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Machine-Crowd-Expert Model for Increasing User Engagement and Annotation Quality., , and . CHI Extended Abstracts, ACM, (2019)Crowdsourcing Multi-label Audio Annotation Tasks with Citizen Scientists., , , , and . CHI, page 292. ACM, (2019)Multi-Label Sound Event Retrieval Using A Deep Learning-Based Siamese Structure With A Pairwise Presence Matrix., , , , , and . ICASSP, page 3482-3486. IEEE, (2020)SONYC Urban Sound Tagging (SONYC-UST): A Multilabel Dataset from an Urban Acoustic Sensor Network., , , , , , , , and . DCASE, page 35-39. (2019)Active Learning for Efficient Audio Annotation and Classification with a Large Amount of Unlabeled Data., , , and . ICASSP, page 880-884. IEEE, (2019)SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context., , , , , , , , , and 2 other author(s). DCASE, page 16-20. (2020)Eliciting Confidence for Improving Crowdsourced Audio Annotations., , , and . Proc. ACM Hum. Comput. Interact., 6 (CSCW1): 88:1-88:25 (2022)Active Learning for Efficient Audio Annotation and Classification with a Large Amount of Unlabeled Data, , , and . ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), page 880-884. (May 2019)