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A large variety of sound sources in the ocean, including biological, geophysical, and man-made, can be simultaneously monitored over instantaneous continental-shelf scale regions via the passive ocean acoustic waveguide remote sensing (POAWRS) technique by employing a large-aperture densely-populated coherent hydrophone array system. Millions of acoustic signals received on the POAWRS system per day can make it challenging to identify individual sound sources. An automated classification system is necessary to enable sound sources to be recognized. Here, the objectives are to (i) gather a large training and test data set of fin whale vocalization and other acoustic signal detections; (ii) build multiple fin whale vocalization classifiers, including a logistic regression, support vector machine (SVM), decision tree, convolutional neural network (CNN), and long short-term memory (LSTM) network; (iii) evaluate and compare performance of these classifiers using multiple metrics including accuracy, precision, recall and F1-score; and (iv) integrate one of the classifiers into the existing POAWRS array and signal processing software. The findings presented here will (1) provide an automatic classifier for near real-time fin whale vocalization detection and recognition, useful in marine mammal monitoring applications; and (2) lay the foundation for building an automatic classifier applied for near real-time detection and recognition of a wide variety of biological, geophysical, and man-made sound sources typically detected by the POAWRS system in the ocean.
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Improving Acoustic Detection and Classification in Mobile and Embedded Platforms: Poster Abstract
Sound detection and classification are critical in many acoustic-based applications. Existing works generally focus on discovering new features and classifiers to improve detection. However, in many scenarios the presence of other sounds may hinder the performance of these sound classifiers. In this work, we take a sound filtering and enhancement approach to improve sound detection for mobile and embedded applications, regardless of the type of detector used.
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- PAR ID:
- 10232674
- Date Published:
- Journal Name:
- Proceedings of the 20th International Conference on Information Processing in Sensor Networks
- Page Range / eLocation ID:
- 402 to 403
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3412382.3458784
