Head-related Transfer Function Reconstruction with Anthropometric Parameters and the Direction of the Sound Source: Deep Learning-Based Head-Related Transfer Function Personalization

An accurate head-related transfer function can improve the subjective auditory localization performance of a particular subject. This paper proposes a deep neural network model for reconstructing the head-related transfer function (HRTF) based on anthropometric parameters and the orientation of the sound source. The proposed model consists of three subnetworks, including a one-dimensional convolutional neural network (1D-CNN) to process anthropometric parameters as input features and another network that takes the sound source position as input to serve as a marker. Finally, the outputs of these two networks are merged together as the input to a third network to estimate the HRTF. An objective method and a subjective method are proposed to evaluate the performance of the proposed method. For the objective evaluation, the root mean square error (RMSE) between the estimated HRTF and the measured HRTF is calculated. The results show that the proposed method performs better than a database matching method and a deep-neural-network-based method. In addition, the results of a sound localization test performed for the subjective evaluation show that the proposed method can localize sound sources with higher accuracy than the KEMAR dummy head HRTF or the DNN-based method. The objective and subjective results all show that the personalized HRTFs obtained using the proposed method perform well in HRTF reconstruction.