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room-acoustic-optimizer

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Room Acoustic Optimizer with Machine Learning

A hybrid acoustic simulation and ML-powered optimization system for room acoustic design.

Architecture

  • FEM (Finite Element Method): Low frequencies (20-300 Hz)
  • ISM (Image Source Method): Mid frequencies (300-1000 Hz)
  • Raytracing: High frequencies (>1000 Hz)
  • ML Optimization: Bayesian (Optuna) + Genetic (NSGA-II)

Based on

  • NERO/ROCKS/TORA concepts from Ready Acoustics
  • DeepONet for Acoustics: arXiv:2308.05141
  • HiFi-HARP Dataset: arXiv:2510.21257

Quick Start 

from room_acoustic_optimizer import HybridSimulator, RoomGeometry

# Create room and simulate
room = create_simple_room(5.0, 4.0, 3.0)
simulator = HybridSimulator()
result = simulator.simulate_full_band(room, source_pos, receiver_pos)

Demo

Live demo: https://huggingface.co/spaces/Neknufelet/room-acoustic-demo

Repo Structure

  • room_acoustic_optimizer/simulator.py - Hybrid FEM+ISM+RT simulator
  • room_acoustic_optimizer/surrogate.py - DeepONet surrogate model
  • room_acoustic_optimizer/optimizer.py - Bayesian + Genetic optimizers
  • room_acoustic_optimizer/metrics.py - Acoustic metrics (ISO 3382)
  • demo_optimizer.py - Full demo script
  • train_surrogate.py - Training script for DeepONet
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