Doctor of Philosophy

dissertationHearing aids suffer from the problem of acoustic feedback that limits the gain provided by hearing aids. Moreover, the output sound quality of hearing aids may be compromised in the presence of background acoustic noise. Digital hearing aids use advanced signal processing to reduce acoustic feedback and background noise to improve the output sound quality. However, it is known that the output sound quality of digital hearing aids deteriorates as the hearing aid gain is increased. Furthermore, popular subband or transform domain digital signal processing in modern hearing aids introduces analysis-synthesis delays in the forward path. Long forward-path delays are not desirable because the processed sound combines with the unprocessed sound that arrives at the cochlea through the vent and changes the sound quality. In this dissertation, we employ a variable, frequency-dependent gain function that is lower at frequencies of the incoming signal where the information is perceptually insignificant. In addition, the method of this dissertation automatically identifies and suppresses residual acoustical feedback components at frequencies that have the potential to drive the system to instability. The suppressed frequency components are monitored and the suppression is removed when such frequencies no longer pose a threat to drive the hearing aid system into instability. Together, the method of this dissertation provides more stable gain over traditional methods by reducing acoustical coupling between the microphone and the loudspeaker of a hearing aid. In addition, the method of this dissertation performs necessary hearing aid signal processing with low-delay characteristics. The central idea for the low-delay hearing aid signal processing is a spectral gain shaping method (SGSM) that employs parallel parametric equalization (EQ) filters. Parameters of the parametric EQ filters and associated gain values are selected using a least-squares approach to obtain the desired spectral response. Finally, the method of this dissertation switches to a least-squares adaptation scheme with linear complexity at the onset of howling. The method adapts to the altered feedback path quickly and allows the patient to not lose perceivable information. The complexity of the least-squares estimate is reduced by reformulating the least-squares estimate into a Toeplitz system and solving it with a direct Toeplitz solver. The increase in stable gain over traditional methods and the output sound quality were evaluated with psychoacoustic experiments on normal-hearing listeners with speech and music signals. The results indicate that the method of this dissertation provides 8 to 12 dB more hearing aid gain than feedback cancelers with traditional fixed gain functions. Furthermore, experimental results obtained with real world hearing aid gain profiles indicate that the method of this dissertation provides less distortion in the output sound quality than classical feedback cancelers, enabling the use of more comfortable style hearing aids for patients with moderate to profound hearing loss. Extensive MATLAB simulations and subjective evaluations of the results indicate that the method of this dissertation exhibits much smaller forward-path delays with superior howling suppression capability

Evaluation of the sparse coding shrinkage noise reduction algorithm for the hearing impaired

Although there are numerous single-channel noise reduction strategies to improve speech perception in a noisy environment, most of them can only improve speech quality but not improve speech intelligibility for normal hearing (NH) or hearing impaired (HI) listeners. Exceptions that can improve speech intelligibility currently are only those that require a priori statistics of speech or noise. Most of the noise reduction algorithms in hearing aids are adopted directly from the algorithms for NH listeners without taking into account of the hearing loss factors within HI listeners. HI listeners suffer more in speech intelligibility than NH listeners in the same noisy environment. Further study of monaural noise reduction algorithms for HI listeners is required.The motivation is to adapt a model-based approach in contrast to the conventional Wiener filtering approach. The model-based algorithm called sparse coding shrinkage (SCS) was proposed to extract key speech information from noisy speech. The SCS algorithm was evaluated by comparison with another state-of-the-art Wiener filtering approach through speech intelligibility and quality tests using 9 NH and 9 HI listeners. The SCS algorithm matched the performance of the Wiener filtering algorithm in speech intelligibility and speech quality. Both algorithms showed some intelligibility improvements for HI listeners but not at all for NH listeners. The algorithms improved speech quality for both HI and NH listeners.Additionally, a physiologically-inspired hearing loss simulation (HLS) model was developed to characterize hearing loss factors and simulate hearing loss consequences. A methodology was proposed to evaluate signal processing strategies for HI listeners with the proposed HLS model and NH subjects. The corresponding experiment was performed by asking NH subjects to listen to unprocessed/enhanced speech with the HLS model. Some of the effects of the algorithms seen in HI listeners are reproduced, at least qualitatively, by using the HLS model with NH listeners.Conclusions: The model-based algorithm SCS is promising for improving performance in stationary noise although no clear difference was seen in the performance of SCS and a competitive Wiener filtering algorithm. Fluctuating noise is more difficult to reduce compared to stationary noise. Noise reduction algorithms may perform better at higher input signal-to-noise ratios (SNRs) where HI listeners can get benefit but where NH listeners already reach ceiling performance. The proposed HLS model can save time and cost when evaluating noise reduction algorithms for HI listeners

