QuietHorizon

🔊 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.

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🧠 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)

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📊 Performance

Evaluated on an unseen test set.

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

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🧪 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

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📥 How to Use

Python Inference Example

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)