LG\",\"eess.AS\"]","methods":"[]","has_code":false}