Research, findings, and thoughts from TONARI LABS. Currently exploring audio-to-synth-parameter estimation, ML for sound design, and whatever else catches my ear.
The concrete step-by-step plan for building the retrieval-first MVP — target synth, renderer, dataset generation, embedding pipeline, and the four experiments that determine if it works.
Existing synth parameter datasets, why we chose Surge XT for data legality, and the synthetic generation strategy.
Most popular audio embedding models are the wrong tool for synth patch retrieval. Here's the model-by-model breakdown and our two-stage architecture.
An honest assessment of Google Magenta's DDSP for subtractive synth parameter inference — what it can do, what breaks, and how we'll use it instead.
Supervised regression, DDSP, retrieval-based matching, reinforcement learning, and generative models — what works, what doesn't, and what we're using.
Why turning audio into synth parameters is mathematically hard, what tools exist today, and what open-source research is available.
What it was like to build an ML audio tool using Claude Code, the mistakes that come from moving fast without understanding deeply, and why we're doing research before code this time.
A walkthrough of the four-layer architecture behind the original Patch Pilot — from input handling to synth parameter output — and the wild December sprint that brought it to life.
Why we shelved the v1, what we learned, and how we're approaching the research reboot for an audio-to-synth-parameter tool.