  Direction of arrival (DoA) estimation of multiple signals is pivotal in
sensor array signal processing. A popular multi-signal DoA estimation method is
the multiple signal classification (MUSIC) algorithm, which enables
high-performance super-resolution DoA recovery while being highly applicable in
practice. MUSIC is a model-based algorithm, relying on an accurate mathematical
description of the relationship between the signals and the measurements and
assumptions on the signals themselves (non-coherent, narrowband sources). As
such, it is sensitive to model imperfections. In this work we propose to
overcome these limitations of MUSIC by augmenting the algorithm with
specifically designed neural architectures. Our proposed deep augmented MUSIC
(DA-MUSIC) algorithm is thus a hybrid model-based/data-driven DoA estimator,
which leverages data to improve performance and robustness while preserving the
interpretable flow of the classic method. DA-MUSIC is shown to learn to
overcome limitations of the purely model-based method, such as its inability to
successfully localize coherent sources as well as estimate the number of
coherent signal sources present. We further demonstrate the superior resolution
of the DA-MUSIC algorithm in synthetic narrowband and broadband scenarios as
well as with real-world data of DoA estimation from seismic signals.
