Skip to content

spectrogram_analysis.md

Latest commit

 

History

History
67 lines (53 loc) · 3.07 KB

spectrogram_analysis.md

File metadata and controls

67 lines (53 loc) · 3.07 KB

Underwater Acoustic Spectrogram Analysis

Dataset Overview

Total number of spectrogram files: 70

Distribution by Category

  • env_noise: 20 files
  • bio_whale: 7 files
  • bio_fish: 4 files
  • bio_coral: 9 files
  • manmade_boat: 7 files
  • manmade_ship: 2 files
  • manmade_submarine: 9 files
  • manmade_speedboat: 2 files
  • transient: 10 files

Key Observations

Environmental Noise

  • Characterized by more uniform energy distribution across frequencies
  • Often shows non-stationary patterns over time
  • Lower overall intensity compared to signal categories

Biological Signals

  • Whale calls: Distinctive frequency modulation patterns, concentrated energy in specific frequency bands
  • Fish sounds: Short, impulsive patterns with broader frequency content
  • Coral scraping: Irregular bursts of broadband energy

Man-made Signals

  • Boats/Ships: Strong harmonic structure with clear fundamental frequency and overtones
  • Submarines: Low-frequency tonals, sometimes with frequency shifts
  • Speedboats: Higher frequency content with potential Doppler effects

Transient Signals

  • Brief, high-energy broadband events
  • Sparse in time domain
  • Wide frequency range coverage

Implications for SimCLR Feature Extractor Design

Data Augmentation Strategies

  1. Time-domain augmentations:

    • Time shifting: To handle varying onset times of signals
    • Time masking: To simulate intermittent signals and improve robustness
    • Time stretching/compression: To handle variations in signal duration

  2. Frequency-domain augmentations:

    • Frequency shifting: To handle variations in pitch/frequency
    • Frequency masking: To improve robustness to frequency-selective noise
    • Pitch shifting: Particularly important for tonal signals like whale calls and boat engines

  3. Intensity augmentations:

    • Amplitude scaling: To handle the orders of magnitude variations in signal strength
    • Adding Gaussian noise: To improve robustness to background noise

Architecture Considerations

  1. Receptive field: Model should capture both fine-grained temporal patterns (e.g., transients) and longer-term patterns (e.g., whale calls)
  2. Multi-scale processing: Different acoustic events occur at different time and frequency scales
  3. Attention mechanisms: May help focus on relevant parts of the spectrogram while ignoring noise
  4. Frequency-aware design: Different frequency bands may require different processing

Projection Head Design

  • Projection dimension should be sufficiently large to capture the diversity of acoustic patterns
  • Multiple non-linear layers may be beneficial for learning complex representations

Conclusion

The underwater acoustic spectrograms exhibit diverse characteristics across different categories. The SimCLR feature extractor should be designed to handle this diversity through appropriate data augmentations and model architecture. The self-supervised approach is particularly well-suited for this domain due to the availability of unlabeled data and the need to learn robust representations that can distinguish between different types of acoustic signals.