RI: Small: Learning the Relationship between the Anatomy and Spatial Hearing

RI：小：学习解剖学和空间听觉之间的关系

• 批准号: 1117716
• 负责人: Ramani Duraiswami
• 金额: $ 16.5万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117716&HistoricalAwards=false
• 关键词: RI; Small; Learning; Relationship; between

To apply machine learning to problems in the physical world, one needs models/algorithms that are faithful to physics. We consider understanding how the anatomical structure of the body and ears leads to the remarkable ability to localize a sound source in a complex and noisy environment that is innate in most animals and humans. The cues used in localization arise from the process of the acoustic wave scattering off the complex-shaped listener's body and ears. Numerically, these changes in the sound spectrum are characterized by the head-related transfer function (HRTF). Every person's body is unique, and the HRTF is highly individual. It is possible to measure the HRTF; however, the measurement requires specialized hardware and is tedious. There has been considerable interest in convenient methods to obtaining the HRTF. We propose to develop a framework to perform machine learning to establish a relationship between the anatomy and HRTF. An HRTF database with 100 subjects, along with their anthropometric measurements, is available. A novel LMA (Learning of Multiple Attributes) algorithm will be developed. The key properties of this algorithm are that it can incorporate physical constraints into the learning and predict complex structured outputs in continuous spaces. The algorithm will find the low-dimensional manifold in high-dimensional HRTF space and to map the manifold structure to anatomical parameters. The research will create novel machine learning algorithms that are able to incorporate physics based constraints, and these will find application in other problems. HRTF generation from simple body measurements will allow introduction of personalized spatial audio into fields such as human-computer interaction, consumer electronics, auditory assistive devices for the vision-impaired, robotics, entertainment, education, and surveillance. Training of K-16 and graduate students in the proposed research will add to the nations talent pool.

为了将机器学习应用于物理世界中的问题，人们需要忠于物理学的模型/算法。我们考虑了解身体和耳朵的解剖结构如何导致在复杂和嘈杂的环境中定位声源的非凡能力，这是大多数动物和人类与生俱来的。用于定位的线索来自于复杂形状的听者的身体和耳朵的声波散射过程。在数值上，声谱中的这些变化由与头部相关的传递函数(HRTF)来表征。每个人的身体都是独一无二的，HRTF是高度个人化的。测量HRTF是可能的；但是，测量需要专门的硬件并且很繁琐。人们对获得HRTF的简便方法很感兴趣。我们建议开发一个执行机器学习的框架，以建立解剖学和HRTF之间的关系。HRTF数据库包含100名受试者，以及他们的人体测量数据。提出了一种新的多属性学习算法。该算法的主要特点是能够将物理约束融入到学习中，并能预测连续空间中复杂的结构化输出。该算法将在高维HRTF空间中找到低维流形，并将流形结构映射到解剖参数。这项研究将创造能够结合基于物理的约束的新型机器学习算法，这些算法将在其他问题中得到应用。通过简单的人体测量生成HRTF将允许将个性化空间音频引入诸如人机交互、消费电子产品、视障人士听觉辅助设备、机器人、娱乐、教育和监控等领域。对K-16和研究生进行拟议研究的培训将增加国家人才库。