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

要将机器学习应用于物理世界中的问题，人们需要忠实于物理的模型/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显着能力，这是大多数动物和人类与生俱来的。定位中使用的线索来自声波从复杂形状的收听者的身体和耳朵散射的过程。在数字上，声谱中的这些变化由头部相关传递函数（HRTF）表征。每个人的身体都是独一无二的，HRTF也是高度个性化的。测量HRTF是可能的;然而，测量需要专门的硬件并且是冗长的。人们对获得HRTF的方便方法有相当大的兴趣。 我们建议开发一个框架来执行机器学习，以建立解剖结构和HRTF之间的关系。HRTF数据库有100名受试者，沿着他们的人体测量数据。将开发一种新型LMA（多属性学习）算法。该算法的关键特性是它可以将物理约束纳入学习中，并在连续空间中预测复杂的结构化输出。 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。该研究将创建新的机器学习算法，这些算法能够结合基于物理的约束，并将在其他问题中找到应用。从简单的身体测量生成HRTF将允许将个性化的空间音频引入到诸如人机交互、消费电子、视觉障碍者的听觉辅助设备、机器人、娱乐、教育和监控等领域。在拟议的研究中培训K-16和研究生将增加国家的人才库。

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.