Summary of Continual Learning Of Range-dependent Transmission Loss For Underwater Acoustic Using Conditional Convolutional Neural Net, by Indu Kant Deo et al.
Continual Learning of Range-Dependent Transmission Loss for Underwater Acoustic using Conditional Convolutional Neural Net
by Indu Kant Deo, Akash Venkateshwaran, Rajeev K. Jaiman
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP); Fluid Dynamics (physics.flu-dyn)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel deep-learning model for predicting far-field underwater radiated noise is proposed, which incorporates ocean bathymetry data into the input using a range-conditional convolutional neural network. This architecture aims to generalize noise prediction over varying bathymetry profiles worldwide by leveraging continual learning and adaptive management systems. The model effectively captures transmission loss in benchmark scenarios like Dickin’s seamount in the Northeast Pacific. By integrating this approach, real-time end-to-end mapping between near-field ship noise sources and received noise at marine mammal locations is possible. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict the noise that ships make underwater is being developed. Right now, predicting this noise is hard because of things like underwater mountains and changes in ocean depth. New kinds of artificial intelligence models have been created to help with this problem. These models use special types of neural networks to reduce the amount of data they need to process. But there are still challenges when trying to predict how far away the noise will travel and for how long. To fix this, a new type of model is being proposed that includes information about the ocean’s depth in its calculations. This could help make predictions more accurate and useful for protecting marine animals. |
Keywords
» Artificial intelligence » Continual learning » Deep learning » Neural network
