Tristan Wu

AutoSynth serializes MIDI performance events, fixed synthesizer parameters, and variable-length modulation routes into a unified sequence and learns their dependencies with an audio-conditioned autoregressive model, trained first by supervised learning on large-scale automatically constructed audio-program pairs and then by group-relative policy optimization with a mixed reward, so that it predicts a synthesizer program directly from reference audio and converts text into a program via a pretrained audio generation model, achieving competitive results in both synthesizer inversion and text-driven generation.

AutoSynth serializes MIDI performance events, fixed synthesizer parameters, and variable-length modulation routes into a unified sequence and learns their dependencies with an audio-conditioned autoregressive model, trained first by supervised learning on large-scale automatically constructed audio-program pairs and then by group-relative policy optimization with a mixed reward, so that it predicts a synthesizer program directly from reference audio and converts text into a program via a pretrained audio generation model, achieving competitive results in both synthesizer inversion and text-driven generation.