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Personalizing the Fitting of Hearing Aids by Learning Contextual Preferences From Internet of Things Data.

, , , , , and . Computers, 7 (1): 1 (2018)

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Low-latency sound-source-separation using non-negative matrix factorisation with coupled analysis and synthesis dictionaries., , and . ICASSP, page 241-245. IEEE, (2015)Low latency sound source separation using convolutional recurrent neural networks., , , , , and . WASPAA, page 71-75. IEEE, (2017)Modeling User Intents as Context in Smartphone-connected Hearing Aids., , , , , and . UMAP (Adjunct Publication), page 151-155. ACM, (2018)Big Data Analytics in Healthcare: Design and Implementation for a Hearing Aid Case Study., , , and . SITIS, page 296-303. IEEE, (2018)Inferring User Intents from Motion in Hearing Healthcare., , , , and . UbiComp/ISWC Adjunct, page 670-675. ACM, (2018)EVOTION - Big Data Supporting Public Hearing Health Policies., and . CBMS, page 7-8. IEEE, (2019)Learning Preferences and Soundscapes for Augmented Hearing., , , , , and . IUI Workshops, volume 2068 of CEUR Workshop Proceedings, CEUR-WS.org, (2018)Associations Between Hearing Performance and Physiological Measures - An Overview and Outlook., , , , , , and . ICIMTH, volume 238 of Studies in Health Technology and Informatics, page 100-103. IOS Press, (2017)Improving Hearing Healthcare with Big Data Analytics of Real-Time Hearing Aid Data., , , , and . SERVICES, page 307-313. IEEE, (2019)Machine Learning for Condition Monitoring and Innovation., , and . ICASSP, page 8067-8071. IEEE, (2019)