InverSynth: Deep Estimation of Synthesizer Parameter Configurations from Audio Signals

TL;DR

InverSynth is a deep learning approach that automatically estimates synthesizer parameters from audio signals, simplifying sound design by reducing manual tuning efforts.

Contribution

It introduces a neural network-based method capable of inferring synthesizer settings directly from spectrograms or raw audio, demonstrating improved accuracy over baselines.

Findings

InverSynth outperforms baseline methods in parameter prediction accuracy.

Deeper networks significantly improve estimation performance.

Effective on a subtractive synthesizer with multiple modules.

Abstract

Sound synthesis is a complex field that requires domain expertise. Manual tuning of synthesizer parameters to match a specific sound can be an exhaustive task, even for experienced sound engineers. In this paper, we introduce InverSynth - an automatic method for synthesizer parameters tuning to match a given input sound. InverSynth is based on strided convolutional neural networks and is capable of inferring the synthesizer parameters configuration from the input spectrogram and even from the raw audio. The effectiveness InverSynth is demonstrated on a subtractive synthesizer with four frequency modulated oscillators, envelope generator and a gater effect. We present extensive quantitative and qualitative results that showcase the superiority InverSynth over several baselines. Furthermore, we show that the network depth is an important factor that contributes to the prediction accuracy.