README.md

---
pretty_name: Procedural Engine Sounds Dataset
task_categories:
- audio-to-audio
- audio-classification
- automatic-speech-recognition
tags:
- audio
- audio-dataset
- engine-sounds
- combustion-engine
- procedural-generation
- time-aligned
- rpm
- torque
- automotive
- nvh
- vehicle-acoustics
- sound-synthesis
- noise-free
size_categories:
- 10B<n<100B
license: cc-by-nc-4.0
---


# Procedural Engine Sounds Dataset

## Dataset Description

The Procedural Engine Sounds Dataset is a comprehensive collection of synthetically generated and annotated engine audio samples. This dataset contains procedurally generated high-resolution engine sounds free of confounding noises, with detailed time-aligned annotations, designed for research in audio processing, vehicle acoustics, and synthetic sound generation.

## Associated Publication

The dataset and generation methodology are described in the following paper:

Doerfler, R., & Wyse, L. (2026).  
*Analysis-Driven Procedural Generation of an Engine Sound Dataset with Embedded Control Annotations.*  
https://arxiv.org/abs/2603.07584

## Dataset Details

### Dataset Summary

- **Repository:** procedural-engine-sounds
- **Version:** 1.0
- **Publication Year:** 2025
- **License:** CC BY-NC 4.0

### Research Applications

- **Audio Generation**: Train models to generate realistic engine sounds conditioned to time-varying engine operation states
- **Audio Classification**: Predict RPM and Engine Torque based on audio signals
- **Audio Analysis**: Research vehicle acoustics and engine sound patterns
- **Sound Synthesis**: Develop procedural audio generation techniques
- **Data Augmentation**: Use as augmentation material for in-cabin speech detection and recognition, noise suppression or other related tasks

### Technical Specifications

This dataset contains only audio signals - no textual or linguistic content. Both the engine sounds and annotations (RPM/torque information) are provided as audio signals at 48 kHz sample rate in WAV format.

## Dataset Structure

### Data Organization

The dataset is organized into 8 distinct sets with two categories:

**Full Sets** (A, B, C, D):
- 3,068 files total (across 4 sets)
- ~9.83 hours of audio total
- ~12.65 GB total
- **Per set (average): ~767 files, ~2.46 hours, ~3.16 GB**

**Large Sets** (E, F, G, H):
- 2,867 files total (across 4 sets)
- ~9.18 hours of audio total
- ~11.82 GB total
- **Per set (average): ~717 files, ~2.30 hours, ~2.96 GB**

**Dataset Total: 5,935 files, ~19.01 hours, ~24.47 GB**

### File Organization

```
README.txt (this file)
USAGE.txt (quick start guide)
audio/
├── A_full_set/ (767 files, ~2.46 hours, ~3.16 GB)
├── B_full_set/ (767 files, ~2.46 hours, ~3.16 GB)
├── C_full_set/ (767 files, ~2.46 hours, ~3.16 GB)
├── D_full_set/ (767 files, ~2.46 hours, ~3.16 GB)
├── E_large_set/ (717 files, ~2.30 hours, ~2.96 GB)
├── F_large_set/ (717 files, ~2.30 hours, ~2.96 GB)
├── G_large_set/ (717 files, ~2.30 hours, ~2.96 GB)
└── H_large_set/ (717 files, ~2.30 hours, ~2.96 GB)
metadata/
├── A_full_set_summary.json
├── A_full_set_stats.csv
├── B_full_set_summary.json
├── B_full_set_stats.csv
└── … (16 metadata files total)
```

### File Formats

**Audio Files:**
- Format: WAV
- Sample Rate: 48 kHz
- Channels: 4 (quad-channel)
- Bit Depth: 16 bit

**Metadata Files:**
- Summary files: JSON format
- Statistics files: CSV format (comma-separated values)

### Data Structure

Each audio file contains **4-channel audio** at 48 kHz sample rate:
- **Channel 1-2**: Stereo engine sound audio
- **Channel 3**: Engine speed (RPM × 0.0001) as continuous audio signal
- **Channel 4**: Engine torque (Nm × 0.001) as continuous audio signal

