2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDAUD-4.1
Paper Title IMPROVING THE ROBUSTNESS OF RIGHT WHALE DETECTION IN NOISY CONDITIONS USING DENOISING AUTOENCODERS AND AUGMENTED TRAINING
Authors William Vickers, Ben Milner, University of East Anglia, United Kingdom; Robert Lee, Gardline Geosurvey Limited, United Kingdom
SessionAUD-4: Music Signal Analysis, Processing, and Synthesis 2: Analysis and Processing
LocationGather.Town
Session Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Poster
Topic Audio and Acoustic Signal Processing: [AUD-CLAS] Detection and Classification of Acoustic Scenes and Events
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract The aim of this paper is to examine denoising autoencoders (DAEs) for improving the detection of right whales recorded in harsh marine environments. Passive acoustic recordings are taken from autonomous surface vehicles (ASVs) and are subject to noise from sources such as shipping and offshore construction. To mitigate the noise we apply DAEs and consider how best to train the classifier by augmenting clean training data with examples contaminated by noise. Evaluations find that the DAE improves detection accuracy and is particularly effective when the classifier is trained on data that has itself been denoised rather than using a clean model. Further, testing on unseen noises is also effective particularly for noises that exhibit similar character to noises seen in training.