Gaussian Process Models for HRTF based Sound-Source Localization and Active-Learning

From a machine learning perspective, the human ability localize sounds can be modeled as a non-parametric and non-linear regression problem between binaural spectral features of sound received at the ears (input) and their sound-source directions (output). The input features can be summarized in terms of the individuals head-related transfer functions (HRTFs) which measure the spectral response between the listeners eardrum and an external point in $3$D. Based on these viewpoints, two related problems are considered: how can one achieve an optimal sampling of measurements for training sound-source localization (SSL) models, and how can SSL models be used to infer the subjects HRTFs in listening tests. First, we develop a class of binaural SSL models based on Gaussian process regression and solve a emph{forward selection} problem that finds a subset of input-output samples that best generalize to all SSL directions. Second, we use an emph{active-learning} approach that updates an online SSL model for inferring the subjects SSL errors via headphones and a graphical user interface. Experiments show that only a small fraction of HRTFs are required for $5^{circ}$ localization accuracy and that the learned HRTFs are localized closer to their intended directions than non-individualized HRTFs.

Sparse Head-Related Impulse Response for Efficient Direct Convolution

Head-related impulse responses (HRIRs) are subject-dependent and direction-dependent filters used in spatial audio synthesis. They describe the scattering response of the head, torso, and pinnae of the subject. We propose a structural factorization of the HRIRs into a product of non-negative and Toeplitz matrices; the factorization is based on a novel extension of a non-negative matrix factorization algorithm. As a result, the HRIR becomes expressible as a convolution between a direction-independent emph{resonance} filter and a direction-dependent emph{reflection} filter. Further, the reflection filter can be made emph{sparse} with minimal HRIR distortion. The described factorization is shown to be applicable to the arbitrary source signal case and allows one to employ time-domain convolution at a computational cost lower than using convolution in the frequency domain.