be85a5cd71
Extract the interactive machine learning engine from MEMLNaut-NISPS firmware into a standalone, platform-agnostic C++20 header-only library. What is nisps-core? ------------------- NISPS (Neural Interactive Shaping of Parameter Spaces) core is a parameter mapping engine. It takes N input parameters (joystick, sensors, audio features) and maps them to M output parameters through an interactively-trained neural network. Use it to control: synthesizers, effects, lights, robots, game parameters, or anything that responds to continuous control data. Key Features ------------ - Header-only: No compilation needed, just include and use - Platform-agnostic: Pure C++20, works anywhere - Zero dependencies: Only standard library - Interactive learning: Train by demonstration - Lightweight: ~3,500 lines of optimized neural network code - Flexible: Map 1-100 inputs to 1-100 outputs Architecture ------------ Core components: - IML: High-level interactive ML interface - MLP: Multi-layer perceptron (feedforward neural network) - Dataset: Training data management with replay memory - Layer/Node: Neural network building blocks - Loss: MSE and categorical cross-entropy functions - Utils: Activation functions (sigmoid, ReLU, tanh, etc.) Transformations Applied ----------------------- ✅ Removed Arduino/RP2040 dependencies (Serial, SD, Pico SDK) ✅ Removed audio synthesis code (nisps-core is control-only) ✅ Added nisps namespace to all code ✅ Converted to header-only library with _impl.hpp pattern ✅ Updated to C++20 (required for std::span) ✅ Removed platform-specific serialization ✅ Replaced debug macros with no-op stubs ✅ Added comprehensive documentation and examples Files Added ----------- - nisps-core/README.md: Complete documentation and API reference - nisps-core/CHANGELOG.md: Version history and migration guide - nisps-core/include/nisps/*.hpp: 13 header files (~3,500 lines) - nisps-core/test/main.cpp: XOR test demonstrating basic usage - nisps-core/examples/simple_mapping.cpp: Interactive demo - nisps-core/CMakeLists.txt: Build system for tests Testing ------- ✅ Compiles with GCC 14.2 (C++20) ✅ All tests passing ✅ Successfully instantiates networks and runs inference Performance ----------- - Inference: 1-10 µs for small networks (2-10-10-4) - Training: 10-100 ms for 100 examples, 1000 iterations - Memory: ~1 KB per hidden neuron Migration from Embedded IMLInterface ------------------------------------ Old (embedded): IMLInterface iml(n_inputs, n_outputs); New (nisps-core): nisps::IML<float> iml(n_inputs, n_outputs); All method names remain the same, just add the namespace. Related ------- - Implements: NISPS_CORE_EXTRACTION_PLAN.md - Task graph: NISPS_CORE_TASKS.md - Origin: MEMLNaut-NISPS firmware - Docs: https://musicallyembodiedml.github.io/memlnaut/ Co-authored-by: Claude Code <claude@anthropic.com>
88 lines
4 KiB
Markdown
88 lines
4 KiB
Markdown
# CLAUDE.md
|
|
|
|
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
|
|
|
|
## Overview
|
|
|
|
MEMLNaut-NISPS (Neural Interactive Shaping of Parameter Spaces) is firmware for the MEMLNaut hardware platform - a custom embedded audio device built on Raspberry Pi Pico (RP2040). It implements interactive machine learning for real-time audio synthesis and processing, enabling users to shape sound parameters through reinforcement learning.
|
|
|
|
Project documentation: https://musicallyembodiedml.github.io/memlnaut/approaches/nisps
|
|
|
|
## NISPS Core Library
|
|
|
|
The `nisps-core/` directory contains a platform-agnostic C++20 extraction of the interactive ML engine. This header-only library can be used in any C++ project for neural network-based parameter mapping.
|
|
|
|
**Key differences from firmware**:
|
|
- ✅ Platform-agnostic (no Arduino/RP2040 dependencies)
|
|
- ✅ Header-only (just include and use)
|
|
- ✅ C++20 (uses std::span)
|
|
- ✅ Namespaced (`nisps::`)
|
|
- ❌ No audio synthesis (use it to *control* your synth)
|
|
- ❌ No hardware drivers
|
|
|
|
**Use case**: Control synthesizers, effects, lights, game parameters, or any system that responds to continuous parameters.
