🌍🔊 Planetary IR Studio

Physics-based acoustic simulation from LIDAR data. Generate impulse responses of caves, lava tubes, and extraterrestrial environments.

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What is this?

Planetary IR Studio converts NASA LIDAR scans of caves and lava tubes into acoustic Impulse Responses (IRs) using physics-based wave simulation (FDTD) and per-triangle ray tracing. It is the only software that simulates how spaces sound on Mars, Titan, and the Moon. The output IRs can be loaded into any DAW with a convolution reverb plugin — record a vocal in your studio, and hear it inside a Martian lava tube.

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🎧 Listen

Download the 5-second IR previews and load them into your favorite convolution reverb:

Space	Planet	RT60	C80	STI	Bass Ratio	IR Preview
Indian Tunnel Lava Tube	🌍 Earth	1.86s	-1.5 dB	0.595	0.94	WAV
Indian Tunnel Lava Tube	🔴 Mars	2.15s	1.2 dB	0.625	0.65	WAV
Indian Tunnel Lava Tube	🟠 Titan	4.67s	8.4 dB	0.821	2.41	WAV
Skull Cave	🌍 Earth	2.12s	24.3 dB	0.863	2.96	WAV
Skull Cave	🔴 Mars	3.35s	21.6 dB	0.841	2.96	WAV

Indian Tunnel is a real lava tube in Craters of the Moon National Monument, Idaho. Scanned by NASA with 11.6 million LIDAR points. Skull Cave is a limestone cave in Lava Beds National Monument, California.

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🔬 How It Works

An 8-step physics pipeline transforms raw LIDAR point clouds into publication-quality impulse responses:

1. LIDAR Input — Reads .f32 point clouds from NASA CAVES, or your own iPhone LIDAR scan (Polycam, Scaniverse)
2. 3D Mesh — Poisson surface reconstruction with convex hull closure to prevent ray escape
3. Material Classification — K-Means clustering on NIR reflectance maps points to 9 cave materials, each with frequency-dependent absorption coefficients
4. FDTD Wave Simulation — FCC 4th-order lattice (Hamilton & Webb 2017) on Metal GPU. Captures diffraction, modal resonance, and wave phenomena that ray tracing cannot
5. Per-Triangle Ray Tracing — Intel Embree-accelerated. Each bounce uses the exact material of the triangle hit, with frequency-dependent absorption and scattering
6. FDN Synthesis — Feedback Delay Network with analytically calibrated gains. Implemented in Rust (391x faster than Python)
7. Crossover Blend — Combines wave simulation (low frequencies, physical accuracy) with ray tracing (high frequencies, computational efficiency)
8. Analysis & Export — ISO 3382 metrics, 5-page PDF report, WAV files, modal analysis, decay curves

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🪐 Planetary Acoustics

No other software in the world simulates extraterrestrial acoustics. Planetary IR Studio models the real physics of sound propagation on other worlds:

Planet	Speed of Sound	Atmosphere	Effect on Acoustics
🌍 Earth	343 m/s	N₂/O₂, 1 atm	Reference standard
🔴 Mars	240 → 250 m/s	CO₂, 600 Pa	High frequencies die after ~50m. Bi-modal dispersion above 2 kHz (verified against Perseverance data)
🟠 Titan	194 m/s	N₂/CH₄, 1.5 atm	Reverb tails 1.77x longer than Earth for the same geometry. Dense atmosphere (5.3 kg/m³)
🌑 Moon	0 m/s	Vacuum	No air propagation — structural vibration through rock only

Mars's CO₂ atmosphere absorbs high frequencies aggressively. The same cave sounds muffled and dark on Mars — like listening through a thick blanket.

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📊 Acoustic Analysis

Every simulation produces a 5-page PDF report with full ISO 3382 acoustic metrics:

• RT60 (T20/T30) — Reverberation time via Schroeder backward integration
• EDT — Early Decay Time
• C50 / C80 — Clarity (speech / music)
• D50 — Definition
• Ts — Centre time
• STI — Speech Transmission Index
• Bass Ratio / Brilliance — Spectral balance
• Modal Analysis — Room modes and resonant frequencies
• Flutter Echo Detection — Periodic reflection patterns
• Early Reflections — Isolated first 50ms

📄 Example PDF Report

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🚀 Quick Start

# Clone
git clone https://github.com/your-username/planetary-ir-studio.git
cd planetary-ir-studio

# Install dependencies
python3.11 -m pip install -r requirements.txt

# Optional: Rust FDN acceleration (391x faster synthesis)
cd rust_fdn && maturin develop --release && cd ..

# Generate your first IR (uses NASA CAVES Indian Tunnel data)
python3.11 lidar_to_ir_3layer.py IndianTunnel_cave/Full/IndianTunnel_full_10x.f32 \
    --preset QUICK --voxel-size 0.3

# Try Mars!
python3.11 lidar_to_ir_3layer.py IndianTunnel_cave/Full/IndianTunnel_full_10x.f32 \
    --preset QUICK --planet MARS --voxel-size 0.3

# Try Titan!
python3.11 lidar_to_ir_3layer.py IndianTunnel_cave/Full/IndianTunnel_full_10x.f32 \
    --preset QUICK --planet TITAN --voxel-size 0.3

Output goes to output/ — you'll find WAV files, a PDF report, metrics JSON, and decay curves.

