# LOCATA challenge: speaker localization with a planar array

## TL;DR

This paper presents a speaker localization method using a planar microphone array and the Global Coherence Field, addressing front-back ambiguity and speech intermittency with post-processing and particle filtering techniques.

## Contribution

The novel approach combines GCF computation with post-processing and particle filtering to improve 3D speaker localization accuracy in challenging scenarios.

## Key findings

- Effective front-back ambiguity resolution in planar arrays.
- Improved localization accuracy with particle filtering.
- Successful application to LOCATA challenge tasks.

## Abstract

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.