From Sound to Setting: AI-Based Equalizer Parameter Prediction for Piano Tone Replication
This enables automated tone matching for music producers, though it is incremental as it builds on existing AI methods for audio processing.
The paper tackles the problem of replicating piano tones in music production by predicting EQ parameter settings from audio features, achieving a mean squared error of 0.0216 with a neural network model.
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.