Head Related Impulse Response Interpolation and Extrapolation Using Deep Belief Networks

Grady Kestler; Shahrokh Yadegari; David Nahamoo

ICASSP 2019accepted0 citations

Head Related Impulse Response Interpolation and Extrapolation Using Deep Belief Networks

Grady Kestler, Shahrokh Yadegari, David Nahamoo

Abstract

This paper presents a machine learning Deep Belief Network technique for interpolation and extrapolation of HRTF (Head Related Transfer Function) databases. In this technique, we decouple the stereo pair of HRTFs for each ear. Furthermore, we remove the inter-aural time differences (ITD) and distance attenuation from the recorded HRTF measurements such that the DBNs only interpolate and extrapolate the spectrum filtering of the audio for the two ears. Testing on the CIPIC and SCUT databases produces results of an average log spectral distortion less than 3 dB for all points around the head.

BibTeX

@inproceedings{icassp2019_headrelatedimpul,
  title = {Head Related Impulse Response Interpolation and Extrapolation Using Deep Belief Networks},
  author = {Grady Kestler and Shahrokh Yadegari and David Nahamoo},
  booktitle = {ICASSP 2019},
  year = {2019}
}