HRTF Representation with Convolutional Auto-encoder

Abstract

The head-related transfer function (HRTF) can be considered as some kind of filter that describes how a sound from an arbitrary spatial direction transfers to the listener’s eardrums. HRTF can be used to synthesize vivid virtual 3D sound that seems to come from any spatial location, which makes it play an important role in the 3D audio technology. However, the complexity and variation of auditory cues inherent in HRTF make it difficult to set up an accurate mathematical model with the conventional methods. In this paper, we put forward an HRTF representation modeling based on convolutional auto-encoder (CAE), which is some type of auto-encoder that contains convolutional layers in the encoder part and deconvolution layers in the decoder part. The experimental evaluation on the ARI HRTF database shows that the proposed model provides very good results on dimensionality reduction of HRTF.