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DDSP: Differentiable Digital Signal Processing
Affiliation: Google AI
Role: Co-author 

Differentiable Digital Signal Processing (DDSP) is an open-source library combining the power of modern neural networks with classical signal processing. By formulating common Digital Sgnal Processing (DSP) elements such as filters and oscillators as differentiable units within Tensorflow, we can build this prior knowledge into machine learning models and enhance their capabilities. The result are ML models which can render high quality audio with lower training data requirements, lighter hardware requirements and faster inference speed. 


In our ICLR 2020 paper we show that using differentiable oscillators, filters, and reverberation from the DDSP library enables us to train high-quality audio synthesis models with less data and fewer parameters, as they do not need to learn to generate audio from scratch. We demonstrated abilities such as training a high quality timbre transfer model with about 10 minutes of training data and other signal processing tasks like blind dereverberation. 

The key idea is to use simple interpretable DSP elements to create complex realistic signals by using a machine learning model to precisely control their many parameters. If you are new to this, you can think of it like an ML model which can either A.) render every pixel of a video game image directly or B.) control a renderer like Unreal engine to create the image.

On one end of the spectrum are completely “deep neural networks”. These systems are often black boxes. They can adapt to many different datasets but are not interpretable. On the other end is DSP or Digital Signal Processing, (without the extra “differentiable” D). This is an area of Electrical Engineering which forms the backbone of modern society telecommunications, medical imaging and file compression.


DDSP kickstarted an entire subfield!


DDSP forms the backbone of the following products: