{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-investigation-of-preprocessing-filters-and","title":"An Investigation of Preprocessing Filters and Deep Learning Methods for Vessel Type Classification With Underwater Acoustic Data","arxiv_id":null,"date":"2022-11-07","proceeding":"IEEE Access 2022 11","authors":["Lucas Cesar Ferreira Domingos","Paulo E. Santos","Phillip S. M. Skelton","Russell S. A. Brinkworth","Karl Sammut"],"abstract":"The illegal exploitation of protected marine environments has consistently threatened the\r\nbiodiversity and economic development of coastal regions. Extensive monitoring in these – often remote\r\n– areas is challenging. Machine learning methods are useful in object detection and classification tasks and\r\nhave the potential to underpin techniques for the development of robust monitoring systems to overcome\r\nthis problem. However, development is hindered due to the limited number of publicly available labelled\r\nand curated datasets. Furthermore, there are relatively few open-source state-of-the-art methods to be used\r\nfor evaluation. This paper presents an investigation of automated classification methods using underwater\r\nacoustic signals to infer the presence and type of vessels navigating in coastal regions. Various combinations\r\nof deep convolutional neural network architectures, and preprocessing filter layers, were evaluated using a\r\nnew dataset based on a subset of the extensive open-source Ocean Networks Canada hydrophone data. Tests\r\nwere conducted in which VGGNet and ResNet networks were applied to classify the input data. The data was\r\npreprocessed using either Constant Q Transform (CQT), Gammatone, Mel spectrogram, or a combination of\r\nthese filters. With over 97% accuracy, using all three preprocessing representations simultaneously yielded\r\nthe most reliable result. However, high accuracies of 94.95% were achieved using CQT as the preprocessing\r\nfilter for a ResNet-based convolutional neural network, providing a trade-off between model complexity\r\nand accuracy; a result that is more than 10% higher than previously reported approaches. This more accurate\r\nclassifier for underwater acoustics could be used as a reliable autonomous monitoring system in maritime\r\nenvironments.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9940921","url_pdf":"https://ieeexplore.ieee.org/iel7/6287639/6514899/09940921.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-investigation-of-preprocessing-filters-and","repo_url":"https://github.com/lucascesarfd/underwater_snd","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"underwater-acoustic-classification","task_name":"Underwater Acoustic Classification"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}