Physics-Informed Neural Engine Sound Synthesis
This repository contains the implementation and trained models for the Pulse-Train-Resonator (PTR), a deep learning architecture for engine sound synthesis that directly models combustion pressure pulses and exhaust propagation through differentiable synthesis components.
Engine sounds originate from sequential combustion pressure pulses rather than sustained harmonic oscillations. While existing neural synthesis methods model the resulting spectral characteristics, PTR directly models the underlying pulse structure: parameterized pressure waves aligned to engine firing patterns, propagated through differentiable Karplus-Strong resonators simulating exhaust acoustics.
The model is trained on the Procedural Engine Sounds Dataset and demonstrates consistent generalization across diverse engine configurations, achieving improved audio reconstruction compared to harmonic-plus-noise baselines using identical network architecture.
Physics-informed pulse synthesis incorporating thermodynamic pitch modulation and valve-dynamics envelopesDifferentiable Karplus-Strong resonators for exhaust system acoustic modelingFiring-order sequencing with per-cylinder parameterizationGeneralization across engine types despite fixed architectural priors
ptr-model/
├── checkpoints/ # Pre-trained model weights
│ └── 2025-08-31_models_and_weights.zip
├── configs/ # Base configuration files
├── scripts/ # Training and inference scripts
│ ├── train.py # Training pipeline with CLI
│ └── inference.py # Model inference with CLI
├── src/ # Source code
│ ├── audio/ # Audio processing utilities
│ ├── data/ # Data and processing
│ ├── models/ # Model implementations
│ │ ├── hpn_model.py # Harmonic-Plus-Noise baseline
│ │ ├── hpn_synth.py # HPN synthesis modules
│ │ ├── ptr_model.py # Pulse-Train-Resonator model
│ │ ├── ptr_synth.py # PTR synthesis modules
│ │ └── model.py # Base model architecture
│ ├── training/ # Training utilities
│ └── utils/ # General utilities
├── pyproject.toml # Python dependencies and metadata
├── README.md # This file
└── LICENSE # License file
- Clone the repository:
git clone https://github.com/rdoerfler/ptr-model.git
cd ptr-model
- Install dependencies:
pip install .
- Extract pre-trained models:
cd checkpoints
unzip 2025-08-31_models_and_weights.zip
Download the Procedural Engine Sounds Dataset and put it next to this repository:
├── ptr-model/ # This repository
├── dataset/ # Procedural Engines Dataset
│ ├── A_full_set # Data subsets
│ ├── B_full_set
│ ├── C_full_set
│ └── ...
Train the HPN variant:
python scripts/train.py --model_type hpn --dataset C_full_set
Train the PTR variant:
python scripts/train.py --model_type ptr --dataset C_full_set
Customize model architecture:
python scripts/train.py --model_type ptr --num_harmonics 128 --hidden_size 512 --gru_size 1024
Generate engine sounds using trained models:
python scripts/inference.py
Note: As of now, the folder name of checkpoint for inference has to be defined directly within the inference.py CLI support will be inplemented soon.
The training and inference scripts use a unified CLI with the following key parameters:
--model_type
: Choose betweenhpn
(Harmonic-Plus-Noise) orptr
(Pulse-Train-Resonator)--dataset
: Specify dataset name (default:C_full_set
)--num_harmonics
: Number of harmonics (default: 100)--num_noisebands
: Number of noise bands (default: 256)--hidden_size
: Hidden layer size (default: 256)--gru_size
: GRU layer size (default: 512)
Configuration is managed through a base config system combined with CLI parameter overrides.
A Harmonic-Plus-Noise (HPN) baseline is included for comparative evaluation. HPN employs direct harmonic synthesis with systematic inharmonicity modeling and temporally structured noise components, using identical encoder-decoder architecture to PTR.
PTR consistently outperforms the HPN baseline across three engine configurations, achieving 5.7% improvement in total validation loss and 21% improvement in audio reconstruction on unseen data.
| Dataset | HPN Harm. | HPN STFT | HPN Total | PTR Harm. | PTR STFT | PTR Total |
|---|---|---|---|---|---|---|
| A | 0.107 | 1.781 | 0.944 | 0.090 | ||
| 1.649 | ||||||
| 0.872 | ||||||
| B | 0.059 | 1.824 | 0.943 | 0.055 | ||
| 1.754 | ||||||
| 0.907 | ||||||
| C | 0.166 | 2.093 | 1.132 | 0.117 | ||
| 2.017 | ||||||
| 1.069 | ||||||
| Mean | 0.111 | 1.899 | 1.006 | 0.088 | 1.807 | 0.949 |
This work utilizes the Procedural Engine Sounds Dataset, a comprehensive collection of procedurally generated engine audio with time-aligned control annotations.
- 19 hours of engine audio across varied operating conditions
- Time-aligned RPM, torque, throttle, and DFCO annotations
- Multiple engine configurations and acoustic scenarios
- Systematic coverage of engine operating parameters
Dataset Availability:
Zenodo:https://doi.org/10.5281/zenodo.16883336** Hugging Face Datasets**:https://huggingface.co/datasets/rdoerfler/procedural-engine-sounds
Evaluation reveals complementary strengths between synthesis approaches:
PTR: 5.7% superior validation performance, consistent training-validation transfer** HPN**: Greater flexibility across engine configurations, robust to harmonic irregularities- Both variants successfully capture authentic engine acoustic behaviors with distinct signatures
If you use the Procedural Engines Dataset in your research, please cite:
@dataset{doerfler_2025_procedural_engine_sounds,
author = {Doerfler, Robin},
title = {Procedural Engine Sounds Dataset},
month = {August},
year = 2025,
publisher = {Zenodo},
version = {1.0},
doi = {10.5281/zenodo.16883336},
url = {https://doi.org/10.5281/zenodo.16883336}
}
This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). See the LICENSE
file for details.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
Under the following terms:
- Attribution — You must give appropriate credit and indicate if changes were made
- NonCommercial — You may not use the material for commercial purposes
For commercial use, please contact the author.
Audio Examples: Supplementary audio examples demonstrating model outputs are available at:https://rdoerfler.github.io/ptr-model-page/Dataset: Procedural Engine Sounds Dataset onZenodoandHugging Face
This research demonstrates systematic integration of physics-informed inductive biases into differentiable synthesis architectures, providing a methodological framework applicable to physically-constrained audio generation beyond automotive contexts.
For questions or collaboration opportunities, please contact me or open an issue on this repository.
Keywords: Engine Sound Synthesis, Differentiable Signal Processing, Physics-Informed Neural Networks, Inductive Biases, Neural Audio Synthesis