Algorithms for acoustic source localization and
tracking are essential for a wide range of applications such
as personal assistants, smart homes, tele-conferencing systems,
hearing aids, or autonomous systems. Numerous algorithms have
been proposed for this purpose which, however, are not evaluated
and compared against each other by using a common database so
far. The IEEE-AASP Challenge on sound source localization and
tracking (LOCATA) provides a novel, comprehensive data corpus
for the objective benchmarking of state-of-the-art algorithms
on sound source localization and tracking. The data corpus
comprises six tasks ranging from the localization of a single
static sound source with a static microphone array to the tracking
of multiple moving speakers with a moving microphone array.
It contains real-world multichannel audio recordings, obtained
by hearing aids, microphones integrated in a robot head, a
planar and a spherical microphone array in an enclosed acoustic
environment as well as positional information about the involved
arrays and sound sources represented by moving human talkers
or static loudspeakers.