No description
Find a file
2026-02-11 15:56:24 +00:00
.vscode memlnautmodes, channelstrip 2025-11-19 15:17:37 +00:00
data view order 2025-11-04 17:35:24 +00:00
modes bypass toggle colours 2025-12-10 17:35:48 +00:00
nisps-core fix: audit and fix nisps-core extraction issues 2026-02-08 18:01:48 +01:00
playground playground: add loss history tracking and diagnostics panel 2026-02-11 15:56:24 +00:00
src bypass toggle colours 2025-12-10 17:35:48 +00:00
voicespaces bypass toggle colours 2025-12-10 17:35:48 +00:00
.gitignore Revert "Censored MacOS files" 2025-11-12 10:25:30 +00:00
.gitmodules Changed gitmodules to https 2025-04-29 10:21:43 +01:00
ChannelStripAudioApp.hpp stereo channel strip 2026-01-13 08:23:01 +00:00
CLAUDE.md playground: add orbiting repulsor field controls 2026-02-11 15:50:06 +00:00
IMLInterface.hpp speed optim, no clicking 2025-05-07 23:58:06 +01:00
LICENSE Initial commit 2025-04-10 14:17:18 +01:00
MEMLNaut-NISPS.ino xiasri latest, and channel strip changes for 4D input 2025-12-10 16:40:00 +00:00
NISPS_CORE_EXTRACTION_PLAN.md fix: audit and fix nisps-core extraction issues 2026-02-08 18:01:48 +01:00
NISPS_CORE_TASKS.md add nisps-core extraction task graph 2026-02-08 17:03:40 +01:00
PAFSynthAudioApp.hpp memlnautmodes, channelstrip 2025-11-19 15:17:37 +00:00
README.md feat: add web-based interactive playground for NISPS 2026-02-11 13:17:17 +01:00
ThruAudioApp.hpp refactoring interface into modes 2025-11-24 11:29:33 +00:00
XiasriAnalysis.cpp xiasri latest, and channel strip changes for 4D input 2025-12-10 16:40:00 +00:00
XiasriAnalysis.hpp refactoring interface into modes 2025-11-24 11:29:33 +00:00
XIASRIAudioApp.hpp xiasri latest, and channel strip changes for 4D input 2025-12-10 16:40:00 +00:00

Neural Interactive Shaping of Parameter Spaces

https://musicallyembodiedml.github.io/memlnaut/approaches/nisps

Web Playground

Try NISPS in your browser — no hardware required:

cd playground
python3 -m http.server
# Open http://localhost:8000

Train a neural network to map joystick positions to generative visuals through interactive machine learning. Two learning modes: direct example mapping and reinforcement learning with thumbs up/down feedback.