  Given an unknown audio source, the estimation of time differences-of-arrivals
(TDOAs) can be efficiently and robustly solved using blind channel
identification and exploiting the cross-correlation identity (CCI). Prior
"blind" works have improved the estimate of TDOAs by means of different
algorithmic solutions and optimization strategies, while always sticking to the
case N = 2 microphones. But what if we can obtain a direct improvement in
performance by just increasing N? In this paper we try to investigate this
direction, showing that, despite the arguable simplicity, this is capable of
(sharply) improving upon state-of-the-art blind channel identification methods
based on CCI, without modifying the computational pipeline. Inspired by our
results, we seek to warm up the community and the practitioners by paving the
way (with two concrete, yet preliminary, examples) towards joint approaches in
which advances in the optimization are combined with an increased number of
microphones, in order to achieve further improvements.
