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arXiv · 2610.00642

Interpretable Destination-Aware synthesizer Modulation Recovery

David Liu·Giulio Cengarle·David Cooper·Mark Vinton·Haici Yang
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Abstract

Synthesizer programming is a challenging task, particularly in configuring modulation: the time-varying control of parameters such as pitch, filter cutoff, and oscillator level by modulators such as Low-Frequency Oscillators (LFOs). Prior work has shown that modulation can be reconstructed from the clean audio through curve matching, yet the recovered parameters are often untransferable to modern synthesizers. We present an interpretable destination-aware modulation and waveform recovery pipeline that mirrors how musicians often recreate sounds on a synthesizer. First, the system identifies the modulated destinations; then it recovers the LFO shape associated with each destination, and estimates the oscillator waveform to match the source timbre. The training is supported by a differentiable synthesizer that incorporates a noise oscillator, and more modulation options than previous work. We carefully train the models for optimal perceptual quality, with extensive exploration of perceptual losses and adversarial training. Through objective and subjective evaluation, we show that predicting the destinations correctly is essential, our model excels in the modulation focused inverse synthesis tasks, and our Gammatone loss and CQT discriminator significantly outperform the traditionally-used MSS loss in improving perceptual quality. We provide audio samples from the subjective test.

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BibTeXRIS

David Liu, Giulio Cengarle, David Cooper, Mark Vinton, Haici Yang. 2026-09-30. Interpretable Destination-Aware synthesizer Modulation Recovery. https://arxiv.org/abs/2610.00642

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