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

Are Multiple Cross-Correlation Identities better than just Two? Improving the Estimate of Time Differences-of-Arrivals from Blind Audio Signals