Characterizing dynamically varying acoustic scenes from egocentric audio recordings in workplace setting

Autor: Jati, Arindam, Nadarajan, Amrutha, Mundnich, Karel, Narayanan, Shrikanth

Devices capable of detecting and categorizing acoustic scenes have numerous applications such as providing context-aware user experiences. In this paper, we address the task of characterizing acoustic scenes in a workplace setting from audio recordin

Externí odkaz: http://arxiv.org/abs/1911.03843

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Investigating Group-Specific Models of Hospital Workers' Well-Being: Implications for Algorithmic Bias.

Autor: Ravuri, Vinesh, Paromita, Projna, Mundnich, Karel, Nadarajan, Amrutha, Booth, Brandon M., Narayanan, Shrikanth S., Chaspari, Theodora

Publikováno v: International Journal of Semantic Computing; Dec2020, Vol. 14 Issue 4, p477-499, 23p

Modeling inter-individual differences in ambulatory-based multimodal signals via metric learning: a case study of personalized well-being estimation of healthcare workers.

Autor: Paromita P; HUman Bio-Behavioral Signals Lab, Texas A & M University, College Station, TX, United States., Mundnich K; Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, CA, United States., Nadarajan A; Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, CA, United States., Booth BM; Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, CA, United States., Narayanan SS; Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, CA, United States., Chaspari T; HUman Bio-Behavioral Signals Lab, Texas A & M University, College Station, TX, United States.

Publikováno v: Frontiers in digital health [Front Digit Health] 2023 Jun 09; Vol. 5, pp. 1195795. Date of Electronic Publication: 2023 Jun 09 (Print Publication: 2023).