---
license: mit
language:
- en
tags:
- audio-classification
- cnn
- environmental-sound
- anthropogenic-noise
- bioacoustics
- soundscape-monitoring
metrics:
- accuracy
- precision
- recall
- auc
library_name: keras
---

# 🔊 QuietHorizon CNN  
A convolutional neural network for **detecting anthropogenic noise vs natural soundscapes**.

QuietHorizon is a lightweight environmental audio classifier that identifies whether a 1–2 second audio clip contains **anthropogenic (human-made)** noise or **natural** sounds.  
It is designed to support **wildlife monitoring**, **soundscape conservation**, **noise-pollution mapping**, and **bioacoustic filtering pipelines**.

---

## 🧠 Model Summary

- **Architecture:** CNN (Mel-spectrogram input → Conv layers → Dense classifier)  
- **Framework:** TensorFlow / Keras  
- **Classes:**  
  - `0 = natural`  
  - `1 = anthropogenic`  
- **Training clips:** ~20,000 samples  
  - ~13k clean nature (birds, frogs, mammals, rain, thunder)  
  - ~7k anthropogenic (vehicles, tools, aircraft, boats, construction, etc.)  
- **Data augmentation:**  
  - Noise injection  
  - Gain shift  
  - Time stretch  
  - Pitch shift  
  - Synthetic mixtures (25% natural over anthropogenic)

---

## 📊 Performance

Evaluated on an unseen test set.

| Metric | Score |
|-------|-------|
| **Accuracy** | 0.95 |
| **Precision** | 0.95 |
| **Recall** | 0.96 |
| **ROC-AUC** | 0.99 |

This means the model is excellent at **detecting contaminated audio** and **rarely misses anthropogenic noise**.

---

## 🧪 Intended Use

**Intended Applications**
- Acoustic monitoring systems  
- Noise pollution detection (roads, aircraft, construction)  
- Wildlife conservation pipelines  
- Pre-filtering for bird/frog classifiers  
- Smart microphones / embedded eco-monitoring

**Not Intended For**
- Determining exact species  
- Identifying specific anthropogenic categories (e.g., car vs plane)  
- Legal, medical, or safety-critical decisions

---

## 📥 How to Use

### **Python Inference Example**

```python
import tensorflow as tf
import librosa
import numpy as np

model = tf.keras.models.load_model("quiet_horizon_cnn.keras")

def predict(path):
    y, sr = librosa.load(path, sr=22050, mono=True)
    mel = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128)
    logmel = librosa.power_to_db(mel)
    logmel = np.expand_dims(logmel, axis=(0, -1))  # shape: (1, H, W, 1)
    
    pred = model.predict(logmel)[0][0]
    return float(pred), "anthro" if pred > 0.5 else "nature"

score, label = predict("example.wav")
print(score, label)