# Individualized Interaural Feature Learning and Personalized Binaural Localization Model +
## 1 Introduction  
Spatial audio technologies has shown its importance in various fields, such as video conferencing, virtual reality, humanoid robot interactions and hearing aids, and there are many existing methods reproducing a spatial sound field on different devices for human listeners. To adapt those devices for great spatial experiences, it is necessary to find the most valuable acoustic cues for human hearing system [1; 2; 3; 4]. Additionally, testing and evaluating the quality of reproduced sound field and the hearing experience of those devices is another challenging problem [5; 6; 7]. The hearing tests with real human volunteers can be time-consuming and would be unfeasible for a huge amount of devices and testing scenarios. One efficient solution is building binaural localization models based on the study of human audio localization mechanism, then running tests on the model for evaluation [8]. Much behavioural and psychoacoustic evidence has confirmed that two individualized interaural cues, Interaural Time Difference (ITD) and Interaural Level Difference (ILD), play an essential role in localizing a sound source. Through intensive studies of the human hearing mechanism, it has become widely understood that individualized ITD and ILD are mainly determined by the filtering of thehead, body and pinna, where the filtering function is the so-called Head-Related Transfer Function (HRTF). During the last two decades, many binaural localization approaches have been developed based on interaural cues. [PERSON] and [PERSON] [9] adopted the Bayes rule to estimate the source position. This method uses interaural intensity difference (IID) and spectral signals to refine the initial estimate given by ITD. [PERSON] et al. [10] provided an individual parametric model jointly using ILD and ITD for azimuth localization. The model first provides a rough estimate of high standard variation based on ILD, and then refines the estimation by selecting the closest ITD. [PERSON] and [PERSON] combined ILD and ITD with a Gaussian Mixture Model (GMM), which also embedded the environmental condition as a latent parameter [11]. Further, many works have attempted to investigate the relationship between interaural cues and source position in a 3-D space. [PERSON] summarized the relationship between interaural cues and azimuth/elevation [12]. [PERSON] et al. proposed a maximum cross-correlation matching method in source cancellation algorithms to pinpoint the source location in a 3-D space [13; 14]. [PERSON] et al. introduced a Bayesian rule-based hierarchical localization system using IID time delay compensation and an interaural matching filter to estimate azimuth and elevation in the interaural-polar coordinate system [15; 16]. [PERSON] et al. proved the local linear bijective mapping between interaural cues and source location on the binaural manifold, and derived a statistical localization model using an EM algorithm [17; 18]. [PERSON] et al. adopted a non-parametric tree-based learning method to retrieve the mapping between the interaural cues and source locations with fewer restrictions on its spatiotemporal characteristics and environment structure [19]. Several studies mentioned above have demonstrated the importance of feature spectrum selection to localization performance, especially for elevation localization, and various methods for feature selection have been proposed. [PERSON] et al. indicated that the IID at the frequency above 2 kHz contains the characteristics of head shadow and pinna diffraction [12], which can be exploited for elevation estimation. [PERSON] et al. retrieved the existence of elevation-dependent features at a low frequency, which was attributed to head diffraction and torso reflections [20]. [PERSON] et al. proved the elevation ambiguities of Interaural Phase Difference (IPD) at the high-frequency range (between 2 to 8 kHz) and used a feature space concatenated by full-spectrum ILD and low-frequency IPD [17].