The increase in shipborne underwater radiated noise (URN) is considered a source of pollution, and should be monitored regularly. We identify knowledge gap in the URN monitoring of vessels of opportunity, where the recording may include URN from multiple vessels simultaneously. To that end, we proposed a method to distinguish, by clustering, between narrow-band tonal lines originated from multiple vessels as received by a single omnidirectional hydrophone. Our clustering is based on feature extraction to classify the tonal lines into vessels by clustering the tonal lines to groups based on their likelihood to be originated from the same source. We present proof-of-concept based on data collected from the "ShipsEar"database and a test-case that shows more than 75% in the true negative and true positive for vessel association.
|Title of host publication||OCEANS 2023 - Limerick, OCEANS Limerick 2023|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - 2023|
|Event||2023 OCEANS Limerick, OCEANS Limerick 2023 - Limerick, Ireland|
Duration: 5 Jun 2023 → 8 Jun 2023
|Name||OCEANS 2023 - Limerick, OCEANS Limerick 2023|
|Conference||2023 OCEANS Limerick, OCEANS Limerick 2023|
|Period||5/06/23 → 8/06/23|
Bibliographical noteFunding Information:
This research was supported by a scholarship sponsored by the Ministry of Science & Technology, Israel.
© 2023 IEEE.
- Multi-view spectral clustering
- Shipping noise
- Underwater radiated noise
ASJC Scopus subject areas
- Computer Networks and Communications
- Hardware and Architecture
- Automotive Engineering
- Management, Monitoring, Policy and Law
- Acoustics and Ultrasonics