{"doi":"10.23919/eusipco55093.2022.9909522","title":"Wake-Cough: cough spotting and cougher identification for personalised long-term cough monitoring","abstract":"We present ‘wake-cough’, an application of wake-word spotting to coughs using a Resnet50 and the identification of coughers using i-vectors, for the purpose of a long-term, personalised cough monitoring system. Coughs, recorded in a quiet ( <tex xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">$73\\pm 5\\ \\text{dB}$</tex> ) and noisy ( <tex xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">$34\\pm 17\\ \\text{dB}$</tex> ) environment, were used to extract i-vectors, x-vectors and d-vectors, used as features to the classifiers. The system achieves 90.02% accuracy when using an MLP to discriminate between 51 coughers using 2-sec long cough segments in the noisy environment. When discriminating between 5 and 14 coughers using longer (100 sec) segments in the quiet environment, this accuracy improves to 99.78% and 98.39% respectively. Unlike speech, i-vectors outperform x-vectors and d-vectors in identifying coughers. These coughs were added as an extra class to the Google Speech Commands dataset and features were extracted by preserving the end-to-end time-domain information in a trigger phrase. The highest accuracy of 88.58% is achieved in spotting coughs among 35 other trigger phrases using a Resnet50. Thus, wake-cough represents a personalised, non-intrusive cough monitoring system, which is power-efficient as on-device wake-word detection can keep a smartphone-based monitoring device mostly dormant. This makes wake-cough extremely attractive in multi-bed ward environments to monitor patients' long-term recovery from lung ailments such as tuberculosis (TB) and COVID-19.","journal":"2022 30th European Signal Processing Conference (EUSIPCO)","year":2022,"id":298481,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.956,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":987538,"name":"Marisa Klopper","orcid":"0000-0002-9318-8289","position":1,"is_corresponding":false},{"id":660389,"name":"Byron W P Reeve","orcid":"0000-0002-3332-3477","position":2,"is_corresponding":false},{"id":89652,"name":"Robin M. Warren","orcid":"0000-0001-5741-7358","position":3,"is_corresponding":false},{"id":244855,"name":"Grant Theron","orcid":"0000-0002-9216-2415","position":4,"is_corresponding":false},{"id":308896,"name":"Andreas H. Diacon","orcid":"0000-0001-8641-6792","position":5,"is_corresponding":false},{"id":987539,"name":"Thomas Niesler","orcid":"0000-0002-7341-1017","position":6,"is_corresponding":false},{"id":987537,"name":"Madhurananda Pahar","orcid":"0000-0002-5926-0144","position":0,"is_corresponding":true}],"reference_count":33,"raw_metadata":null,"created_at":"2026-07-19T00:31:36.269611Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}