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