Neural network-based adaptive noise cancellation for enhancement of speech auditory brainstem responses

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• 作者：Shiva Gholami-Boroujeny ; Anwar Fallatah…
• 关键词：Speech auditory brainstem responses ; Adaptive filtering ; Multilayer perceptron neural network
• 刊名：Signal, Image and Video Processing
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：10
• 期：2
• 页码：389-395
• 全文大小：697 KB
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• 作者单位：Shiva Gholami-Boroujeny (1)
  Anwar Fallatah (1)
  Brian P. Heffernan (1)
  Hilmi R. Dajani (1)
  
  1. School of Electrical Engineering and Computer Science, University of Ottawa, 161 Louis Pasteur, Ottawa, ON, K1N 6N5, Canada
  
• 刊物类别：Engineering
• 刊物主题：Signal,Image and Speech Processing
  Image Processing and Computer Vision
  Computer Imaging, Vision, Pattern Recognition and Graphics
  Multimedia Information Systems
  
• 出版者：Springer London
• ISSN：1863-1711

文摘

The measurement of the speech-evoked auditory brainstem response (speech ABR) is a promising technique for evaluating auditory function. However, the speech ABR is severely contaminated by background noise related to other brain electrical activity. The most commonly used method to enhance the signal-to-noise ratio (SNR) of the response is coherent averaging, while recently adaptive filtering has also been reported. All of the applied methods are based on linear operations, but since the assumption of linearity may not be valid for neural activity, linear methods may not be adequate. In this paper, we present a new nonlinear adaptive noise cancellation (ANC) based on a multilayer perceptron neural network to enhance the speech ABR and compare its performance with a linear ANC algorithm based on least mean squares adaptive filtering. The effectiveness of the methods is tested using speech ABR data and is based on two different types of SNR measures, the local SNR at the fundamental frequency of the response and the overall SNR. The results show that the nonlinear neural network-based ANC can reduce the required recording time and performs better than the linear ANC especially when the SNR of the recorded speech ABR is low.