  Online audio source separation has been an important part of auditory scene
analysis and robot audition. The main type of technique to carry this out,
because of its online capabilities, has been spatial filtering (or
beamforming), where it is assumed that the location (mainly, the direction of
arrival; DOA) of the source of interest (SOI) is known. However, these
techniques suffer from considerable interference leakage in the final result.
In this paper, we propose a two step technique: 1) a phase-based beamformer
that provides, in addition to the estimation of the SOI, an estimation of the
cumulative environmental interference; and 2) a BLSTM-based TF binary masking
stage that calculates a binary mask that aims to separate the SOI from the
cumulative environmental interference. In our tests, this technique provides a
signal-to-interference ratio (SIR) above 20 dB with simulated data. Because of
the nature of the beamformer outputs, the label permutation problem is handled
from the beginning. This makes the proposed solution a lightweight alternative
that requires considerably less computational resources (almost an order of
magnitude) compared to current deep-learning based techniques, while providing
a comparable SIR performance.
