Abstract

Audio-signal acquisition as part of wearable sensing adds an important dimension for applications such as understanding human behaviors. As part of a large study on work place behaviours, we collected audio data from individual hospital staff using custom wearable recorders. The audio features collected were limited to preserve privacy of the interactions in the hospital. A first step towards audio processing is to identify the foreground speech of the person wearing the audio badge. This task is challenging because of the multi-party nature of possible ambulatory interactions, lack of access to speaker information and varying channel and ambient conditions. In this paper, we present a speaker-agnostic approach to foreground detection. We propose a convolutional neural network model to predict foreground regions using a limited set of audio features. We show that these models generalize across the proxy …

Date

May 12, 2019

Authors

Amrutha Nadarajan, Krishna Somandepalli, Shrikanth S Narayanan

Conference

ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Pages

6765-6769

Publisher

IEEE