DDSP: Differentiable Digital Signal Processing
Today, we’re pleased to introduce the Differentiable Digital Signal Processing (DDSP) library. DDSP lets you combine the interpretable structure of classical...
DDSP: Differentiable Digital Signal Processing Jan 15, 2020 Jesse Engel jesseengel jesseengel Hanoi Hantrakul lamtharnhantrakul hanoihantrakul Chenjie Gu gcj calbeargu Adam Roberts adarob ada_rob Today, we’re pleased to introduce the Differentiable Digital Signal Processing (DDSP) library. DDSP lets you combine the interpretable structure of classical DSP elements (such as filters, oscillators, reverberation, etc.) with the expressivity of deep learning. GitHub Code 🎵 Audio Examples Colab Demo Colab Tutorials 📝 ICLR Paper Neural networks (such as WaveNet or GANSynth ) are often black boxes.
saved by
related reading
- Circles Sines and Signals - Introductionjackschaedler.github.io
- DSP Workshopdsp-workshop.nl
- GANSynth: Making music with GANsmagenta.tensorflow.org
- Generating music in the waveform domain – Sander Dielemansander.ai
- Diffusion is spectral autoregression – Sander Dielemansander.ai
- DSP Firstdspfirst.gatech.edu
- Deepa Kundur: Real-Time Digital Signal Processingcomm.utoronto.ca
- An Intuitive Explanation of Sparse Autoencoders for LLM Interpretability | Adam Karvonenadamkarvonen.github.io
- Diffusion models from scratchchenyang.co
- Understanding Convolutions on Graphsdistill.pub
- Audio Deep Learning Made Simple: Sound Classification, step-by-step | Towards Data Sciencetowardsdatascience.com
- Paint With Music - Google Arts & Cultureartsandculture.google.com