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

TL;DR

This paper investigates whether increasing the number of microphones beyond two improves the accuracy of time difference-of-arrivals estimation in blind audio signals, demonstrating potential performance gains without changing existing algorithms.

Contribution

It shows that using more than two microphones can significantly enhance TDOA estimation accuracy with current blind channel identification methods based on cross-correlation identities.

Findings

Increasing microphone count improves TDOA estimation accuracy.

Performance gains are achieved without altering the existing computational pipeline.

Preliminary examples suggest benefits of joint optimization and multiple microphones.

Abstract

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.