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

Adaptive gain processing with offending frequency suppression for digital hearing aids

Journal ArticleDigital hearing aids identify acoustic feedback signals and cancel them continuously in a closed loop with an adaptive filter. This scheme facilitates larger hearing aid gain and improves the output sound quality of hearing aids. However, the output sound quality deteriorates as the hearing aid gain is increased. This paper presents two methods to modify the forward path gain in digital hearing aids. The first approach employs a variable, frequency-dependent gain function that is lower at frequencies of the incoming signal where the information is perceptually insignificant. The second method of this paper 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 gain processing methods of this paper provide 8 to 12 dB more hearing aid gain than feedback cancelers with fixed gain functions. Furthermore, experimental results obtained with real world hearing aid gain profiles indicate that the gain processing methods of this paper, individually and combined, provide 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

Design of a reusable distributed arithmetic filter and its application to the affine projection algorithm

Digital signal processing (DSP) is widely used in many applications spanning the spectrum from audio processing to image and video processing to radar and sonar processing. At the core of digital signal processing applications is the digital filter which are implemented in two ways, using either finite impulse response (FIR) filters or infinite impulse response (IIR) filters. The primary difference between FIR and IIR is that for FIR filters, the output is dependent only on the inputs, while for IIR filters the output is dependent on the inputs and the previous outputs. FIR filters also do not sur from stability issues stemming from the feedback of the output to the input that aect IIR filters. In this thesis, an architecture for FIR filtering based on distributed arithmetic is presented. The proposed architecture has the ability to implement large FIR filters using minimal hardware and at the same time is able to complete the FIR filtering operation in minimal amount of time and delay when compared to typical FIR filter implementations. The proposed architecture is then used to implement the fast affine projection adaptive algorithm, an algorithm that is typically used with large filter sizes. The fast affine projection algorithm has a high computational burden that limits the throughput, which in turn restricts the number of applications. However, using the proposed FIR filtering architecture, the limitations on throughput are removed. The implementation of the fast affine projection adaptive algorithm using distributed arithmetic is unique to this thesis. The constructed adaptive filter shares all the benefits of the proposed FIR filter: low hardware requirements, high speed, and minimal delay.Ph.D.Committee Chair: Anderson, Dr. David V.; Committee Member: Hasler, Dr. Paul E.; Committee Member: Mooney, Dr. Vincent J.; Committee Member: Taylor, Dr. David G.; Committee Member: Vuduc, Dr. Richar

Control of feedback for assistive listening devices

Acoustic feedback refers to the undesired acoustic coupling between the loudspeaker and microphone in hearing aids. This feedback channel poses limitations to the normal operation of hearing aids under varying acoustic scenarios. This work makes contributions to improve the performance of adaptive feedback cancellation techniques and speech quality in hearing aids. For this purpose a two microphone approach is proposed and analysed; and probe signal injection methods are also investigated and improved upon

Adaptive Feedback Cancellation in Hearing Aids

Acoustic feedback is a well-known phenomenon in hearing aids and public address systems. Under certain conditions it causes the so-called howling effect, which is highly annoying for the hearing aid user and limits the maximum amplification of the hearing aid. The most common choice to prevent howling is the adaptive feedback cancellation algorithm, which is able to completely eliminate the feedback signal. However, standard adaptive feedback cancellation algorithms suffer from a biased adaptation if the input signal is spectrally colored, as it is for speech and music signals. Due to this bias distortion artifacts (entrainment) are generated and consequently, the sound quality is significantly reduced. Most of the known methods to reduce the bias have focused on speech signals. However, those methods do not cope with music, since the tonality and correlation are much stronger for such signals. This leads to a higher bias and consequently, to stronger entrainment for music than for speech. Other methods, which deal with music signals, work only satisfactorily when using a very slow adaptation speed. This reduces the ability to react fast to feedback path changes. Hence, howling occurs for a longer time when the feedback path is changing. In this thesis, a new sub-band adaptive feedback cancellation system for hearing aid applications is proposed. It combines decorrelation methods with a new realization of a non-parametric variable step size. The adaptation is realized in sub-bands which decreases the computational complexity and increases the adaptation performance of the system simultaneously. The applied decorrelation methods, prediction error filter and frequency shift, are well known approaches to reduce the bias. However, the combination of both is first proposed in this thesis. To apply the proposed step size in the context of adaptive feedback cancellation, a method to estimate the signal power of the desired input signal, i.e., without feedback, also referred to as source signal power is necessary. This estimate is theoretically derived and it is demonstrated that it is a reliabe estimate if the decorrelation methods are additionally applied. In order to further improve the performance of the system three additional control methods are derived: The first one is an impulse detection to detect wideband impulses, which could lead to misadaptation. Secondly, a modified estimate of the source signal power to stabilize the system in case of jarring voices is proposed. Lastly, a correlation detection, which is applied to improve the trade-off between adaptation stability and tracking behavior, is developed. The complete system is optimized and evaluated for several speech and music signals as well as for different feedback scenarios in simulations with feedback paths measured under realistic situations. Additionally, the system is tested by real-time simulations with hearing aid dummies and a torso and head simulator. For both simulation setups hearing loss compensation methods as applied in realistic hearing aids are used. The performance is measured in terms of being able to prevent entrainment (adaptation stability) and reacting to feedback path changes (tracking behavior). The complete adaptive feedback cancellation system shows an excellent performance. Furthermore, the system relies only on few parameters, shows a low computational complexity, and therefore has a strong practical relevance

Subband Adaptive Modeling of Digital Hearing Aids

In this thesis, the application of a subband adaptive model to characterize compression behaviour of five digital hearing aids is investigated. Using a signal-to-error ratio metric, modeling performance is determined by varying the number of analysis bands in the subband structure as well as consideration of three adaptive algorithms. The normalized least mean-squares (NLMS), the affine projection algorithm (APA), and the recursive least-squares (RLS) algorithms are employed using a range of parameters to determine the impact on modeling performance. Using the subband adaptive model to estimate the time-varying frequency response of each hearing aid allows the Perceptual Evaluation of Speech Quality (PESQ) mean-opinion score (MOS) to be computed. The PESQ MOS facilitates an estimation of a subjective assessment of speech quality using an objective score. Initial results suggest the PESQ MOS score is able to differentiate speech processed by hearing aids allowing them to be ranked accordingly. Further work is required to obtain subjective assessments of the processed speech signals and determine if possible correlations exist

Objective Assessment of Machine Learning Algorithms for Speech Enhancement in Hearing Aids

Speech enhancement in assistive hearing devices has been an area of research for many decades. Noise reduction is particularly challenging because of the wide variety of noise sources and the non-stationarity of speech and noise. Digital signal processing (DSP) algorithms deployed in modern hearing aids for noise reduction rely on certain assumptions on the statistical properties of undesired signals. This could be disadvantageous in accurate estimation of different noise types, which subsequently leads to suboptimal noise reduction. In this research, a relatively unexplored technique based on deep learning, i.e. Recurrent Neural Network (RNN), is used to perform noise reduction and dereverberation for assisting hearing-impaired listeners. For noise reduction, the performance of the deep learning model was evaluated objectively and compared with that of open Master Hearing Aid (openMHA), a conventional signal processing based framework, and a Deep Neural Network (DNN) based model. It was found that the RNN model can suppress noise and improve speech understanding better than the conventional hearing aid noise reduction algorithm and the DNN model. The same RNN model was shown to reduce reverberation components with proper training. A real-time implementation of the deep learning model is also discussed

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