🧠 Step-by-Step: Building an Acoustic Drone Detection AI Model

🎙️ 1. Collect Acoustic Data

You’ll need a large dataset of audio recordings from:

• Various drone models (FPV, DJI, quadcopters, etc.)
• Different environments (open field, urban, wind, birds)
• Multiple microphone types and positions

Label each file by:

• Drone model/type
• Recording conditions
• Distance from mic
• Flight mode (hover, fast pass, ascend)

🧹 2. Preprocess The Audio

Convert raw audio into a format suitable for machine learning:

• Resample to a uniform rate (e.g., 16 kHz)
• Normalize amplitude levels
• Apply Short-Time Fourier Transform (STFT) or MFCC (Mel Frequency Cepstral Coefficients)
  • These extract spectrograms and sound signature features

🤖 3. Build The AI Model

Model Type:

• CNN (Convolutional Neural Network) for spectrogram classification
• RNN/LSTM if using time sequences
• Hybrid CNN+LSTM for both spatial and temporal features

Architecture:

• Input: MFCC or spectrogram
• Hidden layers: CNN or LSTM blocks
• Output: Classification (e.g., FPV drone, no drone, bird, noise)

🧪 4. Train The Model

• Use labeled audio data split into train/test sets
• Use data augmentation to simulate real conditions (wind, echo, overlapping sounds)
• Monitor accuracy, precision, recall

🧭 5. Deploy In Real-Time System

• Convert model with ONNX or TensorFlow Lite for edge devices
• Deploy on:
• Base perimeter sensor nodes (e.g., Raspberry Pi + mic arrays)
• Fusion with EO/RF data systems

🎯 Output:

• Drone detected → classify type → feed to C2 system
• Auto-trigger visual tracking or jamming

📦 Bonus: Use Pre-Trained or Open Datasets

• Drone Detection Challenge Dataset (IEEE, Kaggle)
• MAVD Dataset – Micro Aerial Vehicle Dataset
• Augment with synthetic drone noise generators if real data is limited

🔐 Security/Field Tips

• Use directional microphones or beamforming arrays for localization
• Train model to filter out false positives (e.g., lawnmowers, birds, helicopters)
• Retrain regularly to adapt to new drone types