The Impact of the White Noise Gain (WNG) of a Virtual Artificial Head on the Appraisal of Binaural Sound Reproduction
Eugen Rasumow, Matthias Blau, Martin Hansen, Simon Doclo, Steven van de Par, Volker Mellert, Dirk Püschel · DepositOnce · 2014
As an individualized alternative to traditional artificial heads, individual head-related transfer functions (HRTFs) can be synthesized with a microphone array and digital filtering. This strategy is referred to as "virtual artificial head" (VAH). The VAH filter coefficients are calculated by incorporating regularization to account for small errors in the characteristics and/or the position of the microphones. A common way to increase robustness is to impose a socalled white noise gain (WNG) constraint. The higher the WNG, the more robust the HRTF synthesis will be. On the other hand, this comes at the cost of decreasing the synthesis accuracy for the given sample of the HRTF set in question. Thus, a compromise between robustness and accuracy must be found, which furthermore depends on the used setup (sensor noise, mechanical stability etc.). In this study, different WNG are evaluated perceptually by four expert listeners for two different microphone arrays. The aim of the study is to find microphone array-dependent WNG regions which result in appropriate perceptual performances. It turns out that the perceptually optimal WNG varies with the microphone array, depending on the sensor noise and mechanical stability but also on the individual HRTFs and preferences. These results may be used to optimize VAH regularization strategies with respect to microphone characteristics, in particular self noise and stability.