CCi-MOBILE: Design and Evaluation of a Cochlear Implant and Hearing Aid Research Platform for Speech Scientists and Engineers

Hansen, John H. L.; Ali, Hina; Saba, Juliana N.; Charan, M C Ram; Mamun, Nursadul; Ghosh, Ria; Brueggeman, Avamarie

doi:10.1109/bhi.2019.8834652

Cited by 22 publications

(13 citation statements)

References 5 publications

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“…Implantable Medical Devices are implanted into the human body as shown in Figure 4. The most common devices belonging to this category are the Pacemaker [42], Neurostimulators [43], Insulin Pumps, Glucose Monitoring Systems [44], Gastric Stimulators [45], Foot Drop Implants [46], Cochlear Implants [47], and Drug Pumps [48], etc. Here, we will discuss each layer with its components.…”

Section: Device Layermentioning

confidence: 99%

Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)

et al. 2022

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The Internet of Things (IoT) is an emerging field consisting of Internet-based globally connected network architecture. A subset of IoT is the Internet of Healthcare Things (IoHT) that consists of smart healthcare devices having significant importance in monitoring, processing, storing, and transmitting sensitive information. It is experiencing novel challenges regarding data privacy protection. This article discusses different components of IoHT and categorizes various healthcare devices based on their functionality and deployment. This article highlights the possible points and reasons for data leakage, such as conflicts in laws, the use of sub-standard devices, lack of awareness, and the non-availability of dedicated local law enforcement agencies. This article draws attention to the escalating demand for a suitable regulatory framework and analyzes compliance problems of IoHT devices concerning healthcare data privacy and protection regulations. Furthermore, the article provides some recommendations to improve the security and privacy of IoHT implementation.

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Section: Device Layermentioning

confidence: 99%

Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)

et al. 2022

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“…(5) An 'n-of-m' CI signal processing strategy selects the 8 most im- portant auditory features employed with a CIS-Continuous Interleaved Sampling strategy. (6) Finally, biphasic pulses are generated from the selected features and sent to the UTDallas CCi-MOBILE research interface board through electrical stimulations [12]. These electrical stimulations can be visualized using electrodograms.…”

Section: Cochlear Implant Signal Processing Pipelinementioning

confidence: 99%

“…This study also explores the causal structures of the proposed CNN-based algorithms. The causal CNNs use only previous samples of the signals and offer a viable option for real-time implementations of CCi-Mobile research platforms [12].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Speech Enhancement for Cochlear Implant Recipients

2019

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Attempts to develop speech enhancement algorithms with improved speech intelligibility for cochlear implant (CI) users have met with limited success. To improve speech enhancement methods for CI users, we propose to perform speech enhancement in a cochlear filter-bank feature space, a feature-set specifically designed for CI users based on CI auditory stimuli. We leverage a convolutional neural network (CNN) to extract both stationary and non-stationary components of environmental acoustics and speech. We propose three CNN architectures:(1) vanilla CNN that directly generates the enhanced signal;(2) spectral-subtraction-style CNN (SS-CNN) that first predicts noise and then generates the enhanced signal by subtracting noise from the noisy signal; (3) Wiener-style CNN (Wiener-CNN) that generates an optimal mask for suppressing noise. An important problem of the proposed networks is that they introduce considerable delays, which limits their real-time application for CI users. To address this, this study also considers causal variations of these networks. Our experiments show that the proposed networks (both causal and non-causal forms) achieve significant improvement over existing baseline systems. We also found that causal Wiener-CNN outperforms other networks, and leads to the best overall envelope coefficient measure (ECM). The proposed algorithms represent a viable option for implementation on the CCi-MOBILE research platform as a pre-processor for CI users in naturalistic environments.

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“…The implant comprises of two important components coupled using a powerful magnetfirst is an external sound processor and the other is a surgically implanted electrode array (16)(17)(18)(19)(20)(21)(22) electrode long) connecting to the auditory nerve. At a time only, a limited number of electrodes can be stimulated in the electrode-array, based on the mechanism and design of the array implanted [8].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Cochlear Implant Users’ Ability for Speaker Identification Using CI Auditory Stimuli

2019

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Speaker recognition is a biometric modality that uses underlying speech information to determine the identity of the speaker. Speaker Identification (SID) under noisy conditions is one of the challenging topics in the field of speech processing, specifically when it comes to individuals with cochlear implants (CI). This study analyzes and quantifies the ability of CI-users to perform speaker identification based on direct electric auditory stimuli. CI users employ a limited number of frequency bands (8 ∼ 22) and use electrodes to directly stimulate the Basilar Membrane/Cochlear in order to recognize the speech signal. The sparsity of electric stimulation within the CI frequency range is a prime reason for loss in human speech recognition, as well as SID performance. Therefore, it is assumed that CIusers might be unable to recognize and distinguish a speaker given dependent information such as formant frequencies, pitch etc. which are lost to un-simulated electrodes. To quantify this assumption, the input speech signal is processed using a CI Advanced Combined Encoder (ACE) signal processing strategy to construct the CI auditory electrodogram. The proposed study uses 50 speakers from each of three different databases for training the system using two different classifiers under quiet, and tested under both quiet and noisy conditions. The objective result shows that, the CI users can effectively identify a limited number of speakers. However, their performance decreases when more speakers are added in the system, as well as when noisy conditions are introduced. This information could therefore be used for improving CI-user signal processing techniques to improve human SID.

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CCi-MOBILE: Design and Evaluation of a Cochlear Implant and Hearing Aid Research Platform for Speech Scientists and Engineers

Cited by 22 publications

References 5 publications

Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)

Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)

Convolutional Neural Network-Based Speech Enhancement for Cochlear Implant Recipients

Quantifying Cochlear Implant Users’ Ability for Speaker Identification Using CI Auditory Stimuli

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