Online Active Learning For Sound Event Detection
This work addresses annotation efficiency and data drift for sound event detection practitioners, but it is incremental as it builds on existing online active learning methods.
The paper tackled the problem of data annotation and adaptation in sound event detection by introducing new loss functions for online active learning, reducing training time and effort by a factor of 5 on the SONYC dataset.
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.