Python project to take sound as input and output as RGB + Brightness values suitable for DMX

Last update: Nov 17, 2021

Related tags

Deep Learning sound-to-light

Overview

sound-to-light

Python project to take sound as input and output as RGB + Brightness values suitable for DMX

Current goals:

Get one pixel working:
- Vary brightness according to the amplitude of the sound input
- Vary RGB value according to the frequency of the sound input
  - Colors mapped similar as https://github.com/CymaSpace/Cymatic-Lighting/blob/master/Cymatic_Lighting/Cymatic_Lighting.ino

Owner

Bobby Cox

GitHub Repository

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Python project to take sound as input and output as RGB + Brightness values suitable for DMX

Related tags

Overview

sound-to-light

Owner

Bobby Cox

HODEmu, is both an executable and a python library that is based on Ragagnin 2021 in prep.

Code for Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights

Multi-Anchor Active Domain Adaptation for Semantic Segmentation (ICCV 2021 Oral)

K Closest Points and Maximum Clique Pruning for Efficient and Effective 3D Laser Scan Matching (To appear in RA-L 2022)

Data-Uncertainty Guided Multi-Phase Learning for Semi-supervised Object Detection

Airborne Optical Sectioning (AOS) is a wide synthetic-aperture imaging technique

Code repository for the paper "Tracking People with 3D Representations"

Official implementation of "Accelerating Reinforcement Learning with Learned Skill Priors", Pertsch et al., CoRL 2020

Streamlit Tutorial (ex: stock price dashboard, cartoon-stylegan, vqgan-clip, stylemixing, styleclip, sefa)

ReLoss - Official implementation for paper "Relational Surrogate Loss Learning" ICLR 2022

CVPR2022 (Oral) - Rethinking Semantic Segmentation: A Prototype View

CS50's Introduction to Artificial Intelligence Test Scripts

Remote sensing change detection tool based on PaddlePaddle

PyTorch implementation of Glow

Torchreid: Deep learning person re-identification in PyTorch.

Code accompanying the paper on "An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers" published at NeurIPS, 2021

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