A multi-band environment-adaptive approach to noise suppression for cochlear implants.

This paper presents an improved environment-adaptive noise suppression solution for the cochlear implants speech processing pipeline. This improvement is achieved by using a multi-band data-driven approach in place of a previously developed single-band data-driven approach. Seven commonly encountered noisy environments of street, car, restaurant, mall, bus, pub and train are considered to quantify the improvement. The results obtained indicate about 10% improvement in speech quality measures.

Real-time dual-microphone noise classification for environment-adaptive pipelines of cochlear implants.

This paper presents an improved noise classification in environment-adaptive speech processing pipelines of cochlear implants. This improvement is achieved by using a dual-microphone and by using a computationally efficient feature-level combination approach to achieve real-time operation. A new measure named Suppression Advantage is also defined in order to quantify the noise suppression improvement of an entire pipeline due to noise classification. The noise classification and suppression improvement results are presented for four commonly encountered noise environments.

Environment-adaptive speech enhancement for bilateral cochlear implants using a single processor.

A computationally efficient speech enhancement pipeline in noisy environments based on a single-processor implementation is developed for utilization in bilateral cochlear implant systems. A two-channel joint objective function is defined and a closed form solution is obtained based on the weighted-Euclidean distortion measure. The computational efficiency and no need for synchronization aspects of this pipeline make it a suitable solution for real-time deployment. A speech quality measure is used to show its effectiveness in six different noisy environments as compared to a similar one-channel enhancement pipeline when using two separate processors or when using independent sequential processing.

Real-time automatic tuning of noise suppression algorithms for cochlear implant applications.

The performance of cochlear implants deteriorates in noisy environments compared to quiet conditions. This paper presents an adaptive cochlear implant system, which is capable of classifying the background noise environment in real time for the purpose of adjusting or tuning its noise suppression algorithm to that environment. The tuning is done automatically with no user intervention. Five objective quality measures are used to show the superiority of this adaptive system compared to a conventional fixed noise-suppression system. Steps taken to achieve the real-time implementation of the entire system, incorporating both the cochlear implant speech processing and the background noise suppression, on a portable PDA research platform are presented along with the timing results.

Adding real-time noise suppression capability to the cochlear implant PDA research platform.

This paper presents the real-time implementation of an environment-adaptive noise suppression algorithm on an FDA-approved PDA platform for cochlear implant studies. This added capability involves identifying the background noise environment in real-time and adapting a data-driven noise suppression approach to that noise environment on-the-fly. Various software optimization steps are taken in order to achieve a real-time throughput on the PDA platform involving both the speech decomposition and the adaptive noise suppression components. Real-time timing results and a quantitative measure of noise suppression are presented.