Journal article

Quantifying ultrasonic mouse vocalizations using acoustic analysis in a supervised statistical machine learning framework

Adam P Vogel, Athanasios Tsanas, Maria Luisa Scattoni

SCIENTIFIC REPORTS | NATURE PUBLISHING GROUP | Published : 2019

University of Melbourne Researchers

Grants

Awarded by National Health and Medical Research Council, Australia


Awarded by Italian Ministry of Health


Awarded by Wellcome Trust


Awarded by Health Data Research UK from HDR UK Ltd - UK Medical Research Council


Funding Acknowledgements

A.P.V.' s work was supported in part by the National Health and Medical Research Council, Australia (#1082910, Career Development Fellowship and a Dementia Fellowship #1135683), and by the Alexander von Humboldt Foundation. M.L.S. received funding from the Italian Ministry of Health Grant (GR-2010-2315883). A. T. was previously supported by the Wellcome Trust through a Centre Grant No. 098461/Z/12/Z, "The University of Oxford Sleep and Circadian Neuroscience Institute (SCNi)". The study was also supported by Health Data Research UK, which receives funding from HDR UK Ltd (HDR-5012) funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), the British Heart Foundation, and the Wellcome Trust. The funders had no role in the study or the decision to submit this work to be considered for publication. A.P.V. is Chief Science Officer of Redenlab P/L who undertake speech biomarker research services. A.T. and M.L.S. declare no competing financial interests.