Neural Network Modeling of Head-Related Transfer Functions for Realistic 3D Sound Generation

This paper presents a software-based approach for the generation and headphone reproduction of three-dimensional (3D) spatial audio in virtual reality (VR) environments such as CAVE systems. The proposed system was implemented in MATLAB with a graphical user interface (GUI) to provide an interactive platform for real-time and offline 3D audio rendering. The core of the proposed method is the modeling of the Head-Related Transfer Function (HRTF) using a feedforward multilayer perceptron (MLP) neural network with a single hidden layer trained by the backpropagation algorithm. The neural network receives the sound source azimuth and elevation angles as its inputs and predicts the corresponding 4.5-ms finite impulse responses (FIRs) for arbitrary source directions. These estimated HRTFs are subsequently employed to synthesize binaural audio signals. To evaluate the proposed approach, several 3D sound samples were generated and compared with those obtained using the conventional database-based method, in which measured HRTFs are directly retrieved from a lookup table. Subjective evaluation demonstrated that the proposed neural network model provides smoother spatial transitions and more natural auditory perception than the conventional interpolation-free database approach. Furthermore, compared with dedicated hardware-based 3D audio systems, the proposed software solution exhibits only a negligible reduction in perceptual audio quality while offering a substantially lower implementation cost, making it an attractive alternative for virtual reality applications.