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.

当前的助听器受到两个主要局限性：1）助听器音频处理策略是僵化的，并且不能充分适应聆听环境，2）听力测试和助听器拟合程序不允许可靠的诊断听力损失的基本性质诊断，并且经常导致设备的不良拟合，这些研究计划将使用新的机器学习方法来革新智能的方法，以革命性的启发性，以革命性的技术来革命性，以革命性的方面革命性的方面，这些方面的行动范围彻底彻底改变了启动的操作。关于患者的听力损失，可以更具个性化的配件。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，在一个安静的办公室举行的对话应与在繁忙的混响餐厅举行的方式不同。当前的高端助听器确实基于简单的声学环境分类系统在少量不同的处理策略之间进行切换，该系统可以监视传入音频的简单方面。但是，分类精度有限，这是听力设备在嘈杂的多源环境中表现较差的原因之一。未来的智能设备应该能够识别出更大，更多样化的音频环境集，可能会使用智能手机使用无线通信。此外，助听器应使用此信息来告知助听器中声音的处理方式。该项目的第一组的目的是开发算法，以促进此类设备的开发。焦点之一将是在一类称为音频纹理的声音上，这些声音纹理结构丰富，但具有暂时的信号。例子包括：食客在一家餐馆里苦苦挣扎；火车沿着轨道嘎嘎作响；在树上呼啸而过；水从水龙头奔跑。音频纹理通常表明环境，因此它们提供有关助听器可以利用的场景的有价值的信息。此外，纹理通常会损坏目标信号及其抑制作用可以帮助听力受损。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减少降噪策略，以利用有关环境中存在的音频纹理的信息。智能听力depicessensorical holice holderalical holderal损失可能与许多基本原因有关。在耳蜗内，内毛细胞（IHC）或外毛细胞（OHC），代谢障碍和结构异常可能存在功能障碍。理想情况下，听力学家应根据对听力损失的根本原因的详细知识来适应患者的助听器，因为这决定了最佳设备设置，或者是否要进行干预。不幸的是，当前拟合程序中采用的听力测试（称为听力图）无法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，但事实证明它很难设计它们。在项目的第二组中，我们提出了一种不同的方法，该方法在测试的每个阶段之后都会完善患者听力损失的模型，并使用此方法自动设计并选择下一阶段的刺激，这特别有用。这些测试将是快速，准确且能够确定患者特定潜在功能障碍的形式。然后，使用计算机模拟和听力测试的混合物，将使用患者听力损失的模型以最佳方式设置听力设备。

项目成果

Infinite-Horizon Gaussian Processes

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

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

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

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