Renata Coelho Borges

Cochlear implants (CI) are implantable electronic prostheses that aim to restore communication in people with severe to profound hearing loss. This is achieved by transforming the acoustic signals into electrical stimuli and applying them directly to the cochlea through a set of electrodes. Despite its effectiveness under optimal environmental conditions, its performance is severely degraded in the presence of noise. This work proposes an envelope-based minimum-variance distortionless response (MVDR) approach for multichannel noise reduction in CI applications. The original constraint in the conventional time-domain MVDR beamformer is modified to ensure that the second-order statistical moments of the processed and original speech-only envelopes are equivalent. Assuming strict computational limitations, as those found in commercial multielectrode systems, a low computational-cost particular case of the proposed method results in a power-based MVDR that has a semianalytical solution. The resulting algorithm provides a fast calculation method for its optimal coefficients without losing significant performance compared to its general form. Results obtained from computational simulations and objective criteria indicate higher intelligibility levels achieved by the proposed methods compared to the conventional time-domain MVDR beamformer. This evidence is corroborated by psychoacoustic experiments with ten normal-hearing volunteers listening to vocoded speech, and one CI user employing a research interface.

The increase in life expectancy, according to the World Health Organization, is a fact, and with it rises the incidence of age-related neurodegenerative diseases. The most recurrent symptoms are those associated with tremors resulting from Parkinson’s disease (PD) or essential tremors (ETs). The main alternatives for the treatment of these patients are medication and surgical intervention, which sometimes have restrictions and side effects. Through computer simulations in Matlab software, this work investigates the performance of adaptive algorithms based on least mean squares (LMS) to suppress tremors in upper limbs, especially in the hands. The signals resulting from pathological hand tremors, related to PD, present components at frequencies that vary between 3 Hz and 6 Hz, with the more significant energy present in the fundamental and second harmonics, while physiological hand tremors, referred to ET, vary between 4 Hz and 12 Hz. We simulated and used these signals as reference signals in adaptive algorithms, filtered-x least mean square (Fx-LMS), filtered-x normalized least mean square (Fx-NLMS), and a hybrid Fx-LMS–NLMS purpose. Our results showed that the vibration control provided by the Fx-LMS–LMS algorithm is the most suitable for physiological tremors. For pathological tremors, we used a proposed algorithm with a filtered sinusoidal input signal, Fsinx-LMS, which presented the best results in this specific case.

The increase in life expectancy, according to the World Health Organization, is a fact, and with it rises the incidence of age-related neurodegenerative diseases. The most recurrent symptoms are those associated with tremors resulting from Parkinson's Disease (PD) or Essential Tremors (ET). The main alternatives for the treatment of these patients are medication and surgical intervention, which sometimes have restrictions and side effects. Through computer simulations in Matlab software, this work investigates the performance of adaptive algorithms based on least mean squares (LMS) to suppress tremors in upper limbs, especially in the hands. The signals resulting from pathological hand tremors, related to PD, present components at frequencies that vary between 3~Hz and 6~Hz, with the more significant energy present in the fundamental and second harmonics, while physiological hand tremors, referred to as ET, vary between 4~Hz and 12~Hz. We simulated and used these signals as reference signals in adaptive algorithms, Filtered-x Least Mean Square (Fx-LMS), Filtered-x Normalized Least Mean Square (Fx-NLMS), and a hybrid Fx-LMS\&NLMS purpose. Our results showed that the vibration control provided by the Fx-LMS\&LMS algorithm is the most suitable for physiological tremors. For pathological tremors, we have used a proposed algorithm with a filtered sinusoidal input signal, Fsinx-LMS, which presented the best results in this specific case.

The occlusion effect is a common complaint from users of hearing aids with narrow or unvented earmolds. This phenomenon makes the user hear his own voice muffled. In the scientific literature, fixed and adaptive controllers have been proposed for occlusion effect reduction. This work proposes a cepstral method to estimate the stable optimal solution for feedforward occlusion cancellation and a fixed controller that utilizes this estimate to reduce the occlusion effect in hearing aids. The cepstral method operates on a feedback structure at a calibration process. Simulations have shown that the performance of the cepstral method improves as the length of the signal uttered by the hearing aid user increases, resulting in estimates with average normalized misalignment less than -19 and -34 dB for signals lasting 1.5 and 5 s. The estimates are significantly more accurate below 500 Hz, which is the frequency range common to the occlusion effect. In addition, results have pointed out that the controller attenuated the occlusion effect, averagely decreasing by 0.17 dB the distortion power and increasing by 0.13 the objective perceptual quality MOS-LQO score.