Machine Learning for Hearing Aids: Intelligent Processing and Fitting

助听器机器学习：智能处理和验配

• 批准号: EP/M026957/1
• 负责人: Richard Turner
• 金额: $ 72.04万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM026957%2F1
• 关键词: Machine; Learning; Hearing; Aids; Intelligent

Current hearing aids suffer from two major limitations:1) hearing aid audio processing strategies are inflexible and do not adapt sufficiently to the listening environment,2) hearing tests and hearing aid fitting procedures do not allow reliable diagnosis of the underlying nature of the hearing loss and frequently lead to poor fitting of devices.This research programme will use new machine learning methods to revolutionise both of these aspects of hearing aid technology, leading to intelligent hearing devices and testing procedures which actively learn about a patient's hearing loss enabling more personalised fitting. Intelligent audio processingThe optimal audio processing strategy for a hearing aid depends on the acoustic environment. A conversation held in a quiet office, for example, should be processed in a different way from one held in a busy reverberant restaurant. Current high-end hearing aids do switch between a small number of different processing strategies based upon a simple acoustic environment classification system that monitors simple aspects of the incoming audio. However, the classification accuracy is limited, which is one of the reasons why hearing devices perform very poorly in noisy multi-source environments. Future intelligent devices should be able to recognise a far larger and more diverse set of audio environments, possibly using wireless communication with a smart phone. Moreover, the hearing aid should use this information to inform the way the sound is processed in the hearing aid. The purpose of the first arm of the project is to develop algorithms that will facilitate the development of such devices.One of the focuses will be on a class of sounds called audio textures, which are richly structured, but temporally homogeneous signals. Examples include: diners babbling at a restaurant; a train rattling along a track; wind howling through the trees; water running from a tap. Audio textures are often indicative of the environment and they therefore carry valuable information about the scene that could be harnessed by a hearing aid. Moreover, textures often corrupt target signals and their suppression can help the hearing impaired. We will develop efficient texture recognition systems that can identify the noises present in an environment. Then we will design and test bespoke real-time noise reduction strategies that utilise information about the audio textures present in the environment.Intelligent hearing devicesSensorineural hearing loss can be associated with many underlying causes. Within the cochlea there may be dysfunction of the inner hair cells (IHCs) or outer hair cells (OHCs), metabolic disturbance, and structural abnormalities. Ideally, audiologists should fit a patient's hearing aid based on detailed knowledge of the underlying cause of the hearing loss, since this determines the optimal device settings or whether to proceed with the intervention at. Unfortunately, the hearing test employed in current fitting procedures, called the audiogram, is not able to reliably distinguish between many different forms of hearing loss. More sophisticated hearing tests are needed, but it has proven hard to design them. In the second arm of the project we propose a different approach that refines a model of the patient's hearing loss after each stage of the test and uses this to automatically design and select stimuli for the next stage that are particularly informative. These tests will be be fast, accurate and capable of determining the form of the patient's specific underlying dysfunction. The model of a patient's hearing loss will then be used to setup hearing devices in an optimal way, using a mixture of computer simulation and listening test.

目前的助听器有两个主要的局限性：1)助听器音频处理策略不灵活，不能充分适应听力环境；2)听力测试和助听器安装程序不能可靠地诊断听力损失的潜在性质，经常导致设备安装不良。这项研究计划将使用新的机器学习方法来彻底改变助听器技术的这两个方面，从而产生智能听力设备和测试程序，这些设备和测试程序可以主动了解患者的听力损失，从而实现更个性化的装配。智能音频处理助听器的最佳音频处理策略取决于声学环境。例如，在安静的办公室里进行的谈话，应该与在嘈杂的餐厅里进行的谈话采用不同的处理方式。目前的高端助听器确实基于一个简单的声环境分类系统，在少数不同的处理策略之间切换，该系统监测传入音频的简单方面。然而，分类精度是有限的，这是听力设备在噪声多源环境下性能很差的原因之一。未来的智能设备应该能够识别更大、更多样化的音频环境，可能会使用智能手机的无线通信。此外，助听器应该使用这些信息来告知声音在助听器中的处理方式。该项目第一部分的目的是开发算法，以促进此类设备的开发。其中一个重点将放在一类被称为音频纹理的声音上，这些声音结构丰富，但在时间上是均匀的信号。例子包括：在餐厅用餐的人叽叽喳喳；火车在轨道上嘎吱作响；风呼啸着穿过树林；从水龙头里流出的水。音频纹理通常是环境的指示，因此它们携带有价值的场景信息，这些信息可以被助听器利用。此外，纹理经常破坏目标信号，抑制它们可以帮助听力受损。我们将开发有效的纹理识别系统，可以识别环境中存在的噪音。然后，我们将设计和测试定制的实时降噪策略，利用环境中存在的音频纹理信息。智能听力设备感音神经性听力损失可能与许多潜在原因有关。耳蜗内可能存在内毛细胞（IHCs）或外毛细胞（OHCs）功能障碍、代谢紊乱和结构异常。理想情况下，听力学家应该在详细了解听力损失的潜在原因的基础上为患者配戴助听器，因为这决定了最佳的设备设置或是否继续进行干预。不幸的是，在目前的装配程序中使用的听力测试，称为听力图，不能可靠地区分许多不同形式的听力损失。需要更复杂的听力测试，但事实证明很难设计出来。在项目的第二阶段，我们提出了一种不同的方法，在每个阶段的测试后，提炼患者听力损失的模型，并使用它来自动设计和选择下一阶段特别有用的刺激。这些检查将是快速、准确的，并且能够确定患者特定的潜在功能障碍的形式。病人的听力损失模型将被用来设置最佳的听力设备，使用计算机模拟和听力测试的混合。

项目成果

Infinite-Horizon Gaussian Processes

• 发表时间: 2018-11
• 作者: A. Solin;J. Hensman;Richard E. Turner

Gaussian Process Behaviour in Wide Deep Neural Networks

• 发表时间: 2018-02
• 期刊: ArXiv
• 作者: A. G. Matthews;Mark Rowland;Jiri Hron;Richard E. Turner;Zoubin Ghahramani

TaskNorm: Rethinking Batch Normalization for Meta-Learning

• 发表时间: 2020-03
• 期刊: ArXiv
• 作者: J. Bronskill;Jonathan Gordon;James Requeima;Sebastian Nowozin;Richard E. Turner

On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes

• DOI: 10.17863/cam.15597
• 发表时间: 2015-04
• 作者: A. G. Matthews;J. Hensman;Richard E. Turner;Zoubin Ghahramani

Deterministic Variational Inference for Robust Bayesian Neural Networks

• 发表时间: 2018-09
• 作者: Anqi Wu;Sebastian Nowozin;Edward Meeds;Richard E. Turner;José Miguel Hernández-Lobato;Alexander L. Gaunt