A sound processor for cochlear implant using a simple dual path nonlinear model of basilar membrane

Recent Advances in Cochlear Implant Electrode Array Design Parameters

Cochlear implants are neural implant devices that aim to restore hearing in patients with severe sensorineural hearing impairment. Here, the main goal is to successfully place the electrode array in the cochlea to stimulate the auditory nerves through bypassing damaged hair cells. Several electrode and electrode array parameters affect the success of this technique, but, undoubtedly, the most important one is related to electrodes, which are used for nerve stimulation. In this paper, we provide a comprehensive resource on the electrodes currently being used in cochlear implant devices. Electrode materials, shape, and the effect of spacing between electrodes on the stimulation, stiffness, and flexibility of electrode-carrying arrays are discussed. The use of sensors and the electrical, mechanical, and electrochemical properties of electrode arrays are examined. A large library of preferred electrodes is reviewed, and recent progress in electrode design parameters is analyzed. Finally, the limitations and challenges of the current technology are discussed along with a proposal of future directions in the field.

The design and validation of a hybrid digital-signal-processing plug-in for traditional cochlear implant speech processors

BACKGROUND AND OBJECTIVE

Cochlear implants (CIs) are electronic devices restoring partial hearing to deaf individuals with profound hearing loss. In this paper, a new plug-in for traditional IIR filter-banks (FBs) is presented for cochlear implants based on wavelet neural networks (WNNs). Having provided such a plug-in for commercially available CIs, it is possible not only to use available hardware in the market but also to optimize their performance compared with the-state-of-the-art.

METHODS

An online database of Dutch diphone perception was used in our study. The weights of the WNNs were tuned using particle swarm optimization (PSO) on a training set (speech-shaped noise (SSN) of 2 dB SNR), while its performance was assessed on a test set in terms of objective and composite measures in the hold-out validation framework. The cost function was defined based on the combination of mean square error (MSE), short‑time objective intelligibility (STOI) criteria on the training set. Variety of performance indices were used including segmental signal- to -noise ratio (SNRseg), MSE, STOI, log-likelihood ratio (LLR), weighted spectral slope (WSS), and composite measures C_sig,C_bak and C_ovl. Meanwhile, the following CI speech processing techniques were used for comparison: traditional FBs, dual resonance nonlinear (DRNL) and simple dual path nonlinear (SPDN) models.

RESULTS

The average SNRseg, MSE, and LLR values for the WNN in the entire data set were 2.496 ± 2.794, 0.086 ± 0.025 and 2.323 ± 0.281, respectively. The proposed method significantly improved MSE, SNR, SNRseg, LLR, C_sig C_bak and C_ovl compared with the other three methods (repeated-measures analysis of variance (ANOVA); P < 0.05). The average running time of the proposed algorithm (written in Matlab R2013a) on the training and test sets for each consonant or vowel on an Intel dual-core 2.10 GHz CPU with 2GB of RAM was 9.91 ± 0.87 (s) and 0.19 ± 0.01 (s), respectively.

CONCLUSIONS

The proposed algorithm is accurate and precise and is thus a promising new plug-in for traditional CIs. Although the tuned algorithm is relatively fast, it is necessary to use efficient vectorized implementations for real-time CI speech signal processing.

An Undecimated Wavelet-based Method for Cochlear Implant Speech Processing

A cochlear implant is an implanted electronic device used to provide a sensation of hearing to a person who is hard of hearing. The cochlear implant is often referred to as a bionic ear. This paper presents an undecimated wavelet-based speech coding strategy for cochlear implants, which gives a novel speech processing strategy. The undecimated wavelet packet transform (UWPT) is computed like the wavelet packet transform except that it does not down-sample the output at each level. The speech data used for the current study consists of 30 consonants, sampled at 16 kbps. The performance of our proposed UWPT method was compared to that of infinite impulse response (IIR) filter in terms of mean opinion score (MOS), short-time objective intelligibility (STOI) measure and segmental signal-to-noise ratio (SNR). Undecimated wavelet had better segmental SNR in about 96% of the input speech data. The MOS of the proposed method was twice in comparison with that of the IIR filter-bank. The statistical analysis revealed that the UWT-based N-of-M strategy significantly improved the MOS, STOI and segmental SNR (P < 0.001) compared with what obtained with the IIR filter-bank based strategies. The advantage of UWPT is that it is shift-invariant which gives a dense approximation to continuous wavelet transform. Thus, the information loss is minimal and that is why the UWPT performance was better than that of traditional filter-bank strategies in speech recognition tests. Results showed that the UWPT could be a promising method for speech coding in cochlear implants, although its computational complexity is higher than that of traditional filter-banks.