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Blind Noise Reduction for Speech Enhancement by Simulated Auditory Nerve Representations

  • Anton YakovenkoEmail author
  • Aleksandr Antropov
  • Galina Malykhina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11555)

Abstract

Background and environmental noises negatively affect the quality of verbal communication between humans as well as in human-computer interaction. However, this problem is efficiently solved by a healthy auditory system. Hence, the knowledge about the physiology of auditory perception can be used along with noise reduction algorithms to enhance speech intelligibility. The paper suggests an approach to noise reduction at the level of the auditory periphery. The approach involves an adaptive neural network algorithm of independent component analysis for blind source separation using simulated auditory nerve firing probability patterns. The approach has been applied to several categories of colored noise models and real-world acoustic scenes. The suggested technique has significantly increased the signal-to-noise ratio for the auditory nerve representations of complex sounds due to the variability in spatial positioning of sound sources and a flexible number of sensors.

Keywords

Speech enhancement Noise reduction Blind source separation Independent component analysis Machine hearing Auditory periphery model Auditory nerve responses FastICA 

Notes

Acknowledgments

The reported study was funded by the Russian Foundation for Basic Research according to the research project No 18-31-00304.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anton Yakovenko
    • 1
    Email author
  • Aleksandr Antropov
    • 1
  • Galina Malykhina
    • 1
    • 2
  1. 1.Peter the Great St. Petersburg Polytechnic UniversitySt. PetersburgRussia
  2. 2.Russian State Scientific Center for Robotics and Technical CyberneticsSt. PetersburgRussia

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