22-011
Rong Zheng Awny M. El- Mohandes Navid H. Zandi
PCT filed
Proof of principle
Lokesh Mohan Business Development Officer
Head-Related Transfer Functions (HRTFs) represent an acoustic filtering response of humans’ outer ear, head, and torso. This natural filtering system plays a key role in human binaural auditory systems that allows people to not only hear but also to perceive the direction of incoming sounds. The HRTF characterizes how a human ear receives sounds from a point in space, and depends on, for example, the shapes of a person’s head, pinna, and torso. Accurate estimations of HRTFs for human subjects are crucial in augmented or virtual realities applications, among other applications. Unfortunately, approaches for HRTF estimation generally rely on specialized devices or lengthy measurement processes. Additionally, using another person’s HRTF, or a generic HRTF, will lead to errors in acoustic spatialization and unpleasant auditory experiences.
Researchers at McMaster have developed a novel lightweight process to obtain individualized HRTFs that make it possible for individuals to determine an individualized HRTF at home, without specialized/expensive equipment. The process consists of two key components: a measurement procedure that collects data from spatial locations using in-ear microphones and a smartphone, and a neural network that can be individualized to predict HRTFs of new subjects from sparse measurements. The measurement procedure is fast, lightweight, and easy to conduct. Given measurement data from as low as 60 sparse locations, the deep neural network model can be adapted to generate individualized HRTFs for all directions.