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 generates interpretable parameter values--such as 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. Results show that our neural network model achieves highly accurate parameter predictions, with a mean squared error of 0.0216 on multi-band tasks. The proposed system enables practical, flexible, and automated tone matching for music producers, laying the foundation for future extensions to more complex audio effects.