22 research outputs found
Knowledge Transfer from Weakly Labeled Audio using Convolutional Neural Network for Sound Events and Scenes
In this work we propose approaches to effectively transfer knowledge from
weakly labeled web audio data. We first describe a convolutional neural network
(CNN) based framework for sound event detection and classification using weakly
labeled audio data. Our model trains efficiently from audios of variable
lengths; hence, it is well suited for transfer learning. We then propose
methods to learn representations using this model which can be effectively used
for solving the target task. We study both transductive and inductive transfer
learning tasks, showing the effectiveness of our methods for both domain and
task adaptation. We show that the learned representations using the proposed
CNN model generalizes well enough to reach human level accuracy on ESC-50 sound
events dataset and set state of art results on this dataset. We further use
them for acoustic scene classification task and once again show that our
proposed approaches suit well for this task as well. We also show that our
methods are helpful in capturing semantic meanings and relations as well.
Moreover, in this process we also set state-of-art results on Audioset dataset,
relying on balanced training set.Comment: ICASSP 201
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