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Ambisonics encoding of microphone array signals can enable various spatialaudio applications, such as virtual reality or telepresence, but it istypically designed for uniformly-spaced spherical microphone arrays. This paperproposes a method for Ambisonics encoding that uses a deep neural network (DNN)to estimate a signal transform from microphone inputs to Ambisonics signals.The approach uses a DNN consisting of a U-Net structure with a learnablepreprocessing as well as a loss function consisting of mean average error,spatial correlation, and energy preservation components. The method isvalidated on two microphone arrays with regular and irregular shapes havingfour microphones, on simulated reverberant scenes with multiple sources. Theresults of the validation show that the proposed method can meet or exceed theperformance of a conventional signal-independent Ambisonics encoder on a numberof error metrics.

Neural Ambisonics encoding for compact irregular microphone arrays