USAGE.txt - Procedural Engine Sounds Dataset
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QUICK START GUIDE
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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: doerflerrobin@gmail.com

For detailed documentation, see README.txt