Abstract

Identification of the acoustic environment from an audio recording, also known as acoustic scene classification, is an active area of research. In this paper, we study dynamically-changing background acoustic scenes from the egocentric perspective of an individual in a workplace. In a novel data collection setup, wearable sensors were deployed on individuals to collect audio signals within a built environment, while Bluetooth-based hubs continuously tracked the individual's location which represents the acoustic scene at a certain time. The data of this paper come from 170 hospital workers gathered continuously during work shifts for a 10 week period. In the first part of our study, we investigate temporal patterns in the egocentric sequence of acoustic scenes encountered by an employee, and the association of those patterns with factors such as job-role and daily routine of the individual. Motivated by evidence of …

Date

2021

Authors

Arindam Jati, Amrutha Nadarajan, Raghuveer Peri, Karel Mundnich, Tiantian Feng, Benjamin Girault, Shrikanth Narayanan

Journal

IEEE/ACM Transactions on Audio, Speech, and Language Processing

Volume

29

Pages

756-769

Publisher

IEEE