{"url":"/dataset/locata","name":"LOCATA","full_name":"LOCATA","description_markdown":"The **LOCATA** dataset is a dataset for acoustic source localization. It consists of real-world ambisonic speech recordings with optically tracked azimuth-elevation labels.\n\nSource: [Regression and Classification for Direction-of-Arrival Estimation with Convolutional Recurrent Neural Networks](https://arxiv.org/abs/1904.08452)\nImage Source: [https://www.locata.lms.tf.fau.de/files/2018/05/LOCATA_Paper_SAM_Workshop_2018.pdf](https://www.locata.lms.tf.fau.de/files/2018/05/LOCATA_Paper_SAM_Workshop_2018.pdf)","description_withheld":null,"homepage":"https://www.locata.lms.tf.fau.de/","introduced_date":"2018-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"The LOCATA Challenge Data Corpus for Acoustic Source Localization and Tracking","first_author":null,"url":"https://doi.org/10.1109/SAM.2018.8448644"},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Audio","url":"/datasets/modality/audio"}],"tasks":[{"name":"Direction of Arrival Estimation","url":"/task/direction-of-arrival-estimation","datasets_with_task":"/datasets/task/direction-of-arrival-estimation"}],"languages":[],"variants":["LOCATA"],"data_loaders":[],"num_papers_in_archive":22,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}