This work presents a method for designing the weighting parameter required by Wiener-based binaural noise reduction methods. This parameter establishes the desired tradeoff between noise reduction and binaural cue preservation in hearing aid applications. The proposed strategy was specially derived for the preservation of interaural level difference, interaural time difference and interaural coherence binaural cues. It is defined as a function of the average input noise power at the microphones, providing robustness against the influence of joint changes in noise and speech power (Lombard effect), as well as to signal to noise ratio (SNR) variations. A theoretical framework, based on the mathematical definition of the homogeneity degree, is presented and applied to a generic augmented Wiener-based cost function. The theoretical insights obtained are supported bycomputational simulations and psychoacoustic experiments using the multichannel Wiener filter with interaural transfer function preservation technique (MWF-ITF), as a case study. Statistical analysis indicates that the proposed dynamic structure for the weighting parameter and the design method of its fixed part provide significant robustness against changes in the original binaural cues of both speech and residual noise, at the cost of a small decrease in the noise reduction performance, as compared to the use of a purely fixed weighting parameter.
Multi-Speaker DOA Estimation in Binaural Hearing Aids using Deep Learning and Speaker Count Fusion
School of Electrical Engineering and Computer Science, University of Ottawa, Canada（Ottawa大学电气与计算机科学学院, 加拿大）；Department of Electrical and Computer Engineering, Universite de Sherbrooke, Canada（Sherbrooke大学电气与计算机工程系, 加拿大）；WS Audiology, Germany（德国WS听觉学）
ABSE-NET: A Lightweight Neural Model for Active Binaural Speech Enhancement in Open-Fit Hearing Aids
College of Computer Science, Inner Mongolia University（内蒙古大学计算机学院）；College of Electronic and Information Engineering, Inner Mongolia University（内蒙古大学电子与信息工程学院）
DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids
University of Augsburg（奥格斯堡大学）；Technical University of Munich（慕尼黑工业大学）；WS Audiology；Imperial College London（帝国理工学院）；Munich Data Science Institute（慕尼黑数据科学研究所）
Advances in Intelligent Hearing Aids: Deep Learning Approaches to Selective Noise Cancellation
Department of Electrical Engineering, National University of Sciences and Technology (NUST), Center for Excellence in Manufacturing Engineering (CEME)（电子工程系，国立科学与技术大学（NUST），制造工程卓越中心）
Overview Of The 2023 Icassp Sp Clarity Challenge: Speech Enhancement For Hearing Aids
Acoustics Research Centre, University of Salford（索尔福德大学声学研究中心）；Department of Computer Science, University of Sheffield（谢菲尔德大学计算机科学系）；School of Medicine, University of Nottingham（诺丁汉大学医学院）；School of Psychology, Cardiff University（卡迪夫大学心理学院）
Feasibility of Time-Domain DNN-Based Speech Enhancement on Embedded FPGA for Hearing Aids
Radboud University, Donders Institute for Brain, Cognition, and Behaviour, The Netherlands（拉德堡德大学，脑认知行为研究所，荷兰）；Mortimer B. Zuckerman Mind, Brain, Behavior Institute, Columbia University, USA（莫蒂默·B·齐克曼心智、大脑与行为研究所，哥伦比亚大学，美国）
Audiovisual joint learning for end-to-end hearing aids
Graduate Institute of Communication Engineering, National Taiwan University（国立台湾大学通讯工程学研究所）；Research Center for Information Technology Innovation, Academia Sinica（中央研究院资讯科技创新研究中心）；Department of Otolaryngology-Head and Neck Surgery, Taipei Veterans General Hospital（台北荣民总医院耳鼻喉头颈外科）；School of Medicine, National Yang Ming Chiao Tung University（国立阳明交通大学医学院）；Center for Hearing Research, University of California, Irvine（加州大学欧文分校听力研究中心）
HAAQI-Net: A Non-intrusive Neural Music Audio Quality Assessment Model for Hearing Aids
Academia Sinica（中央研究院）；National Chengchi University（国立政治大学）；National Yang Ming Chiao Tung University（国立阳明交通大学）；Research Center for Information Technology Innovation, Academia Sinica（中央研究院资讯科技创新研究中心）；Institute of Information Science, Academia Sinica（中央研究院资讯科学研究所）；College of Informatics, National Chengchi University（国立政治大学资讯学院）；Institute of Communications Engineering, National Yang Ming Chiao Tung University（国立阳明交通大学通讯工程研究所）
Restoring speech intelligibility for hearing aid users with deep learning
Audatic（Audatic公司）；Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Technische Universität Berlin（柏林工业大学）；Bernstein Center for Computational Neuroscience Berlin（柏林伯恩斯坦计算神经科学中心）
Head shadow enhancement with low-frequency beamforming improves sound localization and speech perception for simulated bimodal listeners
KU Leuven – University of Leuven（鲁汶大学）
由于双耳线索的可用性差，许多听力受损的听众难以定位声音。带有人工耳蜗和对侧助听器的听众--所谓的双峰听众--是表现最差的，因为两耳间的时间和水平差都很难传递。提出了一种新的低频头部阴影增强方法。头部阴影增强是通过在每只耳朵中使用对侧衰减的固定波束形成器来实现的。该方法导致耳间水平差随角度单调变化。在语音和噪声空间分离的情况下，它还提高了低频信噪比。我们验证了该方法在两个实验中的声学模拟双峰听。在定位实验中，与标准全向麦克风相比，性能从50.5°提高到26.8°均方根误差。在噪声中的语音实验中，语音从正面方向呈现。当噪声来自人工耳蜗侧时，语音接收阈值提高了15.7 dB SNR，当噪声来自助听器侧时，语音接收阈值提高了7.6 dB SNR，当噪声来自各个方向时，语音接收阈值不受影响。除了双模态听众，该方法也可能是有前途的双侧人工耳蜗或助听器用户。其低计算复杂度使得该方法适合于在当前临床设备中的应用。保留字：头影增强、耳间水平差增强、声音定位、定向听力、噪声中言语、言语清晰度PACS：43.60.Fg、43.66.Pn、43.66.Qp、43.66.Rq、43.66.Ts、43.71.- k，43.71.Es，43.71.Ky
