Online Active Learning For Sound Event Detection
Mark Lindsey Ankit Shah Francis Kubala Richard M. Stern
Data collection and annotation is a laborious, time-consuming prerequisite
for supervised machine learning tasks. Online Active Learning (OAL) is a
paradigm that addresses this issue by simultaneously minimizing the amount of
annotation required to train a classifier and adapting to changes in the data
over the duration of the data collection process. Prior work has indicated that
fluctuating class distributions and data drift are still common problems for
OAL. This work presents new loss functions that address these challenges when
OAL is applied to Sound Event Detection (SED). Experimental results from the
SONYC dataset and two Voice-Type Discrimination (VTD) corpora indicate that OAL
can reduce the time and effort required to train SED classifiers by a factor of
5 for SONYC, and that the new methods presented here successfully resolve
issues present in existing OAL methods.