Hearing Aid Applications Research Articles

Low power area efficient adaptive FIR filter for hearing aids using distributed arithmetic architecture

In this paper, we propose a low complex architectural design for hearing aid applications. In this, we recast the hearing aid using distributed arithmetic (DA), which enables the implementation of hearing aid without multipliers. The design is based on the distributed arithmetic based formulation of it. It is further shown that high order filters, which are required to implement high-speed hearing aid can be realized using only look-up-tables and shift-accumulate operations. A novel approach was proposed to replace the decimation filter of a hearing aid using multiplier less architecture with a single DA unit. By proper initialization, it is shown that low complexity hearing aid architecture can be obtained. The proposed distributed arithmetic architecture is implemented in ASIC SAED 90 nm technology. The application of hearing aid is implemented in Matlab Simulink and Xilinx system generator tool. The obtained results show $$20\%$$ less area delay product and $$40\%$$ less power delay product when compared with the existing architecture.

TIMELY APPLICATION OF HEARING AIDS HELPS PRESERVE SPEECH DISCRIMINATION ABILITY

Background Hearing damage has been related to certain brain conditions, particularly cognitive impairment. Hearing aids are commonly prescribed to compensate for hearing loss. In the current study, the time from when a hearing aid was first used by a patient was studied in terms of their speech discrimination ability in comparison with matched individuals who used no hearing aids. Material and methods Some 204 patients were enrolled and classified into two groups: 102 patients willing to use a hearing aid as soon as their hearing loss was diagnosed (Aided Group) and 102 patients who were also diagnosed with hearing loss but did not adopt hearing aids (Unaided group). The mean hearing threshold (HT) at octave intervals from 125 to 8000 Hz, speech reception threshold (SRT), and speech discrimination score (SDS) were compared between the two groups of patients at 18 and 36 months. Results According to measurements 36 months after a patient’s first attendance, patients who did not use a hearing aid immediately had significantly lower SDS scores compared to patients who decided to use hearing aids early on. This clearly demonstrates that timely application of hearing aids can prevent the loss of speech perception capabilities. Conclusions It is suggested that the golden time for hearing aid application is important. Hearing aids worn during the early stages of hearing loss can lead to improved speech discrimination ability. If there is hearing loss while discrimination is good (80% or higher), a hearing aid will be very useful. However, if discrimination is poor the results will not be as good.

Enhancement of Speech Signal Using Improved FA-ANFIS Classifier for Hearing AIDS Application

Enhancement of Speech Signal Using Improved FA-ANFIS Classifier for Hearing AIDS Application

Enhancement of Speech Intelligibility using Binary Mask Based on channel selection criteria

Most of the existing noise reduction algorithms used in hearing aid applications apply a gain function in order to reduce the noise intervention. In the present paper, we study the effect of the two types of speech distortions introduced by the gain functions. If these distortions are properly controlled large gains in intelligibility can be obtained. The sentences were corrupted by various kinds of noises i.e. babble noise, car noise, helicopter noise and random noise and processed through a noise-reduction algorithm. Subjective tests were conducted with normal hearing listeners by presenting the processed speech with controlled distortions. The method proposed by Kim et al uses the wiener filter. Here in this paper, we have used the parametric wiener filter. The experimental results clearly indicated improvement in intelligibility at 0dB, -5dB, +5dB and 10dB input signal-to-noise (SNR) values in short-time objective intelligibility (STOI) and Segmental signal-to-noise ratio (SSNR) objective measures.

