DocumentCode :
1651276
Title :
Modeling head-related transfer functions via spatial-temporal Gaussian process
Author :
Komatsu, Teruhisa ; Nishino, Takanori ; Peters, Gareth W. ; Matsui, Takashi ; Takeda, Kenji
Author_Institution :
Grad. Sch. of Inf. Sci., Nagoya Univ., Nagoya, Japan
fYear :
2013
Firstpage :
301
Lastpage :
305
Abstract :
We propose a novel application of a family of non-parametric statistical models to estimate head-related transfer functions (HRTFs) using spatial-temporal Gaussian processes (GPs). In this approach, we model the head-related impulse response (HRIR) utilizing non-parametric regression via a GP. The challenge posed by this problem involves accurate modeling of the spatial correlation structure jointly with the temporal correlation structure at each spatial location for the HRIR. We solve this problem by constructing a joint spatial-temporal kernel characterizing the GP regression model. To perform inference, we estimate the hyper-parameters of the GP regression kernel via maximum signal-to-deviation-ratio estimation on the basis of a real experimental setup in which we collected observations of the HRIR using two head-and-torso simulators (HATSs): KEMAR and B&K. We also perform cross validation of the model by training on the KEMAR system and assessing the generalization of our model and its out-of-sample predictive power for HRIRs at any locations that we predict by the model assessed on the B&K system. The corresponding HRTFs are obtained as the Fourier transform of the HRIRs. In the experiments, we show that our method is robust against variation in the azimuth interval needed to perform high-accuracy interpolation and has the expressive power to handle the individual characteristics of each HATS.
Keywords :
Fourier transforms; Gaussian processes; correlation methods; interpolation; regression analysis; transfer functions; transient response; B&K system; Fourier transform; GP regression kernel; HATS; HRIR; KEMAR system; head-and-torso simulators; head-related impulse response; head-related transfer functions; interpolation; maximum signal-to-deviation-ratio estimation; nonparametric regression; nonparametric statistical models; spatial correlation structure; spatial-temporal Gaussian process; spatial-temporal kernel; temporal correlation structure; Azimuth; Gaussian processes; Interpolation; Mathematical model; Predictive models; Training data; Transfer functions; Gaussian Process; Head-Related Transfer Function; Head-related Impulse Response; Interpolation; Kernel Methods;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
ISSN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2013.6637657
Filename :
6637657
Link To Document :
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