Acoustic event detection utilizing event class and localization information

Xianjun Xia

Research output: ThesisDoctoral Thesis

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Abstract

Acoustic event detection aims to detect the event class and to localize the start and the end times of the audio events in various real world scenarios. The audio events are either monophonic or polyphonic depending on whether they are isolated or overlapping with each other. This thesis proposes deep learning solutions utilizing both the event class and event localization information for improving the detection performance when dealing with monophonic or polyphonic sound events. A data augmentation solution is also proposed that addresses limitations in the available training data when deploying deep learning for acoustic event detection.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • The University of Western Australia
Thesis sponsors
Award date1 Jul 2019
DOIs
Publication statusUnpublished - 2019

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