Accurate estimation of decay coefficients for dynamic range compressors in hearing aids and a hardware level comparison of different architectures

Dynamic Range Compression (DRC) algorithm helps to protect the residual hearing ability of hearing aid users by compressing the signal levels which go above a particular threshold. This paper addresses two different aspects of DRC for hearing aid applications. In the first part, methods to estimate the decay coefficients corresponding to the required time constants for a feed-forward DRC architecture accurately, to meet the hearing aid specifications are proposed. The effect of compression on the attack and release time parameters are compensated with the new formula. The hardware implementation of four different DRC architectures is explained in the second part of the paper. The estimated decay coefficients for a test signal were used for the corresponding hardware implementations and verified the validity of proposed algorithmic modifications. The architectures were implemented using UMC 65 nm standard cell libraries and the power and error results were compared. The proposed methods to estimate the decay coefficients for both attack and release phases show close to 0 dB error from expected output values, while conventional methods are not meeting the specifications. Hardware implementation shows that there is not much improvement in power performance, between a lower resolution Look-Up Table (LUT) based logarithm implementation and a higher resolution one. From the results, we propose using the absolute level detector based DRC with higher resolution logarithm without a gain smoothing stage at the output for lowest power consumption and better approximation error performance.

Cognitive-Driven Binaural Beamforming Using EEG-Based Auditory Attention Decoding

Identifying the target speaker in hearing aid applications is an essential ingredient to improve speech intelligibility. Recently, a least-squares-based auditory attention decoding (AAD) method has been proposed to identify the target speaker from single-trial EEG recordings in an acoustic scenario with two competing speakers. Aiming at enhancing the target speaker and suppressing the interfering speaker and ambient noise, in this article, we propose a cognitive-driven speech enhancement system, consisting of a binaural beamformer which is steered based on AAD and estimated relative transfer function (RTF) vectors, which require estimates of the direction-of-arrivals (DOAs) of both speakers. For binaural beamforming and to generate reference signals for AAD, we consider either minimum-variance-distortionless-response (MVDR) beamformers or linearly-constrained-minimum-variance (LCMV) beamformers. Contrary to the binaural MVDR beamformer, the binaural LCMV beamformer allows to preserve the spatial impression of the acoustic scene and to control the suppression of the interfering speaker, which is important when intending to switch attention between speakers. The speech enhancement performance of the proposed system is evaluated in terms of the binaural signal-to-interference-plus-noise ratio ( $\text {SINR}$ ) improvement in anechoic and reverberant conditions. Furthermore, we investigate the impact of RTF and DOA estimation errors and AAD errors on the speech enhancement performance. The experimental results show that the proposed system using LCMV beamformers yields a larger decoding performance and binaural $\text {SINR}$ improvement compared to using MVDR beamformers.

Online Spectrogram Inversion for Low-Latency Audio Source Separation

Audio source separation is usually achieved by estimating the short-time Fourier transform (STFT) magnitude of each source, and then applying a spectrogram inversion algorithm to retrieve time-domain signals. In particular, the multiple input spectrogram inversion (MISI) algorithm has been exploited successfully in several recent works. However, this algorithm suffers from two drawbacks, which we address in this paper. First, it has originally been introduced in a heuristic fashion: we propose here a rigorous optimization framework in which MISI is derived, thus proving the convergence of this algorithm. Besides, while MISI operates offline, we propose here an online version of MISI called oMISI, which is suitable for low-latency source separation, an important requirement for e.g., hearing aids applications. oMISI also allows one to use alternative phase initialization schemes exploiting the temporal structure of audio signals. Experiments conducted on a speech separation task show that oMISI performs as well as its offline counterpart, thus demonstrating its potential for real-time source separation.

Worst-Case-Optimization Robust-MVDR Beamformer for Stereo Noise Reduction in Hearing Aids

This paper presents a robust beamformer for stereo noise reduction in hearing aid applications. The worst-case optimization method was applied to the binaural minimum-variance distortionless-response (BMVDR) beamformer, for providing robustness against parameter estimation inaccuracies. This property is essential in real-world applications, since it is well known that steering-vector and noise-correlation estimation errors may significantly degrade the BMVDR performance. It is shown that its control parameters can be designed as a function of the noisy-speech interaural-level-difference estimate. Simulation experiments for synthetic and real-world noise, as well as for a range of signal to interference ratios (SIR) were performed. For a particular set of fixed control parameters, it is shown that the mean performance of the conventional BMVDR may be improved by 0.76 wideband perceptual evaluation of speech quality (WPESQ). The proposed method is especially effective for input SIRs higher than 10 dB, which is a crucial range for hearing aid users.

