Robot Audition in Everyday Environments with Deep Learning
通过深度学习在日常环境中进行机器人试镜
基本信息
- 批准号:RGPIN-2021-03908
- 负责人:
- 金额:$ 2.04万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2022
- 资助国家:加拿大
- 起止时间:2022-01-01 至 2023-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Humans and animals rely on audition to monitor their environment, to detect threats and to communicate. In particular, hearing acts as instantaneous omnidirectional attention mechanism, while vision has more continuous and directed field of view. Robots should have similar hearing capabilities to naturally interact with their environment. It is a challenging task to address noisy and dynamic environments, i.e. in the wild and messy world we live in, with the robot having to perform all computations in real-time. Robots audition will allow machines to focus their attention on high priority events not perceptible by vision. Humans could also give robots voice commands when no other interface is available. I believe that large scale deployment of robots in everyday environments will be possible if intelligent machines have advanced hearing capabilities. The goal of this Discovery Grant is to investigate new methods to provide robots with hearing capabilities similar or superior to humans in real world setting. Deep learning showed promising results in audio processing but relies on large datasets and involves high computing power at test time. Deep learning methods need to be adapted for robots as both the embedded computing power and the amount of training data are usually limited. To accomplish this, and based on my current research background, I plan to address the following four short-term objectives: 1) develop a transfer learning method to perform few-shot ego-noise learning; 2) perform sound event localization based on large weakly-labeled datasets; 3) enhance a speech source with neural networks estimating time-frequency masks for each pair of microphones for an array of arbitrary shape; 4) optimize the previous neural networks for real-time processing on an embedded system. The methods will be validated on a wheeled robot that interacts with its environment in realistic settings. To maximize the impact on the robotics community, all the methods will be integrated in an open source software framework. This program will train 4 Highly Qualified Personnel (HQPs) (2 PhDs and 2 MScAs) and 4 undergrad coop trainees. The expertise developed by the HPQs will be based on a unique blend of signal processing, machine learning and embedded system design, which is in high demand amongst Canadian companies involved in the field of robotics. Robots are believed to be part of the solutions to deal with the aging population and labor shortage in developed countries. Robot audition would allow machine to collaborate with workers in warehouses and smart plants and make robotic technologies safer for seniors and healthcare. There is also some serious concern regarding privacy with smart speakers as the technology relies on cloud computing. My research program goes beyond robotics and can benefit this industry as some part of the current research aims to perform speech enhancement on low-cost embedded hardware.
人类和动物依靠听觉来监测他们的环境,检测威胁和交流。特别是,听觉作为瞬时的全方位注意机制,而视觉具有更连续和定向的视野。机器人应该有类似的听力能力,自然地与他们的环境互动。解决嘈杂和动态环境是一项具有挑战性的任务,即在我们生活的狂野和混乱的世界中,机器人必须实时执行所有计算。机器人听觉将允许机器将注意力集中在视觉无法感知的高优先级事件上。当没有其他接口可用时,人类也可以给机器人语音命令。我相信,如果智能机器具有先进的听觉能力,那么在日常环境中大规模部署机器人将是可能的。这项发现补助金的目标是研究新的方法,使机器人在真实的世界环境中具有与人类相似或上级的听力能力。深度学习在音频处理方面表现出了良好的效果,但依赖于大型数据集,并且在测试时需要高计算能力。深度学习方法需要适应机器人,因为嵌入式计算能力和训练数据量通常都是有限的。为了实现这一目标,基于我目前的研究背景,我计划解决以下四个短期目标:1)开发一种迁移学习方法来执行少量自我噪声学习; 2)基于大型弱标记数据集执行声音事件定位; 3)使用神经网络增强语音源,为任意形状的阵列的每对麦克风估计时频掩模; 4)对已有的神经网络进行优化,使其能够在嵌入式系统上进行实时处理。这些方法将在一个轮式机器人上进行验证,该机器人在现实环境中与环境进行交互。为了最大限度地发挥对机器人社区的影响,所有方法都将集成到一个开源软件框架中。该计划将培养4名高素质人才(HQP)(2名博士和2名硕士)和4名本科生合作学员。HPQ开发的专业知识将基于信号处理,机器学习和嵌入式系统设计的独特融合,这是参与机器人领域的加拿大公司的高需求。机器人被认为是解决发达国家人口老龄化和劳动力短缺问题的一部分。机器人试镜将允许机器与仓库和智能工厂的工人合作,并使机器人技术对老年人和医疗保健更安全。由于智能音箱技术依赖于云计算,因此对智能音箱的隐私也存在一些严重的担忧。我的研究计划超越了机器人技术,可以使这个行业受益,因为目前的研究的一部分旨在在低成本嵌入式硬件上进行语音增强。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Grondin, François的其他文献
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{{ truncateString('Grondin, François', 18)}}的其他基金
Robot Audition in Everyday Environments with Deep Learning
通过深度学习在日常环境中进行机器人试镜
- 批准号:
DGECR-2021-00246 - 财政年份:2021
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Launch Supplement
Robot Audition in Everyday Environments with Deep Learning
通过深度学习在日常环境中进行机器人试镜
- 批准号:
RGPIN-2021-03908 - 财政年份:2021
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Modélisation des différentes techniques de sciage selon la courbure
库尔布尔科学技术的不同技术模型化
- 批准号:
170223-1996 - 财政年份:1999
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Modélisation des différentes techniques de sciage selon la courbure
库尔布尔地区不同技术的模型化
- 批准号:
170223-1996 - 财政年份:1998
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Modélisation des différentes techniques de sciage selon la courbure
库尔布尔地区不同技术的模型化
- 批准号:
170223-1996 - 财政年份:1997
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Modélisation des différentes techniques de sciage selon la courbure
库尔布尔科学技术的不同技术模型化
- 批准号:
170223-1996 - 财政年份:1996
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
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