Investigating, modelling and improving music listening for hearing impaired individual.

该项目旨在通过探索和理解当前助听器（HAs）技术的局限性，以及为新的建模研究奠定基础，以更好地理解所涉及的感知机制，并潜在地预测新技术，技术和工具的结果，来改善听障人士的音乐聆听体验。这将通过三个主要的工作包（WP）来实现：WP 1- Dataset。设计一套用于评估听力受损者和助听器使用者音乐听力的标记数据。这将包括音频材料（例如，主干音乐曲目、录音场景和声学环境--测量和/或模拟），以及用于进行评估和产生额外数据的工具（例如，听力损失模型和虚拟助听器，以及用于创建沉浸式交互体验的工具，例如声音空间化器和混响模拟器）。为了最大限度地发挥潜在影响，数据集的设计将涉及相关利益相关者（例如助听器用户、临床医生、科学家、音乐家等）。WP 2-确认。使用正常听力参与者对数据集进行初步验证，可能使用模拟听力损失。这一步的一个重要结果是开始标记输入和输出（例如感知的声音质量等）。从评估中获取数据，并开始建立人工智能模型，随后（即在本研究的以下步骤中，或可能超出本特定项目）将其训练为人工侦听器/预测器。通过使用开发的数据集和AI模型，进一步探索研究的不同方面。这个阶段的重点将在项目中期进行选择，但具体的开发可能包括：-接口/控制。开发一个可个性化/可扩展的界面原型，用于存储MP的各种参数。这可以基于人工智能，从用户那里了解什么可以/应该被控制以及如何控制，并最终产生自己定制的控制界面的原型。最佳音响效果。为特定的听众优化空间（真实的或虚拟的）的声学效果。在这里将计算优化方法与行为评估进行比较可能会很有趣，再次旨在进一步训练人工智能模型，并可能开发一个原型优化器，用于本项目之外的进一步评估。最佳信号处理选择。如上所述，但具体涉及MP的校准。

This project aims at improving the music listening experiences of hearing impaired individuals byexploring and understanding the limitations of current hearing aids (HAs) technologies, as well assetting the grounds for novel modelling research for better understanding the perceptualmechanisms involved, and potentially predicting outcomes from novel technologies, techniquesand tools. This will be achieved through three main work-packages (WP):WP1 - Dataset. Design of a set of labelled data for assessing music listening for hearing impairedlisteners and hearing aid users. This will include audio material (e.g. stem music tracks, listeningscenarios and acoustic environments - measured and/or simulated), and tools to carry outevaluations and produce additional data (e.g. hearing loss models and virtual hearing aids, as wellas tools for the creation of immersive an interactive experiences, such as sound spatialisers andreverb simulators). In order to maximise the potential impact, the dataset will be designedinvolving the relevant stakeholders (e.g. hearing aid users, clinicians, scientists, musicians, etc.).WP2 - Validation. Carry out an initial validation of the dataset with normal hearing participants,possibly using simulated hearing loss. An important outcome of this step is to start labelling theinput and output (e.g. perceived sound quality, etc.) data from the evaluation, and commence thesetup of an AI model to be subsequently (i.e. in the following steps of this research, or possiblybeyond this specific project) trained as artificial listener/predictor.WP3 - Explorations. By using the developed dataset and AI model, further explore differentaspects of the research. The choice on the focus of this stage will be made mid-project, but specificdevelopments might include:- Interface/control. Develop a prototype of personalisable/scalable interface for controllingthe various parameters of the MP. This could be based on AI, understanding from the userwhat can/should be controlled and how, and ultimately producing a prototype of theirown customised control interface.- Optimal acoustics. Optimising the acoustics of the spaces (either real or virtual) for aspecific listener. It might be interesting here to compare computational optimisation3methods with behavioural assessments, aiming again at further training the AI model, andpossibly developing a prototype optimiser for further evaluations beyond this project.- Optimal signal processing choices. As above, but related specifically to the calibration ofthe MP.