Dynamic Processing Neural Network Architecture For Hearing Loss Compensation

This paper proposes neural networks for compensating sensorineural hearing loss. The aim of the hearing loss compensation task is to transform a speech signal to increase speech intelligibility after further processing by a person with a hearing impairment, which is modeled by a hearing loss model. We propose an interpretable model called dynamic processing network, which has a structure similar to band-wise dynamic compressor. The network is differentiable, and therefore allows to learn its parameters to maximize speech intelligibility. More generic models based on convolutional layers were tested as well. The performance of the tested architectures was assessed using spectro-temporal objective index (STOI) with hearing-threshold noise and hearing aid speech intelligibility (HASPI) metrics. The dynamic processing network gave a significant improvement of STOI and HASPI in comparison to popular compressive gain prescription rule Camfit. A large enough convolutional network could outperform the interpretable model with the cost of larger computational load. Finally, a combination of the dynamic processing network with convolutional neural network gave the best results in terms of STOI and HASPI

Hearing Aids

This chapter presents an overview of the current state of a hearing aid tracing back through the history. The hearing aid, which was just a sound collector in the sixteenth century, has continued to develop until the current digital hearing aid for realizing the downsizing and digital signal processing, and this is the age of implanted hearing devices. However, currently popular implanted hearing devices are a fairly large burden for people soon after they become aware of their hearing loss, although auditory stimulation to the nerve in the early stage can avoid accelerated cognitive decline and an increased risk of incident all-cause dementia. For this reason, we tend to stick to wearable hearing aids that are easy to be put on and take off. Although the digital hearing aid has already reached the technical ceiling, the noninvasive hearing aids have some severe problems that are yet to be resolved. In the second half of this chapter, we discuss the scientific and technical solutions to broaden the range of permissible users of hearing aids

Manipulation of Auditory Feedback in Individuals with Normal Hearing and Hearing Loss

Auditory feedback, the hearing of one’s own voice, plays an important role in the detection of speech errors and the regulation of speech production. The limited auditory cues available with a hearing loss can reduce the ability of individuals with hearing loss to use their auditory feedback. Hearing aids are a common assistive device that amplifies inaudible sounds. Hearing aids can also change auditory feedback through digital signal processing, such as frequency lowering. Frequency lowering moves high frequency information of an incoming auditory stimulus into a lower frequency region where audibility may be better. This can change how speech sounds are perceived. For example, the high frequency information of /s/ is moved closer to the lower frequency area of /ʃ/. As well, real-time signal processing in a laboratory setting can also manipulate various aspects of speech cues, such as intensity and vowel formants. These changes in auditory feedback may result in changes in speech production as the speech motor control system may perceive these perturbations as speech errors. A series of experiments were carried out to examine changes in speech production as a result of perturbations in the auditory feedback in individuals with normal hearing and hearing loss. Intensity and vowel formant perturbations were conducted using real-time signal processing in the laboratory. As well, changes in speech production were measured using auditory feedback that was processed with frequency lowering technology in hearing aids. Acoustic characteristics of intensity of vowels, sibilant fricatives, and first and second formants were analyzed. The results showed that the speech motor control system is sensitive to changes in auditory feedback because perturbations in auditory feedback can result in changes in speech production. However, speech production is not completely controlled by auditory feedback and other feedback systems, such as the somatosensory system, are also involved. An impairment of the auditory system can reduce the ability of the speech motor control system to use auditory feedback in the detection of speech errors, even when aided with hearing aids. Effects of frequency lowering in hearing aids on speech production depend on the parameters used and acclimatization time

The effects of hearing aid circuitry and speech presentation level on acceptance of background noise

The present study investigated the effects of hearing aid circuitry and speech presentation level on ANL and hearing in noise in 19 adult, bilateral hearing aid users. The acceptable noise level (ANL) procedure was used to assess acceptance of background noise. Conventional ANLs (i.e., measured at the participant\u27s most comfortable listening level (MCL)) and ANLs at eight fixed speech presentation levels were obtained. Then global ANLs (i.e., ANLs averaged over eight fixed speech presentation levels) and ANL growth (i.e., the slope of the ANL function) were calculated Each measure was obtained in three conditions: unaided, aided with wide dynamic range (WDRC) circuitry, and aided with output limiting compression (dSC) circuitry. Results revealed that conventional ANLs are not significantly different when obtained using any of the three levels of hearing aid circuitry. However, results demonstrated that global ANLs may be affected by hearing aid circuitry in that listeners are able to accept more background noise when in the unaided or dSC circuitry condition compared to using WDRC. Finally, results showed that ANL growth for each type of hearing aid circuit was not significantly different, indicating that ANL growth is stable for all three types of circuitry