Tsai, Yuan-ShengYuan-ShengTsaiFang, Yin-YingYin-YingFangCHI-FANG CHENTsai, Meng-FanMeng-FanTsaiWeng, Shih-HsienShih-HsienWengKuo, Ting-JungTing-JungKuo2025-06-172025-06-172025-03-02https://www.scopus.com/record/display.uri?eid=2-s2.0-105003217013&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/730149This paper proposes using Yet another mobile network (YAMNet) to detect engine noise from ships within the underwater soundscape. This novel approach combines deep learning with sound classification techniques to accurately identify and categorize engine noises from various vessels in the marine environment. It holds significant potential for marine ecological protection, ship management, and noise pollution control applications. YAMNet is a deep learning model based on the MobileNet architecture, designed for audio event detection and classification. MobileNet is a lightweight convolutional neural network (CNN) known for its high computational efficiency and compactness, making it suitable for execution on resource-constrained Internet of Things (IoT) devices through edge computing. YAMNet was originally designed to effectively process audio data from various environments by converting sound signals into spectrograms, which are then processed through the deep learning network for feature extraction and classification. This enables YAMNet to quickly and accurately recognize various audio events, such as natural sounds, mechanical noises, and human activities.Convolutional Neural Networkships noiseUnderwater sound scapeYet Another Mobile Network[SDGs]SDG14conference paper10.1109/UT61067.2025.10947367