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

本项目旨在通过探索和理解当前助听器（HAs）技术的局限性来改善听障人士的音乐聆听体验，并为更好地理解所涉及的感知机制提供新的建模研究基础，并潜在地预测新技术，技术和工具的结果。这将通过三个主要工作包（WP）来实现：WP1 -数据集。设计一组标签数据，用于评估听力受损听众和助听器使用者的音乐听力。这将包括音频材料（如stem music tracks，听力场景和声学环境-测量和/或模拟），以及进行外部评估和产生额外数据的工具（如听力损失模型和虚拟助听器，以及用于创建沉浸式互动体验的工具，如声音空间化器和动词模拟器）。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）。WP2 -验证。对听力正常的参与者进行数据集的初步验证，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来自评估的输入和输出（例如感知音质等）数据，并开始建立随后（即在本研究的以下步骤中，或可能超出此特定项目）训练为人工听众/预测器的AI模型。WP3 -探索。利用开发的数据集和人工智能模型，进一步探索研究的不同方面。这一阶段的重点将在项目中期做出选择，但具体的开发可能包括：-界面/控制。开发可个性化/可扩展接口的原型，用于控制MP的各种参数。这可以基于AI，从用户那里了解什么可以/应该被控制以及如何控制，并最终生成他们自己定制控制界面的原型。-最佳音响效果。为特定的听众优化空间的声学效果（无论是真实的还是虚拟的）。将计算优化方法与行为评估进行比较可能会很有趣，目的是进一步训练人工智能模型，并可能开发出一个原型优化器，用于该项目之外的进一步评估。-最佳信号处理选择。如上所述，但具体涉及到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.