  While current deep learning (DL)-based beamforming techniques have been
proved effective in speech separation, they are often designed to process
narrow-band (NB) frequencies independently which results in higher
computational costs and inference times, making them unsuitable for real-world
use. In this paper, we propose DL-based mel-subband spatio-temporal beamformer
to perform speech separation in a car environment with reduced computation cost
and inference time. As opposed to conventional subband (SB) approaches, our
framework uses a mel-scale based subband selection strategy which ensures a
fine-grained processing for lower frequencies where most speech formant
structure is present, and coarse-grained processing for higher frequencies. In
a recursive way, robust frame-level beamforming weights are determined for each
speaker location/zone in a car from the estimated subband speech and noise
covariance matrices. Furthermore, proposed framework also estimates and
suppresses any echoes from the loudspeaker(s) by using the echo reference
signals. We compare the performance of our proposed framework to several NB,
SB, and full-band (FB) processing techniques in terms of speech quality and
recognition metrics. Based on experimental evaluations on simulated and
real-world recordings, we find that our proposed framework achieves better
separation performance over all SB and FB approaches and achieves performance
closer to NB processing techniques while requiring lower computing cost.
