LOCATA challenge: speaker localization with a planar array
This work addresses speaker localization for audio processing applications, but it is incremental as it builds on existing methods like GCF and Particle Filtering.
The authors tackled speaker localization using a planar microphone array in the LOCATA challenge, addressing front-back ambiguity and intermittent speech by post-processing GCF peaks and using a Particle Filter, achieving results for Tasks 1, 3, and 5.
This document describes our submission to the 2018 LOCalization And TrAcking (LOCATA) challenge (Tasks 1, 3, 5). We estimate the 3D position of a speaker using the Global Coherence Field (GCF) computed from multiple microphone pairs of a DICIT planar array. One of the main challenges when using such an array with omnidirectional microphones is the front-back ambiguity, which is particularly evident in Task 5. We address this challenge by post-processing the peaks of the GCF and exploiting the attenuation introduced by the frame of the array. Moreover, the intermittent nature of speech and the changing orientation of the speaker make localization difficult. For Tasks 3 and 5, we also employ a Particle Filter (PF) that favors the spatio-temporal continuity of the localization results.