The LOCATA Challenge: Acoustic Source Localization and Tracking

Abstract

The ability to localize and track acoustic events is a fundamental prerequisite for equipping machines with the ability to be aware of and engage with humans in their surrounding environment. However, in realistic scenarios, audio signals are adversely affected by reverberation, noise, interference, and periods of speech inactivity. In dynamic scenarios, where the sources and microphone platforms may be moving, the signals are additionally affected by variations in the source-sensor geometries. In practice, approaches to sound source localization and tracking are often impeded by missing estimates of active sources, estimation errors, as well as false estimates. The aim of the LOCAlization and TrAcking (LOCATA) Challenge is an open-access framework for the objective evaluation and benchmarking of broad classes of algorithms for sound source localization and tracking. This article provides a review of relevant localization and tracking algorithms and, within the context of the existing literature, a detailed evaluation and dissemination of the LOCATA submissions. The evaluation highlights achievements in the field, open challenges, and identifies potential future directions.

arXiv commentPublished in IEEE/ACM Transactions on Audio, Speech, and Language Processing

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Cited by in corpus (13)

• A Survey of Sound Source Localization with Deep Learning Methods
• MYRiAD: A Multi-Array Room Acoustic Database
• A Review on Sound Source Localization in Robotics: Focusing on Deep Learning Methods
• Assisted RTF-Vector-Based Binaural Direction of Arrival Estimation Exploiting a Calibrated External Microphone Array
• State-Space Estimation of Spatially Dynamic Room Impulse Responses using a Room Acoustic Model-based Prior
• Scalable-Complexity Steered Response Power Mapping based on Low-Rank and Sparse Interpolation
• Blind identification of Ambisonic reduced room impulse response
• Deterministic Sampling on the Circle using Projected Cumulative Distributions
• CNN-based Robust Sound Source Localization with SRP-PHAT for the Extreme Edge
• An experiment on an automated literature survey of data-driven speech enhancement methods
• Completing Sets of Prototype Transfer Functions for Subspace-based Direction of Arrival Estimation of Multiple Speakers
• The trajectoRIR Database: Room Acoustic Recordings Along a Trajectory of Moving Microphones
• Single-Microphone Audio Point Source Discriminative Localization From Reverberation Late Tail Estimation