### Metadata Structure

#### Summary Files (.json)
Per-set statistics including:
- `num_files`: Number of audio files in set
- `total_duration_*`: Duration in seconds/minutes/hours
- `total_size_gb`: Storage size in GB
- `rpm_distribution`: Statistical distribution (min, max, mean, std, percentiles)
- `torque_distribution`: Statistical distribution (min, max, mean, std, percentiles)

#### Statistics Files (.csv)
Per-file metrics with columns:
- `filename`: Audio file name
- `samplerate`: Sample rate (48 kHz)
- `duration_sec`: File duration in seconds
- `size_MB`: File size in megabytes
- `rpm_min/max/mean/std`: RPM statistics for the file
- `torque_min/max/mean/std`: Torque statistics for the file

### Data Access

Each audio file is a standard WAV file containing a 4-channel audio array at 48 kHz sample rate. When loaded, you receive the raw multichannel audio data from which RPM and torque information can be extracted from channels 3 and 4 respectively.

### Audio Signal Encoding

- **RPM Signal**: Channel 3 contains engine speed values in RPM scaled by 0.0001 (multiply by 10,000 to get actual RPM)
- **Torque Signal**: Channel 4 contains torque values in Newton meters scaled by 0.001 (multiply by 1,000 to get actual Nm)
- **Engine Audio**: Channels 1-2 contain the stereo procedural engine sound

## Technical Requirements

To work with this dataset, you will need:
- Audio processing software capable of reading multi-channel WAV files
- Programming languages: Python (recommended with librosa, soundfile, or scipy), MATLAB, R, or similar
- For metadata: JSON and CSV reading capabilities

## Dataset Creation

### Source Data

All audio samples are synthetically generated using procedural audio synthesis techniques. 
No real-world engine recordings were used for audio generation. 
Dataset results were thoroughly analysed and compared to real world recordings to verify representativeness and similarity regarding engine order magnitudes and harmonic deviations.

### Annotations

Annotations were created during the generation process, with additional manual verification for quality assurance.

## Considerations for Using the Data

### Social Impact of Dataset

This dataset enables research in:
- Automotive audio simulation
- Vehicle sound design
- Audio processing algorithms
- Synthetic data generation techniques

### Discussion of Biases

As a synthetic dataset, it reflects the biases inherent in the procedural generation algorithms and may not capture all real-world engine sound variations.

### Other Known Limitations

- Limited to procedurally generated sounds
- May not represent all engine types or acoustic environments
- Generated with empirically determined synthesis parameters, hence contains fictional engine types and exhaust pipe configurations

## License and Usage

### License

This dataset is released under CC BY-NC 4.0 license (Creative Commons Attribution-NonCommercial 4.0 International).

**Attribution Required**: Please cite this dataset in any research or publications.

### Citation

```bibtex
@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}
}

@misc{doerfler2026analysisdrivenproceduralgenerationengine,
      title={Analysis-Driven Procedural Generation of an Engine Sound Dataset with Embedded Control Annotations}, 
      author={Robin Doerfler and Lonce Wyse},
      year={2026},
      eprint={2603.07584},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2603.07584}, 
}

```

## Contact

For questions or issues regarding the dataset, please use the Hugging Face repository discussion page or refer to the associated publication.

## Acknowledgments

This dataset was created through procedural audio synthesis leveraging established principles from engine acoustics research, including engine order analysis, extended harmonic-plus-noise synthesis methodologies, and exhaust system resonance modeling. The synthesis methodology builds upon decades of foundational research in vehicle acoustics and internal combustion engine sound modeling. Special thanks to the digital signal processing and vehicle acoustics research communities for their foundational work that made this dataset possible.