DDSP: Differentiable Digital Signal Processing · HackerPapers discussions

DDSP: Differentiable Digital Signal Processing — mentioned by vvolhejn

Tue, 21 Oct 2025 19:50:58 GMT

Author here, thanks for the kind words! I think such a physics-based codec is unlikely to happen: in general, machine learning is always moving from handcrafted domain-specific assumptions to leaving as much as possible to the model. The more assumptions you bake in, the smaller the space of sounds you can model, so the quality is capped. Basically, modern ML is just about putting the right data into transformers. That being said, having a more constrained model can also lead to some really cool stuff. The DDSP paper learns how to control a synthesizer to mimic instruments…

Paper and discussion history. A mention is not an endorsement.

DDSP: Differentiable Digital Signal Processing — HN submission

Fri, 25 Feb 2022 13:59:59 GMT

Show HN: Cloning a musical instrument from 16 seconds of audio

In 2020, Magenta released DDSP [1], a machine learning algorithm / python library which made it possible to generate good sounding instrument synthesizers from about 6-10 minutes of data. While working with DDSP for a project, we realised how it was actually quite hard to find 6-10 minute of clean recordings of monophonic instruments. In this project, we have combined the DDSP architecture with a domain adaptation technique from speech synthesis [2]. This domain adaptation technique works by pre-training our model on many different recordings from the Solos dataset [3] first and then…

185 points; 72 discussion replies on HN. Paper and source history. A submission is not an endorsement.

DDSP: Differentiable Digital Signal Processing — mentioned by ssalazar

Wed, 15 Jan 2020 23:56:09 GMT

arXiv link for more context: https://arxiv.org/abs/2001.04643 and blog posting with audio examples: https://magenta.tensorflow.org/ddsp

Paper and discussion history. A mention is not an endorsement.