Abstract
Cochlear implants (CIS) require efficient speech processing to maximize information transmission to the brain, especially in noise. A novel CI processing strategy was proposed in our previous studies, in which sparsity-constrained non-negative matrix factorization (NMF) was applied to the envelope matrix in order to improve the CI performance in noisy environments. It showed that the algorithm needs to be adaptive, rather than fixed, in order to adjust to acoustical conditions and individual characteristics. Here, we explore the benefit of a system that allows the user to adjust the signal processing in real time according to their individual listening needs and their individual hearing capabilities. In this system, which is based on MATLAB®, SIMULINK® and the xPC Target™ environment, the input/outupt (I/O) boards are interfaced between the SIMULINK blocks and the CI stimulation system, such that the output can be controlled successfully in the manner of a hardware-in-the-loop (HIL) simulation, hence offering a convenient way to implement a real time signal processing module that does not require any low level language. The sparsity constrained parameter of the algorithm was adapted online subjectively during an experiment with normal-hearing subjects and noise vocoded speech simulation. Results show that subjects chose different parameter values according to their own intelligibility preferences, indicating that adaptive real time algorithms are beneficial to fully explore subjective preferences. We conclude that the adaptive real time systems are beneficial for the experimental design, and such systems allow one to conduct psychophysical experiments with high ecological validity.
Highlights
Various speech processing algorithms have been proposed in the literature to reduce the background noise for different applications [1,2,3,4,5,6,7,8,9,10,11,12]
We hypothesized that (i) listeners in general would prefer different settings for different listening conditions (different signal-to-noise ratio (SNR)) and (ii) not all listeners would choose the same settings for any given listening condition
negative matrix factorization (NMF) is applied to the whole envelopegram with a dimension of 22 ∗ T, where T is the number of the short-time frames of the word ‘Din’
Summary
Various speech processing algorithms have been proposed in the literature to reduce the background noise for different applications [1,2,3,4,5,6,7,8,9,10,11,12]. Most signal processing algorithms need to be adaptive rather than fixed, in order to adjust to (a) acoustical conditions and (b) individual characteristics (e.g., different characteristics of hearing capability or pathology). Signal processing algorithms are fixed or may be first adjusted for each user and fixed. We wished to explore the benefit of a system that potentially allows the user to adjust the processing according to their individual listening needs at a particular time and their individual hearing capabilities. None of these can be predicted ahead with our current state of knowledge, so a real-time adaptive system is needed. In order to explore this idea, we needed to first implement a real-time system and, evaluate whether it was potentially beneficial
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