Deep multiple instance learning for foreground speech localization in ambient audio from wearable devices

Deep multiple instance learning for foreground speech localization in ambient audio from wearable devices

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. Howev...

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Bibliographic Details
Main Authors: Rajat Hebbar, Pavlos Papadopoulos, Ramon Reyes, Alexander F. Danvers, Angelina J. Polsinelli, Suzanne A. Moseley, David A. Sbarra, Matthias R. Mehl, Shrikanth Narayanan
Format: Article
Language:English
Published: SpringerOpen 2021-02-01
Series:EURASIP Journal on Audio, Speech, and Music Processing
Subjects:
Foreground speech detection
Multiple instance learning
Wearable audio
Weakly labeled audio
Online Access:https://doi.org/10.1186/s13636-020-00194-0

Internet

https://doi.org/10.1186/s13636-020-00194-0

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