Deep SDR

This is an idea page for designs for an SDR project involving deep learning.

Implementation approach

1. Build sw/hw radio to collect data
2. feed training data to deep net
   1. voice recognition: extract transcript
      • use existing OSS for recognition?
   2. voice semantics: extract call signs for labels
      • handle unlabeled voices
   3. voice finger printing: match voices to call signs
   4. attempt fingerprinting radios: match voices to radios
3. program responses to specific voices
   • greetings
   • mailboxes
   • RDF

Data collection

• hardware: RTL-SDR on a pc or pi
• receive on one or more repeater outputs
  • bandwidth, segregate into channels
  • squelch detection
    • CTCSS recognition
    • FM detection, audio volume detection
    • signal strength
  • filter
    • remove ctcss
    • cut at squelch transitions
    • split into manageable chunk sizes
• save data
  • timestamp, frequency
  • demodulated data
  • I/Q data (later?)
  • voice recognition transcript
  • codec2 compression??

Training and analysis issues

• bias in data: male / female
• supervised labeling
• unsupervised labeling from transcripts / check correlation for errors
• noise in sample --> issues in transcription
• labeled and unlabled samples

External references

• Gnu Radio tutorials
• http://rtl-sdr.com