Details "Knowledge Transfer From Weakly Labeled Audio Using Convolutional Neural Network For Sound Events And Scenes" Anurag Kumar, Maksim Khadkevich, Christian Fügen

ICASSP 2018

https://arxiv.org/pdf/1711.01369.pdf

Check out this webpage "http://www.cs.cmu.edu/~alnu/TLWeak.htm" for more results and details

This code provides the bare minimum to obtain audio representations using Deep CNN models trained on weakly labeled data (Audioset - Balanced set)

1. call the main function in feat_extractor - returns 1024 or 527 dimensional features

2. It will work with audio of any duration but I would suggest to pad it to make it at least 1.5 seconds for now.

3. You can turn on gpu use by 'usegpu' variable. Although, for very long audio (more than a few minutes) you might end up getting gpu memory error.

The trained CNN is used to learn meaningful representations for a given audio recording. The classification task can be done by training another classifier on these representations (linear SVMs in this case).

4. class names and id in classes_id_name.txt for the 527 sounds in audioset over which the model was trained.

6. Doubts??? - send me an email.