{"doi":"10.1145/3412382.3458265","title":"Sound-Adapter","abstract":"The accuracy of an audio classifier drops when it is trained and tested in different conditions aka domains, e.g., different devices, different environments, or their combinations. Previous works have proposed audio domain adaptation techniques for a special case where the training data are recorded with a single source microphone and the model is applied to test data recorded with a different but single target microphone (i.e., single source to single target domain adaptation). In this paper, we solve a more generic and practical problem where the goal is to adapt models that are trained on data from more than one acoustic (i.e., multi-source domain adaptation). Unlike previous works, the proposed method does not assume availability of recording metadata (i.e., domain labels) in the training data---which makes the adaptation problem harder. To solve this, we propose the first multi-task deep neural network architecture to cluster audio samples according to their domain in an unsupervised way. Using the inferred domain information, we perform domain adaptation to remove biases due to domain heterogeneity from the machine learning model. We conduct extensive experiments on an empirical dataset that we collect from five domains as well as on a public dataset. Our results show that the proposed technique has a mean accuracy of 87% for domain discovery in a five domain scenario and its model adaptation step improves acoustic event classification accuracy by up to 21% when compared to state-of-the-art algorithms on datasets containing samples from multiple source domains.","journal":null,"year":2021,"id":216583,"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":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.955,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":552121,"name":"Shahriar Nirjon","orcid":"0000-0003-1443-1146","position":1,"is_corresponding":false},{"id":812915,"name":"Md Tamzeed Islam","orcid":null,"position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":null,"created_at":"2026-07-18T23:53:11.245932Z","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":[]}