Large-scale acoustic scene analysis with deep residual networks

Many of the recent advances in audio event detection, particularly on the AudioSet dataset, have focused on improving performance using the released embeddings produced by a pre-trained model. In this work, we instead study the task of training a multi-label event classifier directly from the audio recordings of AudioSet. Using the audio recordings, not only are we able to reproduce results from prior work, we have also confirmed improvements of other proposed additions, such as an attention module. Moreover, by training the embedding network jointly with the additions, we achieve a mean Average Precision (mAP) of 0.392 and an area under ROC curve (AUC) of 0.971, surpassing the state-of-the-art without transfer learning from a large dataset. We also analyze the output activations of the network and find that the models are able to localize audio events when a finer time resolution is needed. In addition, we use this model in exploring multimodal learning, transfer learning, and realtime sound event detection tasks.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Ford, Logan H.

Advisor dc:contributor.advisor

• James Glass and Hao Tang.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123026

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123026