2017/10/26 by Eric L. Ferguson, Ferguson, Eric L., Stefan B. Williams +4
Computer Science · Earth and Planetary Sciences · Engineering · #Audio and Speech Processing (eess.AS) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Sound (cs.SD) #Speech and Audio Processing #Underwater Acoustics Research #Underwater Vehicles and Communication Systems #cs.CV #cs.SD #eess.AS #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1710.10948
5 pages, 5 figures, Final draft of paper submitted to 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 15-20 April 2018 in Calgary, Alberta, Canada. arXiv admin note: text overlap with arXiv:1612.03505
openalex publication_date 2017/10/26 · arxiv created 2017/10/27 · arxiv updated 2017/10/31 · openalex created_date 2022/10/05 · openalex updated_date 2026/07/28
The propagation of sound in a shallow water environment is characterized by boundary reflections from the sea surface and sea floor. These reflections result in multiple (indirect) sound propagation paths, which can degrade the performance of passive sound source localization methods. This paper proposes the use of convolutional neural networks (CNNs) for the localization of sources of broadband acoustic radiated noise (such as motor vessels) in shallow water multipath environments. It is shown that CNNs operating on cepstrogram and generalized cross-correlogram inputs are able to more reliably estimate the instantaneous range and bearing of transiting motor vessels when the source localization performance of conventional passive ranging methods is degraded. The ensuing improvement in source localization performance is demonstrated using real data collected during an at-sea experiment.