{"url":"/dataset/ears-reverb","name":"EARS-Reverb","full_name":null,"description_markdown":"The EARS-Reverb dataset uses real recorded room impulse responses (RIRs) from multiple public datasets (ACE-Challenge, AIR, ARNI, BRUDEX, dEchorate, DetmoldSRIR, and Palimpsest).  All RIRs are fullband, and a randomly selected channel for multi-channel recordings is used. The reverberant speech is generated by convolving the clean speech with the RIR. To avoid a time delay between the reverberant and clean speech signal caused by the direct path of the RIR, the beginning of the RIR is cut off up to the index with the highest amplitude. Only RIRs with an RT60 reverberation time that does not exceed 2 s are used. Finally, the loudness of the reverberant speech is normalized to the loudness of the clean speech using the loudness K-weighted relative to full scale (LKFS).","description_withheld":null,"homepage":"https://sp-uhh.github.io/ears_dataset/","introduced_date":"2024-06-10","introduced_date_note":null,"introduced_by":{"paper":"/paper/ears-an-anechoic-fullband-speech-dataset","title":"EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation","first_author":"Julius Richter","url":null},"license":{"name":"CC-NC 4.0 International license","url":"https://github.com/facebookresearch/ears_dataset/blob/main/LICENSE"},"modalities":[{"name":"Speech","url":"/datasets/modality/speech"}],"tasks":[{"name":"Speech Dereverberation","url":"/task/speech-dereverberation","datasets_with_task":"/datasets/task/speech-dereverberation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["EARS-Reverb"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/speech-dereverberation-on-ears-reverb","task":"Speech Dereverberation","dataset_variant":"EARS-Reverb","rows":1,"metrics":["PESQ-WB","SI-SDR","ESTOI","SIGMOS","MOS Reverb"],"first_row_in_archive_order":{"model":"SGMSE+","paper":"/paper/speech-enhancement-and-dereverberation-with","metrics":{"ESTOI":"0.85","MOS Reverb":"4.73","PESQ-WB":"3.03","SI-SDR":"5.79","SIGMOS":"3.49"},"code_links":[{"title":"sp-uhh/sgmse","url":"https://github.com/sp-uhh/sgmse"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/speech-enhancement-and-dereverberation-with","title":"Speech Enhancement and Dereverberation with Diffusion-based Generative Models","date":"2022-08-11","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+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."}