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In this work we propose approaches to effectively transfer knowledge fromweakly labeled web audio data. We first describe a convolutional neural network(CNN) based framework for sound event detection and classification using weaklylabeled audio data. Our model trains efficiently from audios of variablelengths; hence, it is well suited for transfer learning. We then proposemethods to learn representations using this model which can be effectively usedfor solving the target task. We study both transductive and inductive transferlearning tasks, showing the effectiveness of our methods for both domain andtask adaptation. We show that the learned representations using the proposedCNN model generalizes well enough to reach human level accuracy on ESC-50 soundevents dataset and set state of art results on this dataset. We further usethem for acoustic scene classification task and once again show that ourproposed approaches suit well for this task as well. We also show that ourmethods 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.

Knowledge Transfer from Weakly Labeled Audio using Convolutional Neural Network for Sound Events and Scenes