  Ambisonics encoding of microphone array signals can enable various spatial
audio applications, such as virtual reality or telepresence, but it is
typically designed for uniformly-spaced spherical microphone arrays. This paper
proposes 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 learnable
preprocessing as well as a loss function consisting of mean average error,
spatial correlation, and energy preservation components. The method is
validated on two microphone arrays with regular and irregular shapes having
four microphones, on simulated reverberant scenes with multiple sources. The
results of the validation show that the proposed method can meet or exceed the
performance of a conventional signal-independent Ambisonics encoder on a number
of error metrics.
