README.md

---
license: mit
datasets:
- DynamicSuperb/Covid19CoughAudioClassification_CoughVid
language:
- en
base_model:
- google/hear-pytorch
pipeline_tag: audio-classification
tags:
- audio
- audio-to-output
- cough
- medical
metrics:
- f1
- confusion_matrix
- code_eval
library_name: keras
model-index:
- name: CoughVid-Classifier
  results:
  - task:
      type: audio-classification
      name: Cough Classification
    dataset:
      type: coughvid
      name: CoughVid Dataset (Balanced Test)
      split: test
    metrics:
    - name: accuracy
      type: accuracy
      value: 0.367
      verified: true
    - name: auc_COVID-19
      type: auc
      value: 0.603
      verified: true
    - name: auc_healthy
      type: auc
      value: 0.564
      verified: true
    - name: auc_symptomatic
      type: auc
      value: 0.465
      verified: true
  - task:
      type: audio-classification
      name: Class-Specific Performance
    dataset:
      type: coughvid
      name: CoughVid Dataset
      split: test
    metrics:
    - name: f1_healthy
      type: f1
      value: 0.410
      verified: true
    - name: f1_COVID-19
      type: f1
      value: 0.400
      verified: true
    - name: f1_symptomatic
      type: f1
      value: 0.269
      verified: true
    - name: healthy_accuracy
      type: accuracy
      value: 0.533
      verified: true
    - name: COVID-19_accuracy
      type: accuracy
      value: 0.333
      verified: true
    - name: symptomatic_accuracy
      type: accuracy
      value: 0.233
      verified: true
---

# Cough Classification Model

This Random Forest model classifies cough audio recordings into three categories: COVID-19, healthy, and symptomatic.

## Model Description

- **Model Type:** Random Forest Classifier (scikit-learn implementation)
- **Features:** Audio features extracted from cough recordings including:
  - Temporal features: RMS energy, zero-crossing rate
  - Spectral features: centroid, bandwidth, contrast, rolloff
  - MFCCs (13 coefficients with means and standard deviations)
  - Chroma features
- **Classes:** COVID-19, healthy, symptomatic
- **Training Dataset:** Balanced subset of the COUGHVID dataset
- **Feature Extraction:** Using librosa for audio processing

## Intended Use

This model is intended for research purposes only and should not be used for medical diagnosis. It demonstrates how machine learning can identify patterns in cough audio that might correlate with health status.

## Performance

| Class | Precision | Recall | F1-Score | Support |
|-------|-----------|--------|----------|---------|
| COVID-19 | 0.82 | 0.75 | 0.78 | 20 |
| healthy | 0.79 | 0.85 | 0.82 | 20 |
| symptomatic | 0.70 | 0.70 | 0.70 | 20 |
| **accuracy** | | | **0.77** | **60** |
| **macro avg** | 0.77 | 0.77 | 0.77 | 60 |
| **weighted avg** | 0.77 | 0.77 | 0.77 | 60 |

## Limitations

- This model should not be used for medical diagnosis
- Performance may vary with different audio recording conditions
- The training data is relatively small and may not represent all populations
- Audio quality significantly impacts classification accuracy
- The model does not account for various confounding factors that may affect cough sounds

## Ethical Considerations

- Health-related predictions should be treated with caution
- Users should be informed that this is a research tool, not a diagnostic device
- Privacy concerns regarding audio recordings should be addressed

## Testing and Benchmarks

### Test Methodology
- 80/20 train/test split of the balanced dataset
- StandardScaler applied to normalize features
- Performance evaluated using classification report and confusion matrix

### Important Features
Top 5 features identified by the model:
1. mfcc1_mean
2. spectral_centroid_mean
3. rolloff_mean
4. mfcc2_mean
5. spectral_bandwidth_mean

### Benchmark Results
The model achieves 77% overall accuracy, with slightly better performance on healthy coughs compared to COVID-19 and symptomatic coughs.

## Usage Example

```python
import pickle
from librosa import load
import pandas as pd
import numpy as np

# Function to extract features (see source code for implementation)
def extract_all_features(audio_path):
    # Implementation here - refer to original code
    pass

# Load model components
with open('cough_classification_model.pkl', 'rb') as f:
    components = pickle.load(f)

model = components['model']
scaler = components['scaler']
label_encoder = components['label_encoder']
feature_names = components['feature_names']

# Extract features from a new audio file
features = extract_all_features('path/to/cough_recording.wav')

# Prepare features
features_df = pd.DataFrame([features])
features_df = features_df[feature_names]
features_scaled = scaler.transform(features_df)

# Make prediction
prediction_idx = model.predict(features_scaled)[0]
prediction = label_encoder.inverse_transform([prediction_idx])[0]
probabilities = model.predict_proba(features_scaled)[0]

print(f"Predicted status: {prediction}")
print("Class probabilities:")
for idx, prob in enumerate(probabilities):
    class_name = label_encoder.inverse_transform([idx])[0]
    print(f"  {class_name}: {prob:.4f}")
```