Real-time Simulink implementation of noise adaptive speech processing pipeline of cochlear implants

Abstract This paper presents the real-time Simulink implementation of a noise adaptive speech processing pipeline for cochlear implants that was developed in a previous work. After providing an overview of each component or module in the pipeline, it is described how each module is implemented in Simulink so that the input audio frames are processed in real-time. This Simulink implementation allows the same code to be run on different hardware boards that are supported by Simulink. The performance of this implemented pipeline is evaluated in terms of six objective measures of speech quality. The results obtained indicate the effectiveness of suppressing noise in this speech processing pipeline when using the implemented automatic mechanism to identify the noise environment.

[1]  Nasser Kehtarnavaz,et al.  Smartphone-Based Real-Time Digital Signal Processing , 2015, Smartphone-Based Real-Time Digital Signal Processing.

[2]  Jianfeng Chen,et al.  Investigations into the relationship between measurable speech quality and speech recognition rate for telephony speech , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[3]  Jessica J. M. Monaghan,et al.  Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users , 2017, Hearing Research.

[4]  David Malah,et al.  Speech enhancement using a minimum mean-square error log-spectral amplitude estimator , 1984, IEEE Trans. Acoust. Speech Signal Process..

[5]  Rainer Martin,et al.  Statistical Methods for the Enhancement of Noisy Speech , 2005 .

[6]  Nasser Kehtarnavaz,et al.  Background noise classification using random forest tree classifier for cochlear implant applications , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  Andries P. Hekstra,et al.  Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[8]  Jesper Jensen,et al.  A data-driven approach to optimizing spectral speech enhancement methods for various error criteria , 2007, Speech Commun..

[9]  Antony William Rix,et al.  Perceptual evaluation of speech quality (PESQ): The new ITU standard for end-to-end speech quality a , 2002 .

[10]  Nasser Kehtarnavaz,et al.  Automatic switching between noise classification and speech enhancement for hearing aid devices , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[11]  Philipos C. Loizou,et al.  Speech Enhancement: Theory and Practice , 2007 .

[12]  Agamemnon Krasoulis,et al.  Development of a Real Time Sparse Non-Negative Matrix Factorization Module for Cochlear Implants by Using xPC Target , 2013, Sensors.

[13]  Nasser Kehtarnavaz,et al.  Real-time implementation of cochlear implant speech processing pipeline on smartphones , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[15]  Nasser Kehtarnavaz,et al.  Smartphone-based real-time classification of noise signals using subband features and random forest classifier , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[16]  S. Soli,et al.  Development of the Hearing in Noise Test for the measurement of speech reception thresholds in quiet and in noise. , 1994, The Journal of the Acoustical Society of America.

[17]  Nasser Kehtarnavaz,et al.  Real-Time Automatic Tuning of Noise Suppression Algorithms for Cochlear Implant Applications , 2012, IEEE Transactions on Biomedical Engineering.

[18]  Leslie M. Collins,et al.  The Effects of Noise on Speech Recognition in Cochlear Implant Subjects: Predictions and Analysis Using Acoustic Models , 2005, EURASIP J. Adv. Signal Process..

[19]  Philipos C. Loizou,et al.  Mimicking the human ear , 1998, IEEE Signal Process. Mag..

[20]  Shih-Tsang Tang,et al.  Objective Measurement of Speech Quality for Hearing Aids , 2013 .

[21]  Nasser Kehtarnavaz,et al.  A multi-band environment-adaptive approach to noise suppression for cochlear implants , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.