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Sound field decomposition predicts waveforms in arbitrary directions usingsignals from a limited number of microphones as inputs. Sound fielddecomposition is fundamental to downstream tasks, including sourcelocalization, source separation, and spatial audio reproduction. Conventionalsound field decomposition methods such as Ambisonics have limited spatialdecomposition resolution. This paper proposes a learning-based Neural Soundfield Decomposition (NeSD) framework to allow sound field decomposition withfine spatial direction resolution, using recordings from microphone capsules ofa few microphones at arbitrary positions. The inputs of a NeSD system includemicrophone signals, microphone positions, and queried directions. The outputsof a NeSD include the waveform and the presence probability of a queriedposition. We model the NeSD systems respectively with different neuralnetworks, including fully connected, time delay, and recurrent neural networks.We show that the NeSD systems outperform conventional Ambisonics and DOANetmethods in sound field decomposition and source localization on speech, music,and sound events datasets. Demos are available athttps://www.youtube.com/watch?v=0GIr6doj3BQ.

Neural Sound Field Decomposition with Super-resolution of Sound Direction