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--- |
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language: en |
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license: mit |
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tags: |
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- audio-classification |
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- engine-diagnostics |
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- knock-detection |
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- cnn |
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- 1d-cnn |
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datasets: |
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- custom |
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metrics: |
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- accuracy |
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- f1 |
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--- |
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# Engine Knock Detection - Custom 1D CNN |
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This model detects engine knock from raw audio waveforms using a custom 1D Convolutional Neural Network. |
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## Model Description |
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- **Architecture**: Custom 4-layer 1D CNN |
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- **Input**: Raw audio waveforms (80,000 samples @ 16kHz = 5 seconds) |
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- **Output**: Binary classification (clean vs knocking) |
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- **Framework**: PyTorch |
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- **Parameters**: ~5M trainable parameters |
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## Performance Metrics |
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Evaluated on test set: |
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| Metric | Score | |
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|-----------|--------| |
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| Accuracy | 0.7389 | |
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| Precision | 0.7660 | |
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| Recall | 0.7423 | |
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| F1-Score | 0.7539 | |
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## Usage |
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```python |
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import torch |
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import torchaudio |
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from huggingface_hub import hf_hub_download |
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# Load model architecture (you'll need to define Custom1DCNN class) |
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# See model architecture in the repository |
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from model import Custom1DCNN |
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model = Custom1DCNN(num_classes=2) |
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model_path = hf_hub_download(repo_id="cxlrd/engine-knock-cnn1d", filename="model.pth") |
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model.load_state_dict(torch.load(model_path, map_location='cpu')) |
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model.eval() |
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# Prepare audio |
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waveform, sample_rate = torchaudio.load('audio.wav') |
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if sample_rate != 16000: |
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waveform = torchaudio.transforms.Resample(sample_rate, 16000)(waveform) |
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# Pad or truncate to 80000 samples |
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if waveform.shape[1] > 80000: |
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waveform = waveform[:, :80000] |
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else: |
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waveform = torch.nn.functional.pad(waveform, (0, 80000 - waveform.shape[1])) |
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# Predict |
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with torch.no_grad(): |
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output = model(waveform) |
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prediction = torch.argmax(output, dim=1) |
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print('Clean' if prediction == 0 else 'Knocking') |
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``` |
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## Training Details |
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- **Dataset**: Custom engine sound recordings (1199 samples) |
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- **Training Split**: 70% train, 15% validation, 15% test |
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- **Optimizer**: Adam (lr=1e-3, weight_decay=1e-4) |
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- **Batch Size**: 32 |
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- **Early Stopping**: Patience of 5 epochs |
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- **No Preprocessing**: Direct raw waveform input |
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## Architecture Details |
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``` |
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Conv1D(1→64, k=80, s=4) → BatchNorm → ReLU → MaxPool(4) |
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Conv1D(64→128, k=3) → BatchNorm → ReLU → MaxPool(4) |
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Conv1D(128→256, k=3) → BatchNorm → ReLU → MaxPool(4) |
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Conv1D(256→512, k=3) → BatchNorm → ReLU → AdaptiveAvgPool |
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Dropout(0.5) → Linear(512→128) → ReLU → Dropout(0.3) → Linear(128→2) |
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``` |
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## Advantages |
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- **Fast Inference**: No spectrogram conversion needed |
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- **Lightweight**: Processes raw audio directly |
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- **Real-time Capable**: Suitable for edge deployment |
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## Citation |
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If you use this model, please cite: |
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```bibtex |
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@misc{engine-knock-cnn1d, |
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author = {cxlrd}, |
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title = {Engine Knock Detection with 1D CNN}, |
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year = {2025}, |
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publisher = {HuggingFace}, |
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howpublished = {\url{https://huggingface.co/cxlrd/engine-knock-cnn1d}} |
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} |
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``` |
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