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

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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.