Updated Docs

CITATION.cff

@@ -6,14 +6,14 @@ authors:
   - family-names: "Doerfler"
     given-names: "Robin"
     orcid: "https://orcid.org/0009-0007-3904-1941"
+doi: "10.5281/zenodo.16883336"
 version: "1.0.0"
-date-released: 2025-08-15
-url: "https://huggingface.co/datasets/rdoerfler/procedural-engine-sounds"
-repository-code: "https://huggingface.co/datasets/rdoerfler/procedural-engine-sounds"
+date-released: 2025-08-21
+url: "https://doi.org/10.5281/zenodo.16883336"
+repository-code: "https://zenodo.org/records/16883336"
 license: "CC-BY-NC-4.0"
 keywords:
   - "audio"
-  - "synthetic data"
   - "engine sounds"
   - "procedural generation"
   - "automotive"
@@ -26,7 +26,7 @@ abstract: |
   contains procedurally generated engine sounds with detailed annotations 
   encoded as 4-channel audio, designed for research in audio processing, 
   vehicle acoustics, and synthetic sound generation. The dataset includes 
-  5.935 high resolution audio files (48 kHz, 16 bit) totaling ~19 hours 
+  5,935 high resolution audio files (48 kHz, 16 bit) totalling ~19 hours 
   and 24.47 GB of content across 8 distinct sets, with RPM and torque 
   information embedded as continuous audio signals alongside stereo engine sounds.
 preferred-citation:
@@ -35,5 +35,8 @@ preferred-citation:
   authors:
     - family-names: "Doerfler"
       given-names: "Robin"
+      orcid: "https://orcid.org/0009-0007-3904-1941"
   year: 2025
-  url: "https://huggingface.co/datasets/rdoerfler/procedural-engine-sounds"
+  publisher: "Zenodo"
+  doi: "10.5281/zenodo.16883336"
+  url: "https://doi.org/10.5281/zenodo.16883336"

README.md

@@ -6,19 +6,24 @@ task_categories:
 - automatic-speech-recognition
 tags:
 - audio
-- synthetic
+- audio-dataset
 - engine-sounds
+- combustion-engine
 - procedural-generation
+- time-aligned
+- rpm
+- torque
 - automotive
-- sound-synthesis
-- mechanical-sounds
-- audio-analysis
+- nvh
 - vehicle-acoustics
+- sound-synthesis
+- noise-free
 size_categories:
 - 10B<n<100B
 license: cc-by-nc-4.0
 ---
 
+
 # Procedural Engine Sounds Dataset
 
 ## Dataset Description
@@ -30,21 +35,22 @@ The Procedural Engine Sounds Dataset is a comprehensive collection of synthetica
 ### Dataset Summary
 
 - **Repository:** procedural-engine-sounds
-- **Paper:** [Coming Soon]
-- **Point of Contact:** [email protected]
+- **Version:** 1.0
+- **Publication Year:** 2025
+- **Point of Contact:** [email protected]
+- **License:** CC BY-NC 4.0
 
-### Supported Tasks
+### 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 supression or other related tasks
-
+- **Data Augmentation**: Use as augmentation material for in-cabin speech detection and recognition, noise suppression or other related tasks
 
-### Languages
+### 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.
+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
 
@@ -66,26 +72,41 @@ The dataset is organized into 8 distinct sets with two categories:
 
 **Dataset Total: 5,935 files, ~19.01 hours, ~24.47 GB**
 
+### File Organization
+
 ```
-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)
+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)
 ```
 
-### Data Instances
+### 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
@@ -111,22 +132,22 @@ Per-file metrics with columns:
 - `rpm_min/max/mean/std`: RPM statistics for the file
 - `torque_min/max/mean/std`: Torque statistics for the file
 
-### Data Fields
+### Data Access
 
-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
+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 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)
+- **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
 
@@ -134,7 +155,7 @@ When loaded programmatically:
 
 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.
+Dataset results were thoroughly analysed and compared to real world recordings to verify representativeness and similarity regarding engine order magnitudes and harmonic deviations.
 
