🎧 Preprocessing in Acoustic Drone Detection

1. Signal Acquisition

•  Input: Analog audio from omnidirectional or directional microphones (often MEMS or condenser mics).

• Sample Rate: Usually 16 kHz or 44.1 kHz, depending on system bandwidth and drone frequency profile.

• Channels: Single or multi-channel (for triangulation/localization).

2. Noise Filtering

• Removes environmental clutter:

  • Wind, insects, vehicles, and human speech

• Common methods:

  • Bandpass filtering (e.g., 200 Hz – 7 kHz)

  • Spectral subtraction

  • Wiener filters

  • Adaptive background modeling

3. Segmentation

• The audio stream is split into manageable windows:

  • Typical length: 1–2 seconds

  • May overlap (e.g., 50%) to preserve temporal continuity

• Segmentation allows real-time inference and continuous scanning

4. Feature Extraction

Transforms audio into a time-frequency representation:

✅ Common Features:

Feature Type	Description
MFCC (Mel-Frequency Cepstral Coefficients)	Mimics human hearing—captures timbre
Spectrogram	Intensity of frequencies over time (visualized as an image)
Chroma features	Tracks harmonic/pitch content
Zero-crossing rate	Frequency content via sign changes in waveform

Example: A 1s clip at 16kHz becomes a 2D array of 13 MFCCs × ~30 frames

5. Normalization

Ensures consistency in audio features regardless of mic distance or drone volume:

• Min-max scaling

• Mean subtraction / variance normalization

• Often critical when training across multiple drone models or environments

🔍 Output

The result is a standardized input matrix or image (e.g., MFCC or spectrogram) sent to the AI classifier, typically a:

• CNN (if using spectrogram images)

• RNN or LSTM (if maintaining time-sequence features)

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