From Sound to Setting: AI-Based Equalizer Parameter Prediction for Piano Tone Replication

• URL: http://arxiv.org/abs/2509.24404v1
• Date: Mon, 29 Sep 2025 07:50:28 GMT
• Title: From Sound to Setting: AI-Based Equalizer Parameter Prediction for Piano Tone Replication
• Abstract summary: This project presents an AI-based system for tone replication in music production, focusing on predicting EQ parameter settings directly from audio features.<n>Unlike traditional audio-to-audio methods, our approach outputs interpretable parameter values that musicians can further adjust in their workflow.<n>The system enables practical, flexible, and automated tone matching for music producers and lays the foundation for extensions to more complex audio effects.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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