Computer Vision and Bi-directional Neural Network for Extraction of Communications Signal from Noisy Spectrogram

Seksan Phonsri, Sankha Subhra Mukherjee, Mathini Sellathurai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


Extraction of communication signals from noisy spectrograms is a challenging problem which has not been explored extensively from an intelligent signal processing and computer vision based perspective. In this paper we propose a novel technique of extracting the communications signal from a noisy spectrogram using a combination of fuzzy neighborhood thresholding based self organizing neural network and morphological operations. We show that about 98% detection is achieved at 5% false alarm of a particular scenario outperforming traditional energy detection.

Original languageEnglish
Title of host publication2015 IEEE Conference on Antenna Measurements & Applications (CAMA)
Number of pages4
ISBN (Electronic)9781467391498
Publication statusPublished - 10 Mar 2015
EventIEEE Conference on Antenna Measurements 2015 - Chiang Mai, Thailand
Duration: 30 Nov 20152 Dec 2015


ConferenceIEEE Conference on Antenna Measurements 2015
CityChiang Mai


  • Computer Vision
  • Spectrogram
  • Bi-directional Self-organizing Neural Networks
  • Fuzzy Hostility Index
  • Morphological Filtering
  • Blob Detection

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