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📱 Use Your Own Space

Scan any room, cave, or tunnel with an iPhone Pro (LIDAR-equipped) using Polycam or Scaniverse:

# Convert your .ply scan to .f32 format
python3.11 convert_ply_to_f32.py your_scan.ply

# Generate the IR
python3.11 lidar_to_ir_3layer.py your_scan.f32 --preset QUICK

# What does your bathroom sound like on Titan?
python3.11 lidar_to_ir_3layer.py your_scan.f32 --preset QUICK --planet TITAN

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🏗️ Architecture

Core Pipeline

Module	Description
lidar_to_ir_3layer.py	Main 8-step pipeline with CLI, presets, planet support
chunked_reader.py	Adaptive streaming reader with fast vectorized path
lidar_to_ir.py	Point cloud processing and Poisson mesh reconstruction
material_classifier.py	K-Means NIR/RGB → 9 cave materials with absorption DB
fdtd_metal.py	Metal GPU FDTD backend (Apple Silicon)
fdtd_cpu.py	CPU fallback FDTD with FCC4 lattice
fdtd_multiband.py	Multi-band FDTD orchestrator (auto-selects GPU/CPU)
ray_tracer_multimaterial.py	Per-triangle Embree ray tracer with Russian Roulette
hybrid_ir_engine.py	FDN synthesis + FDTD/ray crossover blend
fdn_optimizer.py	Analytical FDN calibration with per-band correction
acoustic_metrics.py	ISO 3382 metrics via Schroeder integration
acoustic_phenomena.py	Flutter echo, modal analysis, diffusion detection
report_generator.py	5-page PDF report with matplotlib charts

Rust Acceleration

Module	Speedup	Description
rust_fdn/	391x	FDN with Householder matrix, prime delays, 1-pole lowpass
rust_raytracer/	Fallback	BVH + Moller-Trumbore (Open3D Embree preferred)

API & Visualization

Module	Description
api_server.py	FastAPI REST API (9 endpoints, job queue)
planetary_ir_sdk/	Python SDK for programmatic access
vis_server.py	WebSocket server for real-time 3D visualization
vis_client.html	Three.js viewer with material colors and FDTD heatmaps

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⚡ Performance

Benchmarked on MacBook Pro M1, 16GB unified memory:

Dataset	Points	Pipeline Time	FDTD Backend	RT60
Indian Tunnel 10x	11.6M	11.2s	Metal FCC4	1.86s
Indian Tunnel full	116.2M	40.9s	Metal FCC4	2.51s
Skull Cave	500K	6.6s	Metal FCC4	2.09s

Optimizations

Technique	Speedup	Reference
FCC4 lattice (vs Cartesian FDTD)	18x	Hamilton & Webb 2017
Russian Roulette ray termination	7.8x	Energy-conserving, r=0.976 correlation
Rust FDN (vs Python)	391x	PyO3 + ndarray
Metal GPU (vs CPU FDTD)	1.24x	Apple Silicon compute shaders
Analytical FDN calibration	∞	0ms vs 30s iterative
Vectorized voxel reader	1.33x	NumPy sort + reduceat

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🏆 Competitive Advantage

Feature	Planetary IR Studio	Treble Tech	ODEON v19	Steam Audio	Altiverb v8
LIDAR point cloud input	✅	❌	❌	❌	❌
Wave simulation (FDTD)	✅	✅	❌	Partial	❌
Planetary acoustics	✅	❌	❌	❌	❌
Auto material classification	✅	❌	❌	❌	❌
iPhone LIDAR support	✅	❌	❌	❌	❌
Consumer GPU (Metal)	✅	❌ (cloud)	N/A	❌	N/A
Open source	✅	❌	❌	✅	❌
ISO 3382 metrics	✅	✅	✅	❌	❌
IR export (.wav)	✅	✅	✅	❌	N/A

Nobody else converts LIDAR scans directly to acoustic impulse responses. Nobody else simulates planetary acoustics.

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🔬 Research Foundation

Built on peer-reviewed acoustic simulation research:

• Hamilton & Webb (2017) — FCC 4th-order FDTD lattice for room acoustics
• Schroeder (1965) — Backward integration for reverberation time measurement
• Eyring (1930) — Absorption-weighted reverberation model for absorptive spaces
• ISO 3382-1:2009 — Measurement of room acoustic parameters
• Chide et al. (2021) — Mars atmospheric acoustics from Perseverance
• NASA CAVES — LIDAR datasets of volcanic caves

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📄 License

MIT License — use freely for commercial, academic, or creative projects.

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🤝 Contributing

PRs welcome! See docs/architecture.md for the module design and docs/adrs/ for architecture decision records.

# Run the smoke tests
python3.11 tests/test_pipeline_smoke.py

# Profile the pipeline
python3.11 -m cProfile -s cumtime lidar_to_ir_3layer.py data/skull_test.f32 --preset QUICK --no-vis

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Built with Claude Code across 8 autonomous R&D sessions.