Design and Integration of Alert Signal Detector and Separator for Hearing Aid Applications

Alert signals like sirens and home alarms are important as they warn people of precarious situations. This work presents the detection and separation of these acoustically important alert signals, not to be attenuated as noise, to assist the hearing impaired listeners. The proposed method is based on convolutional neural network (CNN) and convolutional-recurrent neural network (CRNN). The developed method consists of two blocks, the detector block, and the separator block. The entire setup is integrated with speech enhancement (SE) algorithms, and before the compression stage, used in a hearing aid device (HAD) signal processing pipeline. The detector recognizes the presence of alert signal in various noisy environments. The separator block separates the alert signal from the mixture of noisy signals before passing it through SE to ensure minimal or no attenuation of the alert signal. It is implemented on a smartphone as an application that seamlessly works with HADs in real-time. This smartphone assistive setup allows the hearing aid users to know the presence of the alert sounds even when these are out of sight. The algorithm is computationally efficient with a low processing delay. The key contribution of this paper includes the development and integration of alert signal separator block with SE and the realization of the entire setup on a smartphone in real-time. The proposed method is compared with several state-of-the-art techniques through objective measures in various noisy conditions. The experimental analysis demonstrates the effectiveness and practical usefulness of the developed setup in real-world noisy scenarios.

High speed low area OBC DA based decimation filter for hearing aids application

This brief presents a decimation filter for hearing aid application using distributed arithmetic (DA) approach. In this paper, we propose a reconfigurable offset-binary code (OBC) DA based finite impulse response (FIR) filter with a shared look-up table (LUT) updating scheme. The size of the LUTs in DA increases exponentially with the order of filters. Shared LUT based DA structure is a solution to reduce this large memory requirement for higher order filters. The proposed shared LUT updating scheme uses LUT partitioning in which coefficients are spilt into small length vectors and it ensures a drastic reduction in the size of LUTs. The proposed DA filter is synthesized on CMOS 90 nm technology using Synapsis ASIC Design Compiler. The proposed design achieves high speed at a reduced area-delay product (ADP) when compared with recent designs. The proposed architecture is implemented and tested on Virtex 5vsx95-1ff1136 FPGA and the results show that the proposed design involves less number of slices and offers high speed than existing designs. A three-stage decimation filter of hearing aids is designed with the proposed FIR filter and is implemented on the target device by Matlab simulink and Xilinx system generator.

Perceptual and Objective Assessment of Envelope Enhancement for Children With Auditory Processing Disorder.

This paper evaluated the performance of an envelope enhancement (EE) algorithm subjectively by children with auditory processing disorder (APD), and objectively through computational models. Speech intelligibility data was collected from children with APD, for unprocessed and envelope-enhanced speech in the presence of stationary and non-stationary background noise at different signal to noise ratios (SNRs), both with and without noise reduction (NR) algorithms as a front-end to the EE algorithm. Furthermore, intrusive and non-intrusive objective speech intelligibility metrics were derived to predict the perceptual impact of this EE algorithm. Subjective data for stationary noise conditions revealed that the combination of NR and EE algorithms significantly improved the speech intelligibility scores at poor SNRs. In contrast, the same combination was ineffective in improving speech intelligibility in non-stationary noise conditions. Taken together, subjective results suggest that exaggerating the envelope cues improves speech identification scores for children with APD. However, the benefit obtained varies depending upon the type and level of the background noise. Both intrusive and non-intrusive objective speech intelligibility estimators exhibited good correlation with the subjective data, with the intrusive metric demonstrating better generalization capabilities. Implications of these results for hearing aid applications for children with APD is discussed.

