Hearing Aid HA algorithms need to be tuned “fitted” to match the impairment of each specific patient. The lack of a fundamental HA fitting theory is a strong contributing factor to an unsatisfying sound experience for about 20 of HA patients. This paper proposes a probabilistic modeling approach to the design of HA algorithms. The proposed method relies on a generative probabilistic model for the hearing loss problem and provides for automated inference of the corresponding 1 signal processing algorithm, 2 the fitting solution as well as 3 a principled performance evaluation metric. All three tasks are realized as message passing algorithms in a factor graph representation of the generative model, which in principle allows for fast implementation on HA or mobile device hardware. The methods are theoretically worked out and simulated with a custom-built factor graph toolbox for a specific hearing loss model.
The state-of-the-art in hearing aid signal processing is well described by Kates @cite_6 and Hamacher @cite_33 . More specifically, the literature on dynamic range compression technology for hearing loss compensation is nicely summarized by @cite_10 and @cite_4 . In both works, DRC circuits are developed through direct design, i.e., the hearing loss problem is not an explicit part of the solution. In contrast, a problem-based signal processing solution for hearing loss compensation has first been formulated in @cite_31 , where an optimal compensation gain is computed through Kalman filtering. The current paper extends that work by proposing a fully probabilistic modelling approach for both the SP, PE and MC tasks as well as an in-situ executable data base collection method. Moreover, the current work presents a factor graph framework for efficient execution of these tasks through message passing on FFGs.
@cite_4 @cite_33 @cite_6 @cite_31 @cite_10
2113159496 2795172492 629565630 1965384704
Modern hearing aids use Dynamic Range Compression (DRC) as the primary solution to combat Hearing Loss (HL). Unfortunately, common DRC based solutions to hearing loss are not directly based on a proper mathematical or algorithmic description of the hearing loss problem. In this paper, we propose a probabilistic model for describing hearing loss, and we use Bayesian inference for deriving optimal HL compensation algorithms. We will show that, for a simple specific generative HL model, the inferred HL compensation algorithm corresponds to the classic DRC solution. An advantage to our approach is that it is readily extensible to more complex hearing loss models, which by automated Bayesian inference would yield complex yet optimal hearing loss compensation algorithms. In millimeter wave (mm-wave) massive multiple-input multiple-output (MIMO) systems, acquiring accurate channel state information is essential for efficient beamforming (BF) and multiuser interference cancellation, which is a challenging task since a low signal-to-noise ratio is encountered before BF in large antenna arrays. The mm-wave channel exhibits a 3-D clustered structure in the virtual angle of arrival (AOA), angle of departure (AOD), and delay domain that is imposed by the effect of power leakage, angular spread, and cluster duration. We extend the approximate message passing (AMP) with a nearest neighbor pattern learning algorithm for improving the attainable channel estimation performance, which adaptively learns and exploits the clustered structure in the 3-D virtual AOA-AOD-delay domain. The proposed method is capable of approaching the performance bound described by the state evolution based on vector AMP framework, and our simulation results verify its superiority in mm-wave systems associated with a broad bandwidth. Preface Hearing-Aid Technology Types of Hearing Aids From Analog to Digital Digital Circuit Components Batteries Concluding Remarks References Signal Processing Basics Signal and System Properties Discrete Fourier Transform Filters and Filter Banks Block Processing Digital System Concerns Concluding Remarks References The Electroacoustic System Hearing Aid System Head and Ear Microphone and Receiver Vent Acoustics Occlusion Effect Concluding Remarks References Directional Microphones Hearing-Aid Microphones Directional Response Patterns Frequency Response Magnitude Frequency Response Microphone Mismatch Interaction with Vents Microphone Noise Microphones on the Head Microphone Performance Indices Rooms and Reverberation Benefit in the Real World Concluding Remarks References Adaptive and Multi-Microphone Arrays Two-Microphone Adaptive Array Delay-And-Sum Beamforming Adaptive Arrays Superdirective Arrays Widely-Spaced Arrays Array Benefits Concluding Remarks References Wind Noise Turbulence Hearing-Aid Measurements Signal Characteristics Wind-Noise Reduction Concluding Remarks References Feedback Cancellation The Feedback System Gain-Reduction Solutions Adaptive Feedback Cancellation Processing Limitations Concluding Remarks References Dynamic-Range Compression Does Compression Help? Algorithm Design Concerns Single-Channel Compression Multi-Channel Compression Frequency-Domain Compression Frequency Warping Concluding Remarks References Single-Microphone Noise Suppression Properties of Speech and Noise Signals Low-Level Expansion Envelope Valley Tracking Bandwidth Reduction Envelope Modulation Filters Concluding Remarks References Spectral Subtraction Noise Estimation Wiener Filter Spectral Subtraction Algorithm Effectiveness Concluding Remarks References Spectral Contrast Enhancement Auditory Filters in the Damaged Cochlea Spectral Valley Suppression Spectral Contrast Modification Excess Upward Spread of Masking F2 F1 Ratio Processing Comparison Combining Spectral Contrast Enhancement with Compression Concluding Remarks References Sound Classification The Rationale for Classification Signal Features Feature Selection Classifier Algorithms Classification Examples Concluding Remarks References Binaural Signal Processing The "Cocktail Party" Problem Signal Transmission Binaural Compression Binaural Noise Suppression Dichotic Band Splitting Concluding Remarks References Index In this work the dynamic compressive sensing (CS) problem of recovering sparse, correlated, time-varying signals from sub-Nyquist, non-adaptive, linear measurements is explored from a Bayesian perspective. While there has been a handful of previously proposed Bayesian dynamic CS algorithms in the literature, the ability to perform inference on high-dimensional problems in a computationally efficient manner remains elusive. In response, we propose a probabilistic dynamic CS signal model that captures both amplitude and support correlation structure, and describe an approximate message passing algorithm that performs soft signal estimation and support detection with a computational complexity that is linear in all problem dimensions. The algorithm, DCS-AMP, can perform either causal filtering or non-causal smoothing, and is capable of learning model parameters adaptively from the data through an expectation-maximization learning procedure. We provide numerical evidence that DCS-AMP performs within 3 dB of oracle bounds on synthetic data under a variety of operating conditions. We further describe the result of applying DCS-AMP to two real dynamic CS datasets, as well as a frequency estimation task, to bolster our claim that DCS-AMP is capable of offering state-of-the-art performance and speed on real-world high-dimensional problems.