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J. Sinker, and J. Angus, "Functional Representation for Efficient Interpolations of Head Related Transfer Functions in Mobile Headphone Listening," Paper 9241, (2015 May.). doi: J. Sinker, and J. Angus, "Functional Representation for Efficient Interpolations of Head Related Transfer Functions in Mobile Headphone Listening," Paper 9241, (2015 May.). doi:
Abstract: In this paper two common methods of HRTF/HRIR dataset interpolation, that is simple linear interpolation in the time and frequency domain, are assessed using a Normalized Mean Square Error metric. Frequency domain linear interpolation is shown to be the superior of the two methods, but both suffer from poor behavior and inconsistency over interpolated regions. An alternative interpolation approach based upon the Principal Component Analysis of the dataset is offered; the method uses a novel application of the Discrete Cosine Transform to obtain a functional representation of the PCA weight vectors that may be queried for any angle on a continuous scale. The PCA/DCT method is shown to perform favorably to the simple time domain method, even when applied to a dataset that has been heavily compressed during both the PCA and DCT analysis.

@article{sinker2015functional,
author={sinker, joseph and angus, jamie},
journal={journal of the audio engineering society},
title={functional representation for efficient interpolations of head related transfer functions in mobile headphone listening},
year={2015},
volume={},
number={},
pages={},
doi={},
month={may},} @article{sinker2015functional,
author={sinker, joseph and angus, jamie},
journal={journal of the audio engineering society},
title={functional representation for efficient interpolations of head related transfer functions in mobile headphone listening},
year={2015},
volume={},
number={},
pages={},
doi={},
month={may},
abstract={in this paper two common methods of hrtf/hrir dataset interpolation, that is simple linear interpolation in the time and frequency domain, are assessed using a normalized mean square error metric. frequency domain linear interpolation is shown to be the superior of the two methods, but both suffer from poor behavior and inconsistency over interpolated regions. an alternative interpolation approach based upon the principal component analysis of the dataset is offered; the method uses a novel application of the discrete cosine transform to obtain a functional representation of the pca weight vectors that may be queried for any angle on a continuous scale. the pca/dct method is shown to perform favorably to the simple time domain method, even when applied to a dataset that has been heavily compressed during both the pca and dct analysis.},}

TY - paper
TI - Functional Representation for Efficient Interpolations of Head Related Transfer Functions in Mobile Headphone Listening
SP - EP -
AU - Sinker, Joseph
AU - Angus, Jamie
PY - 2015
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2015 TY - paper
TI - Functional Representation for Efficient Interpolations of Head Related Transfer Functions in Mobile Headphone Listening
SP - EP -
AU - Sinker, Joseph
AU - Angus, Jamie
PY - 2015
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2015
AB - In this paper two common methods of HRTF/HRIR dataset interpolation, that is simple linear interpolation in the time and frequency domain, are assessed using a Normalized Mean Square Error metric. Frequency domain linear interpolation is shown to be the superior of the two methods, but both suffer from poor behavior and inconsistency over interpolated regions. An alternative interpolation approach based upon the Principal Component Analysis of the dataset is offered; the method uses a novel application of the Discrete Cosine Transform to obtain a functional representation of the PCA weight vectors that may be queried for any angle on a continuous scale. The PCA/DCT method is shown to perform favorably to the simple time domain method, even when applied to a dataset that has been heavily compressed during both the PCA and DCT analysis.