 ### Annotations
 
@@ -160,22 +181,36 @@ As a synthetic dataset, it reflects the biases inherent in the procedural genera
 - 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 and Usage
 
 ### License
 
-This dataset is released under CC BY-NC 4.0 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{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}
+@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}
 }
 ```
+
+## Contact Information
+
+For questions, issues, or collaboration opportunities:
+- **Email:** [email protected]
+- **Dataset DOI:** 10.5281/zenodo.16883336
+- **Related Publications:** Available upon paper 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.

USAGE.txt

@@ -0,0 +1,114 @@
+USAGE.txt - Procedural Engine Sounds Dataset
+==========================================
+
+QUICK START GUIDE
+-----------------
+
+This dataset contains 4-channel WAV audio files with engine sounds and embedded RPM/torque data.
+
+FILE STRUCTURE:
+- audio/[A-H]_*_set/: WAV files organized in 8 sets
+- metadata/: JSON summaries and CSV statistics for each set
+
+AUDIO CHANNEL LAYOUT:
+- Channel 1-2: Stereo engine sound audio
+- Channel 3: RPM values (multiply by 10,000 for actual RPM)
+- Channel 4: Torque values (multiply by 1,000 for actual Newton-meters)
+
+BASIC USAGE EXAMPLES:
+
+1. PYTHON (with soundfile):
+   ```python
+   import soundfile as sf
+   import numpy as np
+   
+   # Load a 4-channel audio file
+   audio, sr = sf.read('audio/A_full_set/engine_001.wav')
+   
+   # Extract channels
+   engine_left = audio[:, 0]      # Left engine audio
+   engine_right = audio[:, 1]     # Right engine audio  
+   rpm_signal = audio[:, 2] * 10000   # RPM values
+   torque_signal = audio[:, 3] * 1000 # Torque in Nm
+   ```
+
+2. PYTHON (with librosa):
+   ```python
+   import librosa
+   
+   # Load specific channels
+   engine_audio, sr = librosa.load('audio/A_full_set/engine_001.wav', 
+                                   sr=48000, mono=False)
+   # Result shape: (4, samples) - channels x samples
+   ```
+
+3. MATLAB:
+   ```matlab
+   [audio, fs] = audioread('audio/A_full_set/engine_001.wav');
+   
+   engine_left = audio(:,1);
+   engine_right = audio(:,2);
+   rpm = audio(:,3) * 10000;
+   torque = audio(:,4) * 1000;
+   ```
+
+METADATA ACCESS:
+
+1. Load set summary (JSON):
+   ```python
+   import json
+   with open('metadata/A_full_set_summary.json', 'r') as f:
+       summary = json.load(f)
+   print(f"Set A contains {summary['num_files']} files")
+   ```
+
+2. Load file statistics (CSV):
+   ```python
+   import pandas as pd
+   stats = pd.read_csv('metadata/A_full_set_stats.csv')
+   print(stats.head())  # View first few files' stats
+   ```
+
+DATASET ORGANIZATION:
+- Full Sets (A,B,C,D): ~767 files each, ~2.46 hours each
+- Large Sets (E,F,G,H): ~717 files each, ~2.30 hours each
+- Total: 5,935 files, ~19 hours, ~24.5 GB
+
+TECHNICAL SPECS:
+- Sample Rate: 48 kHz
+- Format: WAV (uncompressed)
+- Channels: 4 (quad)
+- Total Duration: ~19.01 hours
+- Total Size: ~24.47 GB
+
+COMMON WORKFLOWS:
+
+1. AUDIO ANALYSIS:
+   - Load engine audio (channels 1-2)
+   - Analyze spectral content, harmonics
+   - Correlate with RPM/torque data (channels 3-4)
+
+2. MACHINE LEARNING:
+   - Use engine audio as input features
+   - Use RPM/torque as target labels
+   - Train regression or classification models
+
+3. AUDIO SYNTHESIS:
+   - Study relationship between RPM/torque and audio features
+   - Train generative models conditioned on engine parameters
+   - Use for data augmentation in automotive applications
+
+REQUIREMENTS:
+- Audio processing software supporting multi-channel WAV
+- ~25 GB free disk space
+- Python/MATLAB/R with audio processing libraries (recommended)
+
+TROUBLESHOOTING:
+- If files won't load: Check multi-channel WAV support in your software
+- If values seem wrong: Remember to apply scaling (RPM×10000, Torque×1000)
+- For large dataset: Consider loading files individually rather than all at once
+
+LICENSE: CC BY-NC 4.0 (Attribution-NonCommercial)
+CONTACT: [email protected]
+
+For detailed documentation, see README.txt