A Study of the Security System for the Current Hearing Aid Fitting and Application Programs

A Study of the Security System for the Current Hearing Aid Fitting and Application Programs

Smartphone and algorithms: A platform for hearing study

Popular smartphones are portable devices with useful features such as powerful processor, microphones, speakers, low audio input/output delay, wire and wireless communication capability, graphical user interface (GUI) and easy-to-use touch screen. The smartphone features are improved every year. These features make smartphone a powerful stand-alone device for the development and real-time implementation of novel signal processing algorithms suitable for improving hearing study and hearing aid applications. In this paper, we present our smartphone-based platform and associated tools and documents for hearing study and hearing aid applications. An overview is given of the algorithms/apps developed to run on iOS and Android based smartphones in real time. Summary of the performance of developed algorithms/apps using smartphone is presented in comparison with other devices that are available in the market under different noisy conditions and low SNRs.

Deep neural network based direction of arrival estimation for hearing aid applications using smartphone

Deep neural network (DNN) techniques are gaining popularity due to performance boost in many applications. In this work we propose a DNN-based method for finding the direction of arrival (DOA) of speech source for hearing aid applications using smartphones. We consider the DOA estimation as a classification problem and use the magnitude and phase of speech signal as a feature set for DNN training stage and obtaining appropriate model. The model is trained and derived using real noisy speech data recorded on smartphone in different environments under low SNRs. The DNN-based DOA method, with the pre-trained model, is implemented and run on Android smartphone in real time and evaluated objectively and subjectively. The test results are presented showing the performance of proposed method versus other methods.

A Robust Target Linearly Constrained Minimum Variance Beamformer With Spatial Cues Preservation for Binaural Hearing Aids

In this paper, a binaural beamforming algorithm for hearing aid applications is introduced.The beamforming algorithm is designed to be robust to some error in the estimate of the target speaker direction. The algorithm has two main components: a robust target linearly constrained minimum variance (TLCMV) algorithm based on imposing two constraints around the estimated direction of the target signal, and a post-processor to help with the preservation of binaural cues. The robust TLCMV provides a good level of noise reduction and low level of target distortion under realistic conditions. The post-processor enhances the beamformer abilities to preserve the binaural cues for both diffuse-like background noise and directional interferers (competing speakers), while keeping a good level of noise reduction. The introduced algorithm does not require knowledge or estimation of the directional interferers' directions nor the second-order statistics of noise-only components. The introduced algorithm requires an estimate of the target speaker direction, but it is designed to be robust to some deviation from the estimated direction. Compared with recently proposed state-of-the-art methods, comprehensive evaluations are performed under complex realistic acoustic scenarios generated in both anechoic and mildly reverberant environments, considering a mismatch between estimated and true sources direction of arrival. Mismatch between the anechoic propagation models used for the design of the beamformers and the mildly reverberant propagation models used to generate the simulated directional signals is also considered. The results illustrate the robustness of the proposed algorithm to such mismatches.

A computationally efficient blind source separation for hearing aid applications and its real-time implementation on smartphone

Conventional Blind Source Separation (BSS) techniques are computationally complex. This is due to the calculation of the demixing matrix for the entire signal or due to the frequent update of the demixing matrix at every time frame index, making them impractical to use in many real-time applications. In this paper, a robust, neural network based two-microphone sound source localization method is used as a criterion to enhance the efficiency of the Independent Vector Analysis (IVA), a BSS method. IVA is used to separate speech and noise sources which are convolutedly mixed. The practical usability of the proposed method is proved by implementing it on a smartphone to separate speech and noise in real-world scenarios for hearing-aid applications. The experimental results with objective and subjective tests reveal the usefulness of the developed method for real-world applications.

Frequency-based multiband adaptive compression for hearing aid application

Multi-band Dynamic Range (MBDR) Compression is a key part of the signal processing operation in hearing aid devices (HADs). Operating speed of the MBDR compressor plays an important role in preserving the quality and intelligibility of the output signal. Traditional fast-acting compressor preserves the audible cues in quiet speech but, in presence of surrounding noise, it can degrade the sound quality by introducing pumping and breathing effects. Alternatively, slow-acting compressor maintains the temporal cues and the listening comfort but may provide inadequate gain for soft inputs that come right after loud inputs. HADs may operate in a variable acoustic environment. Therefore, a fixed speed in compression might affect the performance of the hearing aids. In this study, we propose a frequency(FFT) based nine-band adaptive MBDR compression which uses spectral flux as a measure of the intensity change in input level to adapt the speed of the compressor in each band. Gain, threshold and compression ratio of the compressor for nine bands are adjusted based on the audiogram of the hearing impaired patient. The proposed frequency-based adaptive MBDR compression method is implemented on smartphone. The objective and subjective test results demonstrate the performance of proposed method compared to fixed compression approaches.

