Combination and Comparison of Sound Coding Strategies Using Cochlear Implant Simulation With Mandarin Speech

Huang, Enoch Hsin-Ho; Wu, C.; Lin, Hung-Ching

doi:10.1109/tnsre.2021.3128064

Cited by 9 publications

(8 citation statements)

References 74 publications

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“…Each subject listened to the simulated CI sound simultaneously played to both ears via Telephonics TDH-39P headphones in a sound isolation room. The experiment was carried out with NCU-CI, a MATLAB-based CI experiment platform with a graphical user interface in traditional Chinese [44], [48], [82]. In the practice session, each subject was instructed on the experimental procedure by being familiarized with sentences from List 16 of the TMHINT material presented at a fixed and comfortable sound level.…”

Section: E Subjective Evaluationmentioning

confidence: 99%

ElectrodeNet – A Deep Learning Based Sound Coding Strategy for Cochlear Implants

Huang¹,

Chao²,

Yu³

et al. 2023

Preprint

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ElectrodeNet, a deep learning based sound coding strategy for the cochlear implant (CI), is proposed to emulate the advanced combination encoder (ACE) strategy by replacing the conventional envelope detection using various artificial neural networks. The extended ElectrodeNet-CS strategy further incorporates the channel selection (CS). Network models of deep neural network (DNN), convolutional neural network (CNN), and long short-term memory (LSTM) were trained using the Fast Fourier Transformed bins and channel envelopes obtained from the processing of clean speech by the ACE strategy. Objective speech understanding using short-time objective intelligibility (STOI) and normalized covariance metric (NCM) was estimated for ElectrodeNet using CI simulations. Sentence recognition tests for vocoded Mandarin speech were conducted with normal-hearing listeners. DNN, CNN, and LSTM based ElectrodeNets exhibited strong correlations to ACE in objective and subjective scores using mean squared error (MSE), linear correlation coefficient (LCC) and Spearman’s rank correlation coefficient (SRCC). The ElectrodeNet-CS strategy was capable of producing N-of-M compatible electrode patterns using a modified DNN network to embed maxima selection, and to perform in similar or even slightly higher average in STOI and sentence recognition compared to ACE. The methods and findings demonstrated the feasibility and potential of using deep learning in CI coding strategy.

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Section: E Subjective Evaluationmentioning

confidence: 99%

ElectrodeNet – A Deep Learning Based Sound Coding Strategy for Cochlear Implants

Huang¹,

Chao²,

Yu³

et al. 2023

Preprint

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“…Among these predictors, STOI and NCM are two intrusive approaches which can effectively estimating speech intelligibility from a reference speech and a processed version of the speech. STOI has a high correlation with speech intelligibility and has been used in CI studies on speech enhancement and sound coding strategies [22], [25], [26], [41], [63], [65]- [69]. NCM is another frequently used indicator for evaluating speech comprehension in CI sound processing [22], [26], [63], [65], [69], including for vocoded Mandarin speech [61].…”

Section: Objective Evaluationmentioning

confidence: 99%

“…Each subject listened to the simulated CI sound simultaneously played to both ears via Telephonics TDH-39P headphones in a sound isolation room. The experiment was carried out with NCU-CI, a MATLAB-based CI experiment platform with a graphical user interface in traditional Chinese [41], [43], [73]. In the practice session, each subject was instructed about the experimental procedure by being familiarized with sentences from List 16 of the TMHINT material presented at a fixed and comfortable sound level.…”

Section: E Subjective Evaluationmentioning

confidence: 99%

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ElectrodeNet – A Deep Learning Based Sound Coding Strategy for Cochlear Implants

Huang¹,

Chao²,

Yu³

et al. 2022

Preprint

Self Cite

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ElectrodeNet, a deep-learning based sound coding strategy for the cochlear implant (CI), is proposed in this study. ElectrodeNet emulates the ACE strategy by replacing the conventional envelope detection using various artificial neural networks, and the extended ElectrodeNet-CS strategy further incorporates the channel selection (CS) in the network. Network models of deep neural network (DNN), convolutional neural network (CNN), and long short-term memory (LSTM) were trained using the Fast Fourier Transformed bins and electrode stimulation patterns from the processing of the ACE strategy for clean speech. Objective speech understanding using short-time objective intelligibility (STOI) and normalized covariance metric (NCM) was estimated for ElectrodeNet with the factors of network architecture, dataset language, and noise type using CI simulations. Subjective listening tests for vocoded Mandarin speech were conducted with normal-hearing listeners to measure sentence recognition scores. DNN, CNN, and LSTM based ElectrodeNets exhibited strong correlations to ACE in objective and subjective scores using mean squared error (MSE), linear correlation coefficient (LCC) and Spearman’s rank correlation coefficient (SRCC). The ElectrodeNet-CS strategy was capable of producing N-of-M compatible electrode patterns using a modified DNN network to embed maxima selection, and to perform in similar or even slightly above average in STOI and sentence recognition compared to ACE. The methods and findings in this study demonstrated the feasibility and potential of using deep learning in CI sound coding strategy.

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“…The FSP strategy is commercially available in the devices of MED-EL (Innsbruck, Austria). However, mixed results have been reported with these strategies [27]. Though some of these strategies were reported to have substantial improvement, pitch perception of CI users is still unsatisfactory compared to that of normal-hearing listeners [28].…”

mentioning

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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Combination and Comparison of Sound Coding Strategies Using Cochlear Implant Simulation With Mandarin Speech

Cited by 9 publications

References 74 publications

ElectrodeNet – A Deep Learning Based Sound Coding Strategy for Cochlear Implants

ElectrodeNet – A Deep Learning Based Sound Coding Strategy for Cochlear Implants

ElectrodeNet – A Deep Learning Based Sound Coding Strategy for Cochlear Implants

Pitch Perception With the Temporal Limits Encoder for Cochlear Implants

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