  Weakly supervised learning algorithms are critical for scaling audio event
detection to several hundreds of sound categories. Such learning models should
not only disambiguate sound events efficiently with minimal class-specific
annotation but also be robust to label noise, which is more apparent with weak
labels instead of strong annotations. In this work, we propose a new framework
for designing learning models with weak supervision by bridging ideas from
sequential learning and knowledge distillation. We refer to the proposed
methodology as SeCoST (pronounced Sequest) -- Sequential Co-supervision for
training generations of Students. SeCoST incrementally builds a cascade of
student-teacher pairs via a novel knowledge transfer method. Our evaluations on
Audioset (the largest weakly labeled dataset available) show that SeCoST
achieves a mean average precision of 0.383 while outperforming prior state of
the art by a considerable margin.