Miniaturized Flexible Piezoresistive Pressure Sensors: Poly(3,4-ethylenedioxythiophene):Poly(styrenesulfonate) Copolymers Blended with Graphene Oxide for Biomedical Applications.

Piezoresistive pressure sensors have garnered significant attention because of their wide applications in automobiles, intelligent buildings, and biomedicine. For in vivo testing, the size of pressure sensors is a vital factor to monitor the pressure of specific portions of a human body. Therefore, the primary focus of this study is to miniaturize piezoresistive pressure sensors with graphene oxide (GO)-incorporated poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) composite films on a flexible substrate for biomedical applications. Prior to the fabrication of pressure sensors, a comprehensive material analysis was applied to identify the horizontal placement of GO flakes within the PEDOT:PSS copolymers, revealing a reduction in variable range hopping distance and an enhancement in carrier mobility. For devices scaled to 0.2 cm, the sensitivity of PEDOT:PSS pressure sensors was conspicuously decreased owing to the late response, which can be effectively solved by GO incorporation. Using technology computer-aided design simulations, the current crowded at the PEDOT:PSS film surface and in the vicinity of an indium-tin-oxide electrode corner was found to be responsible for the changes in piezoresistive behaviors of the scaled devices. The miniaturized flexible piezoresistive pressure sensors with PEDOT:PSS/GO composite films are capable of monitoring the brain pressure of intracranial surgery of a rat and discerning different styles of music for a potential application in hearing aids.

Enhanced smart hearing aid using deep neural networks

Being smart has become a feature of almost all the electronic devices that we use every day. Recently, deep neural network based speech enhancement has made a breakthrough to speech de-noising process; however, in some situations, noise could be more important than speech, like talking while a fire alarm is activated. Hearing impaired people are the ones that could be most hurt in those situations, because of their lower ability to hear, especially in noisy environment. Available hearing aids lack the smart feature that distinguishes between desired and undesired noise types, so their users have to rely on separate alerting systems to ensure their safety. The idea that takes our concern in this work is to develop a smart hearing aid that has the ability to detect important noise and make it audible. In this paper, we will present three noise of interest aware speech enhancement networks for hearing aids application using deep learning, so as to introduce the idea of smart hearing aid.

Feasibility of Using a Smartphone-Based Hearing Aid Application to Improve Attitudes Toward Amplification and Hearing Impairment.

Objective The objective of the study was to determine whether short-term experience (i.e., 4 weeks) with a smartphone-based hearing aid application (SHAA) might positively improve attitudes toward amplification uptake and hearing impairment. Method We recruited 15 experienced hearing aid wearers who had ceased wearing their devices for > 1 year (i.e., "In-the-Drawer" group) and 15 individuals with self-reported hearing difficulties who had yet to adopt hearing aids (i.e., "First-Time" group). We obtained participant attitudes pre- and post-SHAA using 3 surveys and analyzed perceptible changes in attitude for each survey. Comparative findings were then generalized to the health belief model in the context of perceived benefits (i.e., efficacy of an action to reduce risk) and reduced perceived barriers (i.e., tangible and psychological costs that inhibit compliance and adoption). Results A short trial period with an SHAA appears to modify the psychological perception toward amplification and reduce listener perception with respect to hearing difficulties in both groups. Conclusion A short trial period with an SHAA improved the perceived benefits and reduced the perceived barriers in the average First-Time listener, who often delays adoption of traditional amplification. The same trial period was also found to improve perceived benefits and reduce perceived barriers for the average In-the-Drawer listener, but to a lesser degree than their First-Time counterparts.