Rapid BRIR generation approach using Variational Auto-Encoders and LSTM neural networks

This article proposes a rapid binaural room impulse response (BRIR) generation for real-time auralization with a moving receiver inside a room and a fixed sound source. The proposed method uses generative models, such as variational auto-encoders and long short-term memory neural networks. First, a number of static receivers are modeled within a given room, covering a representative area where listeners can typically be found. Then, a BRIR is simulated in each receiver, and from them, more BRIRs uniformly distributed in the scenario are synthesized through a data augmentation procedure. In the sequel, by applying an unsupervised model to cluster the receivers positionally, the BRIRs are organized into groups. Furthermore, variational autoencoder training is performed to reduce computational complexity. The results when comparing the reconstructed BRIRs with the original ones show that the error is within the just noticeable difference threshold. A computational cost analysis in terms of the total number of operations is carried out, achieving a computational gain of over 75%, and a storage reduction of more than 99% with respect to classic BRIR generation techniques. This opens the possibility of obtaining a reliable real-time auralization with a moving receiver inside the room.