Abstract

Over the recent years, machine learning techniques have been employed to produce state-of-the-art results in several audio related tasks. The success of these approaches has been largely due to access to large amounts of open-source datasets and enhancement of computational resources. However, a shortcoming of these methods is that they often fail to generalize well to tasks from real life scenarios, due to domain mismatch. One such task is foreground speech detection from wearable audio devices. Several interfering factors such as dynamically varying environmental conditions, including background speakers, TV, or radio audio, render foreground speech detection to be a challenging task. Moreover, obtaining precise moment-to-moment annotations of audio streams for analysis and model training is also time-consuming and costly. In this work, we use multiple instance learning (MIL) to facilitate …

Date

2021

Authors

Rajat Hebbar, Pavlos Papadopoulos, Ramon Reyes, Alexander F Danvers, Angelina J Polsinelli, Suzanne A Moseley, David A Sbarra, Matthias R Mehl, Shrikanth Narayanan

Journal

EURASIP Journal on Audio, Speech, and Music Processing

Volume

2021

Issue

1

Pages

7

Publisher

Springer International Publishing