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.
AbstractThe purpose of this paper is to study the effects of dynamic-range compression and linear amplification on speech intelligibility and quality for hearing-impaired listeners. The paper focuses on the relative benefit of compression compared to linear amplification and the effect of varying the number of compression channels and the compression time constants. The stimuli are sentences in a background of stationary speech-shaped noise. Speech intelligibility and quality indices are used to predict the listener responses for a mild, moderate sloping, and moderate severe hearing loss. The results show a strong interaction between signal processing, speech intensity, and hearing loss. The results are interpreted in terms of the two major effects of compression on speech: the increase in audibility and the decrease in temporal and spectral envelope contrast.SumarioEl proposito de este trabajo fue estudiar los efectos de la compresion del rango dinamico y de la amplificacion lineal sobre la inteligibilid... The development of hearing aids incorporates two aspects, namely, the audiological and the technical point of view. The former focuses on items like the recruitment phenomenon, the speech intelligibility of hearing-impaired persons, or just on the question of hearing comfort. Concerning these subjects, different algorithms intending to improve the hearing ability are presented in this paper. These are automatic gain controls, directional microphones, and noise reduction algorithms. Besides the audiological point of view, there are several purely technical problems which have to be solved. An important one is the acoustic feedback. Another instance is the proper automatic control of all hearing aid components bymeans of a classification unit. In addition to an overview of state-of-the-art algorithms, this paper focuses on future trends. 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 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. 