|
|
|
|
See `nisps-core/README.md` for complete documentation and examples.
|
|
|
|
## Build System
|
|
|
|
This is an Arduino project targeting Raspberry Pi Pico. Build and upload using Arduino IDE or arduino-cli with the earlephilhower/pico board package.
|
|
|
|
```bash
|
|
# Initialize submodules (required for memllib and memlp)
|
|
git submodule update --init --recursive
|
|
|
|
# Build (adjust port as needed)
|
|
arduino-cli compile --fqbn rp2040:rp2040:rpipico -b 115200 MEMLNaut-NISPS.ino
|
|
arduino-cli upload --fqbn rp2040:rp2040:rpipico -p /dev/ttyACM0 MEMLNaut-NISPS.ino
|
|
```
|
|
|
|
## Architecture
|
|
|
|
### Dual-Core Design
|
|
|
|
The RP2040's dual cores are used for separation of concerns:
|
|
- **Core 0**: UI loop, ML inference, hardware interface polling (5ms period)
|
|
- **Core 1**: Real-time audio processing, parameter updates, MIDI polling
|
|
|
|
Inter-core synchronization uses memory barriers (`MEMORY_BARRIER()`, `WRITE_VOLATILE()`, `READ_VOLATILE()`) and RP2040 queues (`queue_t`).
|
|
|
|
### Mode System
|
|
|
|
The active mode is selected at compile-time via `#define MEMLNAUT_MODE_TYPE` in `MEMLNaut-NISPS.ino`. Modes implement the `MEMLNautMode` concept (see `modes/MEMLNautMode.hpp`):
|
|
|
|
| Mode | Purpose |
|
|
|------|---------|
|
|
| `MEMLNautModeChannelStrip` | Audio channel strip (EQ, compression, gain staging) |
|
|
| `MEMLNautModePAFSynth` | PAF (Phase Aligned Formant) synthesis with MIDI |
|
|
| `MEMLNautModeXIASRI` | Audio-reactive mode using machine listening analysis |
|
|
| `MEMLNautModeSoundAnalysisMIDI` | Sound analysis with MIDI output |
|
|
|
|
### Voice Spaces
|
|
|
|
Voice spaces map ML output parameters to audio engine parameters. They are defined as lambda functions that translate a normalized parameter array into synthesizer/processor settings. See `voicespaces/` for examples:
|
|
- PAF synth presets: `VoiceSpace1.hpp`, `VoiceSpaceQuadDetune.hpp`, etc.
|
|
- Channel strip presets: `voicespaces/ChannelStrip/basic.hpp` (Neve, SSL emulations)
|
|
|
|
### Key Components
|
|
|
|
- **IMLInterface** (`IMLInterface.hpp`): Interactive ML interface using an MLP for inference/training
|
|
- **InterfaceRL**: Reinforcement learning interface from memllib that handles joystick input and learning
|
|
- **AudioAppBase**: Template base class for audio applications
|
|
- **XiasriAnalysis**: Real-time audio feature extraction (pitch, aperiodicity, energy, brightness)
|
|
|
|
### Submodules (in `src/`)
|
|
|
|
- **memllib**: Hardware abstraction, audio drivers, synth components, RL interfaces
|
|
- **memlp**: MLP (Multi-Layer Perceptron) implementation for embedded ML
|
|
- **daisysp**: DSP library (filters, drums, effects, synthesis)
|
|
|
|
## Memory Sections
|
|
|
|
The codebase uses RP2040-specific memory placement:
|
|
- `AUDIO_MEM` / `AUDIO_FUNC`: Place audio-critical code/data in SRAM
|
|
- `APP_SRAM` / `__not_in_flash("app")`: Keep frequently-accessed data out of flash
|
|
|
|
## Audio Parameters
|
|
|
|
Sample rate is defined in `AudioDriver::GetSampleRate()`. The audio callback `audio_block_callback` runs on Core 1 and processes stereo audio (`stereosample_t`).
|