  Recently, many forms of audio industrial applications, such as sound
monitoring and source localization, have begun exploiting smart multi-modal
devices equipped with a microphone array. Regrettably, model-based methods are
often difficult to employ for such devices due to their high computational
complexity, as well as the difficulty of appropriately selecting the
user-determined parameters. As an alternative, one may use deep network-based
methods, but these are often difficult to generalize, nor can they generate the
desired beamforming map directly. In this paper, a computationally efficient
acoustic beamforming algorithm is proposed, which may be unrolled to form a
model-based deep learning network for real-time imaging, here termed the
DAMAS-FISTA-Net. By exploiting the natural structure of an acoustic beamformer,
the proposed network inherits the physical knowledge of the acoustic system,
and thus learns the underlying physical properties of the propagation. As a
result, all the network parameters may be learned end-to-end, guided by a
model-based prior using back-propagation. Notably, the proposed network enables
an excellent interpretability and the ability of being able to process the raw
data directly. Extensive numerical experiments using both simulated and
real-world data illustrate the preferable performance of the DAMAS-FISTA-Net as
compared to alternative approaches.
