FPGA Implementation of Hearing Impaired Assistive Device for Hard to Hear Individuals

The Noise cancellation and suppression techniques have been developed and implemented in field-programmable gate array (FPGA) in this work. Hearing aids are primarily meant for improving hearing and speech comprehensions. Digital hearing aids score over their analog counterparts. This happens as digital hearing aids provide flexible gain besides facilitating feedback reduction and noise elimination. Recent advances in digital signal processors (DSP) and Microelectronics have led to the development of superior digital hearing aids. Many researchers have investigated several algorithms suitable for hearing aid application that demands low noise, feed-back cancellation, echo cancellation, etc., however the toughest challenge is the implementation. Furthermore, the additional constraints are power and area. The device must consume as minimum power as possible to support extended battery life and should be as small as possible for increased portability. In this work, we are using cross-channel suppression technique to remove the unwanted audio signals. The unwanted signals are suppressed using twotone suppression scheme. In this project, the speech signal is absorbed by microphone. This signal is then converted to digital using ADC. The digitized signal is processed using FPGA. Here in FPGA the speech signal is enhanced and amplified to the desired level. The processed speech signal is then converted into analog format using DAC and is given to speaker

Acoustic Feedback Noise Cancellation in Hearing Aids Using Adaptive Filter

To enhance speech intelligibility for people with hearing loss, hearing aids will amplify speech using gains derived from evidence-based prescriptive methods, in addition to other advanced signal processing mechanisms. While the evidence supports the use of hearing aid signal processing for speech intelligibility, these signal processing adjustments can also be detrimental to hearing aid sound quality, with poor hearing aid sound quality cited as a barrier to device adoption. In general, an uncontrolled environment may contain degradation components like background noise, speech from other speakers etc. along with required speech components. In this paper, we implement adaptive filtering design for acoustic feedback noise cancellation in hearing aids. The adaptive filter architecture has been designed using normalized least mean square algorithm. By using adaptive filters both filter input coefficients are changeable during run-time and reduce noise in hearing aids. The proposed design is implemented in matlab and the simulations shows that the proposed architecture produces good quality of speech, accuracy, maintain stable steady state. The proposed design is validated with parameters like Noise Distortion, Perceptual Evaluation of Speech Quality, Signal to Noise Ratio, and Speech Distortion. The feedback canceller is implemented in MATLAB 9.4 simulink version release name of R2018a is used for validation with Echo Return Loss Enhancement (ERLE). The ERLE of the NMLS is reduced when the filter order is increases. Around 10% of the power spectrum density (PSD) is less when compared with existing designs

Acoustic Feedback Noise Cancellation in Hearing Aids Using Adaptive Filter

To enhance speech intelligibility for people with hearing loss, hearing aids will amplify speech using gains derived from evidence-based prescriptive methods, in addition to other advanced signal processing mechanisms. While the evidence supports the use of hearing aid signal processing for speech intelligibility, these signal processing adjustments can also be detrimental to hearing aid sound quality, with poor hearing aid sound quality cited as a barrier to device adoption. In general, an uncontrolled environment may contain degradation components like background noise, speech from other speakers etc. along with required speech components. In this paper, we implement adaptive filtering design for acoustic feedback noise cancellation in hearing aids. The adaptive filter architecture has been designed using normalized least mean square algorithm. By using adaptive filters both filter input coefficients are changeable during run-time and reduce noise in hearing aids. The proposed design is implemented in matlab and the simulations shows that the proposed architecture produces good quality of speech, accuracy, maintain stable steady state. The proposed design is validated with parameters like Noise Distortion, Perceptual Evaluation of Speech Quality, Signal to Noise Ratio, and Speech Distortion. The feedback canceller is implemented in MATLAB 9.4 simulink version release name of R2018a is used for validation with Echo Return Loss Enhancement (ERLE). The ERLE of the NMLS is reduced when the filter order is increases. Around 10% of the power spectrum density (PSD) is less when compared with existing designs

First steps towards solving the café problem

Hearing loss, and assistive technologies to compensate for the loss, are becoming more and more regular. Hearing aids have improved the quality of life for many suffering from hearing loss but are still insufficient in some social settings. The café problem rises when there are a group of people talking in a relatively noisy environment where one person has hearing aids. Even with modern advancements, such as speech recognition and noise cancellation, people using hearing aids have difficulties differentiating the group's conversation from other noises. This thesis will provide the architecture, design, implementation and evaluation of a mobile application as a first step in creating a system that can counter this café problem. A critical factor in a system like this is to reduce the audio latency to a minimum. We investigate where latency is introduced in the system by creating an experimental setup and evaluating the system. We implement a prototype system and use the experimental setup to identify latency-inducing components. We discuss how this latency can be reduced and bring forward future steps that must be made in completing the system

