Cartilage Conduction HRTF Calibration for Real-Time Spatial Audio

The patent introduces an intermediary computational model that maps simplified audio signals to HRTFs, replacing the need for complex physical measurement systems. This intermediary layer allows accurate HRTF determination without requiring sound dampening chambers or multiple speaker arrays.

The patent creates a computational copy of the HRTF measurement process through machine learning models trained on conventional measurement data. These models can then predict HRTFs for new users without requiring actual physical measurements, thus reducing hardware complexity while maintaining precision.

The patent performs preliminary training of computational models using conventional measurement data in advance. Once trained, these models can rapidly predict HRTFs for individual users without requiring time-consuming physical measurements, thus reducing determination time while maintaining precision through the pre-computed knowledge.

The patent creates a computational copy of the HRTF measurement process through machine learning models trained on conventional measurement data. These models can then predict HRTFs for new users without requiring actual physical measurements, thus reducing hardware complexity while maintaining precision.

The patent makes the cartilage conduction transducer multi-functional by using it both to present test sounds and to capture the resulting audio signals. This universal use of a single component simplifies the measurement setup while maintaining the ability to collect data from multiple pinna positions efficiently.

The patent merges the sound presentation function and signal capture function into a single integrated measurement process using cartilage conduction transducers. This combining of functions reduces the number of separate components needed while maintaining calibration efficiency through the use of multiple test positions.