The CCi-MOBILE Vocoder

Ali, Hina; Mamun, Nursadul; Bruggeman, Avamarie; Shekar, Ram C. M. C.; Saba, Juliana N.; Hansen, John H. L.

doi:10.1121/1.5068238

Cited by 6 publications

(5 citation statements)

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“…To achieve real-time performance with minimal processing delay, the hardware-interface board buffers the incoming and outgoing data stream on a frame-by-frame basis. In this design, a ping-pong strategy [7] is used for data-storage and processing. Pong buffers (BUF 1 of electric and acoustic) will process data when ping buffers (BUF 0 of electric and acoustic) are storing data.…”

Section: A Hardware Designmentioning

confidence: 99%

“…Unlike traditional listening experiments where the participant is presented speech in a sound booth through loud speakers, CCi-MOBILE can directly communicate with the intra-cochlear array, bypassing the proprietary sound processing schemes programmed within the user's clinical processor. Additionally, a vocoder was developed within the CCi-MOBILE software suite to yield an acoustic approximation for NH listeners of the electric input heard by CI users [7]. For scientists exploring acoustics in various environments and noise types (e.g., the multi-talker phenomenon known as the cocktail party effect), the platform can record naturalistic audio without the user being constrained to a sound booth.…”

Section: Applicationsmentioning

confidence: 99%

“…Approximately 35 out of 10,000 children suffer from sever to permanent hearing loss, which makes the phenomenon the most common birth defect in developed countries [1]. In the United States alone, the percentage of individuals impacted by hearing loss ranges from 15% in school-age children (ages [6][7][8][9][10][11][12][13][14][15][16][17][18][19] to about 33% at 65 years of age [2]. Assistive hearing devices, such as hearing aids (HAs), bone-conduction devices, and CIs, are some examples of the prevalent technology that can help to provide/improve hearing sensation.…”

Section: Introductionmentioning

confidence: 99%

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

Hansen

Ali

Saba

et al. 2019

2019 IEEE EMBS International Conference on Biomedical &Amp; Health Informatics (BHI)

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Hearing loss is an increasingly prevalent condition resulting from damage to the inner ear which causes a reduction in speech intelligibility. The societal need for assistive hearing devices has increased exponentially over the past two decades; however, actual human performance with such devices has only seen modest gains relative to advancements in digital signal processing (DSP) technology. A major challenge with clinical hearing technologies is the limited ability to run complex signal processing algorithms requiring high computation power. The CCi-MOBILE platform, developed at UT-Dallas, provides the research community with an open-source, flexible, easy-to-use, software-mediated, powerful computing research interface to conduct a wide variety of listening experiments. The platform supports cochlear implants (CIs) and hearing aids (HAs) independently, as well as bimodal hearing (i.e., a CI in one ear and HA in the contralateral ear). The platform is ideally suited to address hearing research for: both quiet and naturalistic noisy conditions, sound localization, and lateralization. The platform uses commercially available smartphone/tablet devices as portable sound processors and can provide bilateral electric and acoustic stimulation. The hardware components, firmware, and software suite are presented to demonstrate safety to the speech scientist and CI/HA user, highlight user-specificity, and outline various applications of the platform for research.

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Section: A Hardware Designmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Hansen

Ali

Saba

et al. 2019

2019 IEEE EMBS International Conference on Biomedical &Amp; Health Informatics (BHI)

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“…A cochlear implant (CI) is an implantable electronic device that provides the necessary sensation for hearing [1,2,3]; CI partially restores hearing ability for subjects with sensorineural hearing loss (generally profound hearing loss). According to a report by the U.S. Food and Drug Administration, over 96000 people in US (324,000 people worldwide) have received a CI device by the end of 2012 [4].…”

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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“…A cochlear implant is an implantable electric device that allows people with sensorineural hearing loss to recover hearing abilities especially speech recognition. Efficient encoding of temporal information in current CI signal processing strategy allows most of the CI users to achieve over 80% speech understanding in quite acoustic condition [1,2]. However, other aspects of auditory processing (such as speech understanding in noise, distinguishing speakers identity, gender, emotion) remain difficult for CI users to interpret, which is important for a better life [3,4,5].…”

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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The CCi-MOBILE Vocoder

Cited by 6 publications

References 0 publications

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

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

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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