Head-related transfer functions (HRTF) describe the sound transmission from the free field to a place in the ear canal in terms of linear time-invariant systems. Due to the physiological differences of the listeners' outer ears, the measurement of each subject's individual HRTFs is crucial for sound localization in virtual environments (virtual reality).

Measurement of an HRTF can be considered a system identification of the weakly non-linear electro-acoustic chain from the sound source room's HRTF microphone. An optimized formulation of the system identification with exponential sweeps, called the "multiple exponential sweep method" (MESM), was used for the measurement of transfer functions. For this measurement of transfer functions, either the measurement duration or the signal-to-noise ratio could be optimized.

Initial heuristic experiments have shown that using Gabor multipliers to extract the relevant sweeps in the MESM post-processing procedure improves the signal-to-noise ratio of the measured data even further. The objective of this project is to study, in detail, how frame multipliers can optimally be used during this post-processing procedure. In particular, wavelet frames, which best fit the structure of an exponential sweep, will be studied.


Systematic numeric experiments will be conducted with simulated slowly time-variant, weakly non-linear systems. As the parameters of the involved signals are precisely known and controlled, an optimal symbol will automatically be created. Finally, the efficiency of the new method will be tested on a "real world" system, which was developed and installed in the semi-anechoic room of the Institute. It uses in-ear microphones, a subject turntable, 22 loudspeakers on a vertical arc, and a head tracker.


The new method will be used for improved HRTF measurement.