• DocumentCode
    2396959
  • Title

    Real time speech enhancement for the noisy MRI environment

  • Author

    Pathak, Nishank ; Panahi, Issa ; Devineni, P. ; Briggs, Richard

  • Author_Institution
    Grad. Teaching Assistant, Univ. of Texas at Dallas, Richardson, TX, USA
  • fYear
    2009
  • fDate
    3-6 Sept. 2009
  • Firstpage
    6950
  • Lastpage
    6953
  • Abstract
    Performance of two Adaptive (nLMS and Normalized Sign-error LMS) and a single channel (LogMMSE) speech enhancement algorithms are tested on a floating point DSP to reveal their effectiveness in enhancing speech corrupted in noisy MRI environment with very low SNR. The purpose of experiments is to reduce the fatigue of the listener by eliminating the strong MRI noise. The experiments use actual data set collected from a 3-Tesla MRI machine. Results of the experiments and performance of the speech enhancement system are presented in this paper. The speech enhancement system is automated. Our experiments reveal that after enhancement of the speech signal using Sign-Error LMS, the residual noise shows characteristics of white noise in contrast to the residual noise of the other algorithms which is more structured. It is also shown that the Sign-Error LMS offers fast convergence in comparison to the other two methods.
  • Keywords
    biocommunications; biomedical MRI; medical diagnostic computing; speech; 3-Tesla MRI machine; least mean square; listener fatigue; noisy MRI environment; real time speech enhancement; residual noise; sign-error LMS; single channel speech enhancement algorithms; speech signal; white noise; Algorithms; Biomedical Engineering; Humans; Least-Squares Analysis; Magnetic Resonance Imaging; Noise; Signal Processing, Computer-Assisted; Software; Speech Acoustics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE
  • Conference_Location
    Minneapolis, MN
  • ISSN
    1557-170X
  • Print_ISBN
    978-1-4244-3296-7
  • Electronic_ISBN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/IEMBS.2009.5333749
  • Filename
    5333749