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Secost: Sequential co-supervision for large scale weakly labeled audio event detection
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.Comment: Accepted IEEE ICASSP 202
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