{"url":"/dataset/icbhi-respiratory-sound-database","name":"ICBHI Respiratory Sound Database","full_name":"The Respiratory Sound database -  ICBHI 2017 Challenge","description_markdown":"The Respiratory Sound database was originally compiled to support the scientific challenge organized at Int. Conf. on Biomedical Health Informatics - ICBHI 2017.\r\n\r\nThe database consists of a total of 5.5 hours of recordings containing 6898 respiratory cycles, of which 1864 contain crackles, 886 contain wheezes, and 506 contain both crackles and wheezes, in 920 annotated audio samples from 126 subjects.\r\n\r\nThe cycles were annotated by respiratory experts as including crackles, wheezes, a combination of them, or no adventitious respiratory sounds. The recordings were collected using heterogeneous equipment and their duration ranged from 10s to 90s. \r\n\r\nFor more information about the dataset, the annotation files, or to download it, please visit [the challenge official page](https://bhichallenge.med.auth.gr/ICBHI_2017_Challenge).","description_withheld":null,"homepage":"https://bhichallenge.med.auth.gr/ICBHI_2017_Challenge","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Audio","url":"/datasets/modality/audio"},{"name":"Biomedical","url":"/datasets/modality/biomedical"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Audio Classification","url":"/task/audio-classification","datasets_with_task":"/datasets/task/audio-classification"},{"name":"Lung Sound Classification","url":"/task/lung-sound-classification","datasets_with_task":"/datasets/task/lung-sound-classification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["ICBHI Respiratory Sound Database"],"data_loaders":[],"num_papers_in_archive":21,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/audio-classification-on-icbhi-respiratory","task":"Audio Classification","dataset_variant":"ICBHI Respiratory Sound Database","rows":25,"metrics":["ICBHI Score","Sensitivity","Specificity"],"first_row_in_archive_order":{"model":"ADD","paper":"/paper/adaptive-differential-denoising-for","metrics":{"ICBHI Score":"65.53","Sensitivity":"45.94","Specificity":"85.13"},"code_links":[{"title":"deegy666/add-rsc","url":"https://github.com/deegy666/add-rsc"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/lung-sound-classification-on-icbhi","task":"Lung Sound Classification","dataset_variant":"ICBHI Respiratory Sound Database","rows":3,"metrics":["Accurcay "],"first_row_in_archive_order":{"model":"RDLINet","paper":"/paper/rdlinet-a-novel-lightweight-inception-network","metrics":{"Accurcay ":"99.6"},"code_links":[{"title":"rsarka34/RDLINet","url":"https://github.com/rsarka34/RDLINet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/adaptive-differential-denoising-for","title":"Adaptive Differential Denoising for Respiratory Sounds Classification","date":"2025-06-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/patient-aware-feature-alignment-for-robust","title":"Patient-Aware Feature Alignment for Robust Lung Sound Classification:Cohesion-Separation and Global Alignment Losses","date":"2025-05-28","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/cycleguardian-a-framework-for-automatic","title":"CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning","date":"2025-02-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/bts-bridging-text-and-sound-modalities-for","title":"BTS: Bridging Text and Sound Modalities for Metadata-Aided Respiratory Sound Classification","date":"2024-06-10","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/masked-modeling-duo-towards-a-universal-audio","title":"Masked Modeling Duo: Towards a Universal Audio Pre-training Framework","date":"2024-04-09","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":7,"samples_unverified":0,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/stethoscope-guided-supervised-contrastive","title":"Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classification","date":"2023-12-15","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/adversarial-fine-tuning-using-generated","title":"Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance","date":"2023-11-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/rdlinet-a-novel-lightweight-inception-network","title":"RDLINet: A Novel Lightweight Inception Network for Respiratory Disease Classification Using Lung Sounds","date":"2023-07-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/patch-mix-contrastive-learning-with-audio","title":"Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification","date":"2023-05-23","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":1,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/supervised-contrastive-learning-for-3","title":"Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning","date":"2022-10-27","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/a-domain-transfer-based-data-augmentation","title":"A DOMAIN TRANSFER BASED DATA AUGMENTATION METHOD FOR AUTOMATED RESPIRATORY CLASSIFICATION","date":"2022-04-27","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/arsc-net-adventitious-respiratory-sound","title":"ARSC-Net: Adventitious Respiratory Sound Classification Network Using Parallel Paths with Channel-Spatial Attention","date":"2022-01-14","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/lungattn-advanced-lung-sound-classification","title":"LungAttn: advanced lung sound classification using attention mechanism with dual TQWT and triple STFT spectrogram","date":"2021-10-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/lung-sound-classification-using-co-tuning-and","title":"Lung Sound Classification Using Co-tuning and Stochastic Normalization","date":"2021-08-04","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/respirenet-a-deep-neural-network-for","title":"RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting","date":"2020-10-31","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/a-lightweight-cnn-model-for-detecting","title":"A Lightweight CNN Model for Detecting Respiratory Diseases from Lung Auscultation Sounds using EMD-CWT-based Hybrid Scalogram","date":"2020-09-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/lungrn-nl-an-improved-adventitious-lung-sound","title":"LungRN+NL: An Improved Adventitious Lung Sound Classification Using Non-Local Block ResNet Neural Network with Mixup Data Augmentation","date":"2020-08-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/respiratory-diseases-recognition-through","title":"Respiratory diseases recognition through respiratory sound with the help of deep neural network","date":"2020-04-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/lungbrn-a-smart-digital-stethoscope-for","title":"LungBRN: A Smart Digital Stethoscope for Detecting Respiratory Disease Using bi-ResNet Deep Learning Algorithm","date":"2019-12-05","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":19,"samples_ran":11,"samples_unverified":8,"pointer_only_for_licence":10,"papers_with_no_sample_that_ran":1,"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."}