Advertisement

Wavelet Based RTL-SDR Real Time Signal Denoising in GNU Radio

  • U. Reshma
  • H. B. Barathi Ganesh
  • J. Jyothi
  • R. Gandhiraj
  • K. P. Soman
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 43)

Abstract

Noise removal is considered to be an efficacious step in processing any kind of data. Here the proposed model deals with removal of noise from aperiodic and piecewise constant signals by utilizing wavelet transform, which is being realized in GNU Radio platform. We have also dealt with the replacement of Universal Software Radio Peripheral with RTL-SDR for a low cost Radio Frequency Receiver system without any compromise in its efficiency. Wavelet analyzes noise level separately at each wavelet scale in time-scale domain and adapts the denoising algorithm especially for aperiodic and piecewise constant signals. GNU Radio companion serves well in analysis and synthesis of real time signals.

Keywords

Signal denoising Wavelets Continuous wavelet transform Multirate signal processing GNU radio companion 

References

  1. 1.
    Huang, Y., Arden, J., Chen, J.: Analysis and comparison of multichannel noise reduction methods in a common framework. IEEE Trans. Audio Speech Lang. Process. 16, 957–968 (2008)Google Scholar
  2. 2.
    Maher, A.G., King, R.W., Rathmell, J.G.: A comparison of noise reduction techniques for speech recognition in telecommunications environments. In: Communications’ 92: Communications Technology, Services and Systems, p. 107 (1992)Google Scholar
  3. 3.
    Soman, K.P.: Insight into wavelets: from theory to practice. 1em plus 0.5em minus 0.4em PHI Learning Pvt. Ltd. (2010)Google Scholar
  4. 4.
  5. 5.
    Mihov, S.G., Ivanov, R.M., Popov, A.N.: Denoising speech signals by wavelet transform, pp. 712–715. Annual, J. Electron. (2009)Google Scholar
  6. 6.
    Chang, S.G., Yu, B., Vetterli, M.: Spatially adaptive wavelet thresholding with context modeling for image denoising. IEEE Trans. Image Process. 9, 1522–1531 (2000)Google Scholar
  7. 7.
    Sardy, S., Tseng, P., Bruce, A.: Robust wavelet denoising. IEEE Trans. Signal Process. 49, 1146–1152 (2001)Google Scholar
  8. 8.
    Durand, S., Froment, J.: Artifact free signal denoising with wavelets. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Proceedings (ICASSP’01), vol. 6 (2001)Google Scholar
  9. 9.
    Soman, K.P., Ramanathan, R.: Digital signal and image processing-the sparse way. 1em plus 0.5em minus 0.4em Isa Publication (2012)Google Scholar
  10. 10.
    Abirami, M., Hariharan, V., Sruthi, M.B., Gandhiraj, R., Soman, K.P.: Exploiting GNU radio and USRP: an economical test bed for real time communication systems. In: Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT). IEEE (20130Google Scholar
  11. 11.
    Gandhiraj, R., Ram, R., Soman, K.P.: Analog and digital modulation toolkit for software defined radio. Procedia Eng. 30, 1155–1162 (2012)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • U. Reshma
    • 1
  • H. B. Barathi Ganesh
    • 1
  • J. Jyothi
    • 1
  • R. Gandhiraj
    • 2
  • K. P. Soman
    • 1
  1. 1.Centre for Excellence in Computational Engineering and NetworkingAmrita Vishwa VidyapeethamCoimbatoreIndia
  2. 2.Department of Electronics and Communication EngineeringAmrita Vishwa VidyapeethamCoimbatoreIndia

Personalised recommendations