Scalable Room Acoustic Modelling (SCReAM)
可扩展的房间声学建模 (SCReAM)
基本信息
- 批准号:EP/V002554/1
- 负责人:
- 金额:$ 51.9万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Research Grant
- 财政年份:2021
- 资助国家:英国
- 起止时间:2021 至 无数据
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
We spend the majority of our lives indoors. Within enclosed spaces, sound is reflected numerous times, leading to reverberation. We are accustomed to perceiving reverberation-we unconsciously use it to navigate the space, and, when absent, we notice. Similarly, our electronic devices, such as laptops, TVs or smart home devices, are exposed to reverberation and need to take into account its presence. Being able to predict, synthesise, and control reverberation is therefore important. This is done using room acoustic models. Existing room acoustic models suffer from two main limitations. First, they were originally developed from very different starting points and for very different purposes, which has led to a highly fragmented research field where advancements in one area do not translate to advancements in other areas, slowing down research. Second, each model has a specific accuracy and a specific computational complexity, with some very accurate models taking several days to run (physical models), while others run in real-time but with low accuracy and only aim to create a pleasing reverberant sound (perceptual models). Thus, there is no single model that allows to scale continuously from one extreme to the other. This project will overcome both limitations by defining a novel, unifying room acoustic model that combines appealing properties of all main types of models and that can scale on demand from a lightweight perceptual model to a full-scale physical model. Such a SCalable Room Acoustic Model (SCReAM) will bring benefits in many applications, ranging from consumer electronics and communications, to computer games, immersive media, and architectural acoustics. The model will be able to adapt in real time, enabling end-users to get the best possible auditory experience allowed by the available computing resources. Audio software developers will not need to update their development chains once more powerful machines become available, thus reducing costs. Electronic equipment, such as hands-free devices, smart loudspeakers, and sound reinforcement systems, will be able to build a more flexible internal representation of room acoustics, allowing them to reduce unwanted echoes, to remove acoustic feedback, and/or to improve the tonal balance of reproduced sound.The main hypothesis of the project is that a connection exists between physical models and perceptual models based on so-called delay networks, and that this connection can be leveraged to develop the sought-after unifying and scalable model.The research will be conducted at the University of Surrey with industrial support by Sonos (audio consumer electronics), Electronic Arts (computer games), Audio Software Development Limited (computer games audio consultancy), and Adrian James Acoustics (acoustics consultancy).
我们一生中的大部分时间都在室内度过。在封闭的空间内,声音被反射无数次,导致混响。我们习惯于感知混响--我们下意识地用它来导航空间,当它不存在时,我们就会注意到。同样,我们的电子设备,如笔记本电脑、电视或智能家居设备,都会受到混响的影响,需要考虑到它的存在。因此,能够预测、合成和控制混响非常重要。这是使用房间声学模型来完成的。现有的房间声学模型受到两个主要限制。首先,它们最初是从非常不同的起点和非常不同的目的开发的,这导致了一个高度分散的研究领域,一个领域的进步不能转化为其他领域的进步,从而减缓了研究速度。其次,每个模型都有特定的精度和特定的计算复杂性,一些非常精确的模型需要几天的时间才能运行(物理模型),而另一些模型实时运行但精度较低,并且只旨在创建令人愉悦的混响声音(感知模型)。因此,不存在允许从一个极端持续扩展到另一个极端的单一模型。该项目将通过定义一种新颖的、统一的房间声学模型来克服这两个限制,该模型结合了所有主要类型模型的吸引人的特性,并且可以根据需要从轻型感知模型扩展到完整的物理模型。这种可扩展的房间声学模型(Scream)将在许多应用中带来好处,从消费电子和通信到计算机游戏、沉浸式媒体和建筑声学。该模型将能够实时适应,使最终用户能够在可用计算资源允许的情况下获得尽可能好的听觉体验。一旦有更强大的机器可用,音频软件开发商将不需要更新他们的开发链,从而降低成本。电子设备,如免提设备、智能扬声器和扩声系统,将能够建立更灵活的室内声学内部表示,使它们能够减少不需要的回声,消除声反馈,和/或改善再现声音的音调平衡。该项目的主要假设是,物理模型和基于所谓延迟网络的感知模型之间存在联系,这种联系可以被用来开发受欢迎的统一和可扩展的模型。这项研究将在萨里大学进行,工业支持包括Sonos(音频消费电子产品)、艺电(计算机游戏)、音响软件开发有限公司(电脑游戏音响咨询)和禤浩焯詹姆斯音响(音响咨询)。
项目成果
期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
User Expectation of Room Acoustic Parameters in Virtual Reality Environments
用户对虚拟现实环境中房间声学参数的期望
- DOI:10.1109/i3da57090.2023.10289314
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Burnett B
- 通讯作者:Burnett B
Perceptual evaluation of low-complexity diffraction models from a single edge
从单边缘对低复杂度衍射模型进行感知评估
- DOI:
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Mannall J
- 通讯作者:Mannall J
HIGHER-ORDER SCATTERING DELAY NETWORKS FOR ARTIFICIAL REVERBERATION
用于人工混响的高阶散射延迟网络
- DOI:
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Scerbo, M.
- 通讯作者:Scerbo, M.
Efficient Diffraction Modeling Using Neural Networks and Infinite Impulse Response Filters
使用神经网络和无限脉冲响应滤波器进行高效衍射建模
- DOI:10.17743/jaes.2022.0107
- 发表时间:2023
- 期刊:
- 影响因子:1.4
- 作者:Mannall J
- 通讯作者:Mannall J
Grouped Feedback Delay Networks With Frequency-Dependent Coupling
具有频率相关耦合的分组反馈延迟网络
- DOI:10.1109/taslp.2023.3277368
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Das O
- 通讯作者:Das O
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Enzo De Sena其他文献
Paraunitary approximation of matrices of analytic functions - the polynomial procrustes problem
解析函数矩阵的拟酉逼近 - 多项式普鲁斯特斯问题
- DOI:
10.1016/j.sctalk.2024.100318 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Stephan Weiss;Sebastian J. Schlecht;Orchisama Das;Enzo De Sena - 通讯作者:
Enzo De Sena
Joint Acoustic Localization and Dereverberation Through Plane Wave Decomposition and Sparse Regularization
通过平面波分解和稀疏正则化进行联合声学定位和去混响
- DOI:
- 发表时间:
2019 - 期刊:
- 影响因子:0
- 作者:
Niccolò Antonello;Enzo De Sena;M. Moonen;P. Naylor;Toon van Waterschoot - 通讯作者:
Toon van Waterschoot
Enzo De Sena的其他文献
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{{ truncateString('Enzo De Sena', 18)}}的其他基金
Challenges in Immersive Audio Technology
沉浸式音频技术的挑战
- 批准号:
EP/X032914/1 - 财政年份:2024
- 资助金额:
$ 51.9万 - 项目类别:
Research Grant
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