An evaluation framework for research platforms to advance cochlear implant/hearing aid technology: A case study with CCi-MOBILE

Shekar, Ram C. M. C.; Hansen, John H. L.

doi:10.1121/10.0002989

Cited by 8 publications

(5 citation statements)

References 64 publications

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“…The real-time TLE and ACE strategies were implemented in MATLAB and presented to CI participants using a CCi-MOBILE research processor developed by University of Texas at Dallas [34], [35]. The output volume and microphone sensitivities were adjusted to compensate for potential loudness differences induced by modulator differences.…”

Section: B Participantsmentioning

confidence: 99%

Pitch Perception With the Temporal Limits Encoder for Cochlear Implants

Zhou

Kan

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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The temporal-limits-encoder (TLE) strategy has been proposed to enhance the representation of temporal fine structure (TFS) in cochlear implants (CIs), which is vital for many aspects of sound perception but is typically discarded by most modern CI strategies. TLE works by computing an envelope modulator that is within the temporal pitch limits of CI electric hearing. This paper examines the TFS information encoded by TLE and evaluates the salience and usefulness of this information in CI users. Two experiments were conducted to compare pitch perception performance of TLE versus the widely-used Advanced Combinational Encoder (ACE) strategy. Experiment 1 investigated whether TLE processing improved pitch discrimination compared to ACE. Experiment 2 parametrically examined the effect of changing the lower frequency limit of the TLE modulator on pitch ranking. In both experiments, F0 difference limens were measured with synthetic harmonic complex tones using an adaptive procedure. Signal analysis of the outputs of TLE and ACE strategies showed that TLE introduces important temporal pitch cues that are not available with ACE. Results showed an improvement in pitch discrimination with TLE when the acoustic input had a lower F0 frequency. No significant effect of lower frequency limit was observed for pitch ranking, though a lower limit did tend to provide better outcomes. These results suggest that the envelope modulation introduced by TLE can improve pitch perception for CI listeners.

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Section: B Participantsmentioning

confidence: 99%

Pitch Perception With the Temporal Limits Encoder for Cochlear Implants

Zhou

Kan

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…The pre-emphasis is part of the ACE processing strategy to increase the likelihood of selecting high-frequency channels by emphasizing high-frequencies in the signal. In the toolbox provided by Cochlear, pre-emphasis is approximated with a high-pass one-order Chebyshev filter with cutoff at 1000Hz (Shekar and Hansen, 2021). Pre-emphasis filter is not required when the audio signal is recorded by the CCI-Mobile microphone.…”

Section: Pre-processingmentioning

confidence: 99%

Capabilities of the CCi-Mobile Cochlear Implant Research Platform for Real-Time Sound Coding

Azadpour

Saba

Hansen

et al. 2022

Preprint

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One important obstacle to optimizing fitting and sound coding for auditory implants is lack of flexible, powerful and portable platforms that can be used in real-world listening environments by implanted patients. The clinical processors and the typically available research tools either do not have sufficient computational power and flexibility or are not portable. In response to this need, the Center for Robust Speech Systems (CRSS) at the University of Texas at Dallas has developed CCI-Mobile, in collaboration with the Laboratory for Translational Auditory Research at New York University School of Medicine and the Binaural Hearing and Speech Laboratory at the University of Wisconsin-Madison. The CCI-Mobile platform provides unique flexibility to implement a variety of real-time sound coding algorithms in real-world environments, including algorithms that require synchronized binaural stimulation. In this paper, we will describe the overall architecture of the CCI-Mobile platform and provide practical considerations for designing real-time sound coding algorithms with this platform. CCI-Mobile is under development and future generations may provide further functionality, beyond what is described in this paper.

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“…Figure 5 shows the electrodograms extracted from speech signals generated from the same speech utterance with different noisy processing. The electrodograms were generated by processing the acoustical signal with the CCi-Mobile, a CI research development platform developed by CI-Lab at the University of Texas at Dallas ( 35 ). Figures 5A,B are for clean speech, and that corrupt by SSN at 0 dB SNR ( Figure 5C ) is for the denoised speech with MSE-MASK DL SE, and ( Figures 5D–L ) are for the denoised speeches with WL-MASK DL SEs with α = 0.1, 0.2, ⋯, 0.9, respectively.…”

Section: Experimental Settingmentioning

confidence: 99%

Deep Learning-Based Speech Enhancement With a Loss Trading Off the Speech Distortion and the Noise Residue for Cochlear Implants

2021

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The cochlea plays a key role in the transmission from acoustic vibration to neural stimulation upon which the brain perceives the sound. A cochlear implant (CI) is an auditory prosthesis to replace the damaged cochlear hair cells to achieve acoustic-to-neural conversion. However, the CI is a very coarse bionic imitation of the normal cochlea. The highly resolved time-frequency-intensity information transmitted by the normal cochlea, which is vital to high-quality auditory perception such as speech perception in challenging environments, cannot be guaranteed by CIs. Although CI recipients with state-of-the-art commercial CI devices achieve good speech perception in quiet backgrounds, they usually suffer from poor speech perception in noisy environments. Therefore, noise suppression or speech enhancement (SE) is one of the most important technologies for CI. In this study, we introduce recent progress in deep learning (DL), mostly neural networks (NN)-based SE front ends to CI, and discuss how the hearing properties of the CI recipients could be utilized to optimize the DL-based SE. In particular, different loss functions are introduced to supervise the NN training, and a set of objective and subjective experiments is presented. Results verify that the CI recipients are more sensitive to the residual noise than the SE-induced speech distortion, which has been common knowledge in CI research. Furthermore, speech reception threshold (SRT) in noise tests demonstrates that the intelligibility of the denoised speech can be significantly improved when the NN is trained with a loss function bias to more noise suppression than that with equal attention on noise residue and speech distortion.

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An evaluation framework for research platforms to advance cochlear implant/hearing aid technology: A case study with CCi-MOBILE

Cited by 8 publications

References 64 publications

Pitch Perception With the Temporal Limits Encoder for Cochlear Implants

Pitch Perception With the Temporal Limits Encoder for Cochlear Implants

Capabilities of the CCi-Mobile Cochlear Implant Research Platform for Real-Time Sound Coding

Deep Learning-Based Speech Enhancement With a Loss Trading Off the Speech Distortion and the Noise Residue for Cochlear Implants

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