Ambisonics Room Impulse Response Estimation From a Single Omnidirectional Measurement Using Deep Neural Networks

Mapping a room impulse response (RIR) to its Ambisonics representation is not always feasible. However, by adding a weak assumption (i.e., the existence of at least two perpendicular walls in the environment), the Ambisonics representation is restricted to be one of a finite set, with known transformations between the set entries. This makes mapping the omnidirectional RIR to the Ambisonics RIR (ARIR) possible. The authors solve the mapping problem with a convolutional neural network and multi-task variational autoencoder. The room is assumed to be rectangular. The proposed method is based on the image source method with frequency independent reflection coefficients exclusively. The authors focus on the early part of RIRs, where the directional information lies. This method requires only a single RIR. Generalizing to the real world, measurements can obviate the need for specialized hardware for Ambisonics measurement. The proposed method can achieve an SNR of 17.62 dB on estimated first-order ARIRs and 16.15 dB on estimated third-order ARIRs.