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.