2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM) 2018
DOI: 10.1109/sam.2018.8448644
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The LOCATA Challenge Data Corpus for Acoustic Source Localization and Tracking

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 benchmarkin… Show more

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Cited by 77 publications
(77 citation statements)
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“…Experimental setups 1) Datasets: We tested and empirically validated our method with the LOCATA and the Kinovis multiple speaker tracking (Kinovis-MST) datasets. The LOCATA (a IEEE-AASP challenge for sound source localization and tracking) [36] data were recorded in the Computing Laboratory of the Department of Computer Science of Humboldt University Berlin. The room size is 7.1 m × 9.8 m × 3 m, with a reverberation time T 60 ≈ 0.55 s. We report the results of the development corpus for tasks #3 and #5 with a single moving speaker, and for tasks #4 and #6 with two moving speakers, each task comprising three recorded sequences.…”
Section: Methodsmentioning
confidence: 99%
“…Experimental setups 1) Datasets: We tested and empirically validated our method with the LOCATA and the Kinovis multiple speaker tracking (Kinovis-MST) datasets. The LOCATA (a IEEE-AASP challenge for sound source localization and tracking) [36] data were recorded in the Computing Laboratory of the Department of Computer Science of Humboldt University Berlin. The room size is 7.1 m × 9.8 m × 3 m, with a reverberation time T 60 ≈ 0.55 s. We report the results of the development corpus for tasks #3 and #5 with a single moving speaker, and for tasks #4 and #6 with two moving speakers, each task comprising three recorded sequences.…”
Section: Methodsmentioning
confidence: 99%
“…We conduct a four-way comparison between the Cartesian regression network, two classification networks trained with cross-entropy loss, and a regression network trained using angular loss. Finally, we investigate results on two 3rd-party datasets: LO-CATA [7] and SOFA [8], where our best model reduces angular prediction error by 43% compared to prior methods. Section 2 gives an overview of prior work.…”
Section: Introductionmentioning
confidence: 99%
“…A recent challenge on acoustic source LOCalization And TrAcking (LOCATA), endorsed by the IEEE Audio and Acoustic Signal Processing technical committee, has established a database to encourage research teams to test their algorithms. 115 The challenge dataset consists of acoustic recordings from real-life scenarios. With this data, the performance of source localization algorithms in real-life scenarios can be assessed.…”
Section: Speaker Localization In Reverberant Envi-ronmentsmentioning
confidence: 99%