---
pretty_name: Procedural Engine Sounds Dataset
task_categories:
- audio-to-audio
- audio-classification
- automatic-speech-recognition
tags:
- audio
- synthetic
- engine-sounds
- procedural-generation
- automotive
- sound-synthesis
- mechanical-sounds
- audio-analysis
- vehicle-acoustics
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.

## Dataset Details

### Dataset Summary

- **Repository:** procedural-engine-sounds
- **Paper:** [Coming Soon]
- **Point of Contact:** rdoerfler2210@gmail.com

### Supported Tasks

- **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 supression or other related tasks


### Languages

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.

## 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**

```
dataset/
├── audio/
│   ├── A_full_set/
│   ├── B_full_set/
│   ├── C_full_set/
│   ├── D_full_set/
│   ├── E_large_set/
│   ├── F_large_set/
│   ├── G_large_set/
│   └── H_large_set/
└── 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)
```

### Data Instances

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 Fields

When loaded programmatically:
- `audio`: 4-channel audio array [channels, samples]
- `sample_rate`: 48000 Hz
- `filename`: Original audio filename
- `duration`: Audio length in seconds
- `rpm_range`: [min, max] RPM values from channel 3
- `torque_range`: [min, max] torque values from channel 4

### Audio Signal Encoding

- **RPM Signal**: Channel 3 contains engine speed values in RPM scaled by 0.0001 (multiply by 10000 to get actual RPM)
- **Torque Signal**: Channel 4 contains torque values in Newton meters scaled by 0.001 (multiply by 1000 to get actual Nm)
- **Engine Audio**: Channels 1-2 contain the stereo procedural engine sound


## 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 varify representiveness 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

## Additional Information

### License

This dataset is released under CC BY-NC 4.0 license. 

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

### Citation

```bibtex
@dataset{procedural_engine_sounds,
  title={Procedural Engine Sounds Dataset},
  author={Robin Doerfler},
  year={2025},
  url={https://huggingface.co/datasets/rdoerfler/procedural-engine-sounds},
  license={CC-BY-NC-4.0}
}
```