Deep SDR

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

This could be part of an advanced repeater controller, or it could be built separately, like a bot that talks to the repeater via a secondary radio or out of band channel.

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