Low noise amplifier design and noise cancellation for wireless hearing aids

Master'sMASTER OF ENGINEERIN

Frequency Controlled Noise Cancellation for Audio and Hearing Purposes

Methods for hearing aids sought to compensate for loss in hearing by amplifying signals of interest in the audio band. In real-world, audio signals are prone to outdoor noise which can be destructive for hearing aid.  Eliminating interfering noise at high speed and low power consumption became a target for recent researches. Modern hearing compensation technologies use digital signal processing which requires minimum implementation costs to reduce power consumption, as well as avoiding delay in real time processing. In this paper, frequency controlled noise cancellation (FCNC) strategy for hearing aid and audio communication is developed with low complexity and least time delay. The contribution of the current work is made by offering a method that is capable of removing inherent distortion due filter-bank insertion and assigning adaptive filtering to a particular sub-band to remove external noise. The performance of the proposed FCNC was examined under frequency-limited noise, which corrupts particular parts of the audio spectrum. Results showed that the FCNC renders noise-immune audio signals with minimal number of computations and least delay. Mean square error (MSE) plots of the proposed FCNC method reached below -30 dB compared to -25 dB using conventional sub-band method and to -10 dB using standard full-band noise canceller. The proposed FCNC approach gave the lowest number of computations compared to other methods with a total of 346 computations per sample compared to 860 and 512 by conventional sub-band and full-band methods respectively. The time delay using FCNC is the least compared to the other methods

Noise injection for feedback cancellation with linear prediction

ABSTRACT Feedback oscillation is one of the major issues with hearing aids. An efficient way of feedback suppression is feedback cancellation, which uses an adaptive filter to estimate the feedback path. However, the feedback canceller suffers from the bias problem in the feedback path estimate. The recent progress suggests a feedback canceller with linear prediction of the desired signal in order to eliminate the bias when certain conditions are met. However, the bias still remains in many situations, for example when the input signal is voiced speech. Noise injection is investigated in this paper to help reduce the bias further and improve the system performance. Two nearly inaudible noises are proposed: a masking noise, which is tailored to the hearing-aid application, and a linear prediction based noise, which is especially efficient for feedback cancellation with linear prediction. Simulation results show that noise injection can further reduce the feedback estimation error by 1-4 dB and/or increase the stable gain by 3-4 dB, depending on the characteristics of the input signal

Aspects of Hearing Aid Fitting Procedures

Sensorineural hearing loss is a common and chronic disorder that affects almost ten percent of the world population. In the Netherlands, it is also the major disorder in the working population [NCvB, 2008]. Hearing loss leads to restriction in the interaction with others and withdrawal from participation in (social) activities. Due to the size of the problem and the vast impact on the function, hearing rehabilitation is an important issue. Although hearing rehabilitation focuses on many more aspects such as learning of communication strategies and adaptation to the acoustical environment, hearing aid fitting is one of its first essential steps. Hearing aids have to amplify sound to a level above the hearing threshold to utilize the residual hearing capacity of the ear as much as possible. In the 20th century, a number of technological advances have taken place in amplification devices. These started from nonelectronic ear horns that were replaced by electronic hearing aids. Amplification was initially achieved by analogue circuits, while from the 1990s digital signal processors have entered the market. An enormously wide variety of hearing aid models has become available since [Bentler & Duve, 2000]. Aside from differences that have to do with the sound that is being produced, hearing aids can be classified with respect to type. While the technological development started with body-worn hearing aids, we nowadays distinguish behind-the-ear (BTE), in-the-ear (ITE) and hearing aids that fit partly or completely in the ear canal (CIC). These types are available in a wide variety of models, colours and sizes and are of various brands. A classical feature is the telecoil for use with induction loops. Options that are available for modern hearing aids are remote controls, infrared and fm-receivers, the use of multiple programs and water resistant housings. Last but not least, every hearing aid has its own price. It is obvious that the search for the hearing aid that is most suitable for the individual patient can be regarded as a real challenge. It is not only based on measures like speech perception but may also be determined by listening comfort, wearing comfort and functionality. This is all devised during the selection phase of a hearing aid fitting. Aside from differences in the exterior and the above-mentioned features, hearing aids can be distinguished with respect to the sound that they produce. For a long time the amount of amplification and the frequency characteristic were the main issues. Later on, electronic compression circuits were added to limit the maximum output and/or gain of the hearing aid. More recently developed features are feedback reduction, noise cancellation and the use of directional microphones. To adjust the various controls of the hearing aid in order to optimally compensate for the affected cochlea is a challenge on its own. This is done during the adjustment phase of a hearing aid fitting. Procedures for hearing aid fitting have been invented in parallel with the development of hearing aid technology

A binaural grouping model for predicting speech intelligibility in multitalker environments

Spatially separating speech maskers from target speech often leads to a large intelligibility improvement. Modeling this phenomenon has long been of interest to binaural-hearing researchers for uncovering brain mechanisms and for improving signal-processing algorithms in hearing-assistive devices. Much of the previous binaural modeling work focused on the unmasking enabled by binaural cues at the periphery, and little quantitative modeling has been directed toward the grouping or source-separation benefits of binaural processing. In this article, we propose a binaural model that focuses on grouping, specifically on the selection of time-frequency units that are dominated by signals from the direction of the target. The proposed model uses Equalization-Cancellation (EC) processing with a binary decision rule to estimate a time-frequency binary mask. EC processing is carried out to cancel the target signal and the energy change between the EC input and output is used as a feature that reflects target dominance in each time-frequency unit. The processing in the proposed model requires little computational resources and is straightforward to implement. In combination with the Coherence-based Speech Intelligibility Index, the model is applied to predict the speech intelligibility data measured by Marrone et al. The predicted speech reception threshold matches the pattern of the measured data well, even though the predicted intelligibility improvements relative to the colocated condition are larger than some of the measured data, which may reflect the lack of internal noise in this initial version of the model.R01 DC000100 - NIDCD NIH HH