A subjective and objective evaluation of a codec for the electrical stimulation patterns of cochlear implants

Zhang

IEEE Trans. Biomed. Eng.

2023

Cochlear implants (CIs) have proven to be successful at restoring the sensation of hearing in people who suffer from profound sensorineural hearing loss. CI users generally achieve good speech understanding in quiet acoustic conditions. However, their ability to understand speech degrades drastically when background interfering noise is present. To address this problem, current CI systems are delivered with front-end speech enhancement modules that can aid the listener in noisy environments. However, these only perform well under certain noisy conditions, leaving quite some room for improvement in more challenging circumstances. In this work, we propose replacing the CI sound coding strategy with a deep neural network (DNN) that performs end-to-end speech denoising by taking the raw audio as input and providing a denoised electrodogram, i.e., the electrical stimulation patterns applied to the electrodes across time. We specifically introduce a DNN that emulates a common CI sound coding strategy, the advanced combination encoder (ACE). We refer to the proposed algorithm as 'Deep ACE'. Deep ACE is designed not only to accurately code the acoustic signals in the same way that ACE would but also to automatically remove unwanted interfering noises, without sacrificing processing latency. The model was optimized using a CI-specific loss function and evaluated using objective measures as well as listening tests in CI participants. Results show that, based on objective measures, the proposed model achieved higher scores when compared to the baseline algorithms. Also, the proposed deep learning-based sound coding strategy gave eight CI users the highest speech intelligibility results.

Section: Audio Materialsmentioning

confidence: 99%

Section: Audio Materialsmentioning

confidence: 99%

Section: Audio Materialsmentioning

confidence: 99%

See 1 more Smart Citation

A Deep Denoising Sound Coding Strategy for Cochlear Implants

Zhang

IEEE Trans. Biomed. Eng.

2023

“…To estimate the speech intelligibility performance expected from each of the algorithms, the VSTOI score [35], [36], [38] was used. This metric relies directly on STOI [34], which is modeled based on normal hearing speech performance.…”

Section: Methodsmentioning

confidence: 99%

A Deep Denoising Sound Coding Strategy for Cochlear Implants

Zhang

2022

Preprint

Self Cite

Cochlear implants (CIs) have proven to be successful at restoring the sensation of hearing in people who suffer from profound sensorineural hearing loss. CI users generally achieve good speech understanding in quiet acoustic conditions. However, their hearing ability degrades drastically in noisy backgrounds. To address this problem, current CI systems are delivered with front-end speech enhancement processors that can be beneficial for the listener, however, these perform well only in certain noisy environments, leaving quite some room for improvement in more challenging conditions. In this work, we propose substituting the CI sound coding strategy with a deep neural network (DNN) that performs end-to-end processing by taking the raw audio as input and providing a denoised electrodogram, i.e., the electrical stimulation patterns that are applied to the electrodes across time. We specifically design a DNN to emulate a common CI sound coding strategy; the advanced combination encoder (ACE). The DNN is designed not only to faithfully emulate the coding of acoustic signals that ACE would perform but also to remove unwanted interfering noises, when present, without sacrificing processing latency. The models were optimized using CI-specific loss functions and evaluated using objective instrumental measures and listening tests in CI participants. Results show that the proposed models achieve higher signal-to-noise ratio improvement and objective intelligibility scores when compared to the baseline models. Furthermore, the proposed deep learning-based sound coding strategy improved speech intelligibility in eight CI users.

“…This new approach is designed to completely bypass the CI sound coding strategy, providing the listener with signals as natural as the original sound coding strategy would, while performing speech denoising. This end-to-end CI strategy may outperform a front-end DNN in terms of speech enhancement, as the estimated electrodograms have a lower dynamic range, less amplitude resolution, lack phase information, and are more redundant than raw audio signals [18], and therefore, may be easier to model.…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Deep Learning Speech Coding and Denoising Strategy for Cochlear Implants

2021

Preprint

Self Cite

Cochlear implant (CI) users struggle to understand speech in noisy conditions. In this work, we propose an end-to-end speech coding and denoising sound coding strategy that estimates the electrodograms from the raw audio captured by the microphone. We compared this approach to a classic Wiener filter and TasNet to assess its potential benefits in the context of electric hearing. The performance of the network is assessed by means of noise reduction performance (signal-to-noise-ratio improvement) and objective speech intelligibility measures. Furthermore, speech intelligibility was measured in 5 CI users to assess the potential benefits of each of the investigated algorithms. Results suggest that the speech performance of the tested group seemed to be equally good using our method compared to the front-end speech enhancement algorithm.