The LOCATA Challenge Data Corpus for Acoustic Source Localization and Tracking

Löllmann H, Evers C, Schmidt A, Mellmann H, Barfuß H, Naylor PA, Kellermann W (2018)


Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2018

Pages Range: 410-414

Event location: Sheffield GB

ISBN: 978-1-5386-4752-3

DOI: 10.1109/SAM.2018.8448644

Abstract

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.

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How to cite

APA:

Löllmann, H., Evers, C., Schmidt, A., Mellmann, H., Barfuß, H., Naylor, P.A., & Kellermann, W. (2018). The LOCATA Challenge Data Corpus for Acoustic Source Localization and Tracking. In Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) (pp. 410-414). Sheffield, GB.

MLA:

Löllmann, Heinrich, et al. "The LOCATA Challenge Data Corpus for Acoustic Source Localization and Tracking." Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Sheffield 2018. 410-414.

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