Cetaceans are an important part of the ocean ecosystem and are widely distributed in seas across the world. Cetaceans are heavily reliant on acoustic signals for communication. Some Odontoceti can perceive their environments using their sonar system, including the detection, localization, discrimination, and recognition of objects. Acoustic signals are one of the most commonly used types of data for Cetacean research, and it is necessary to develop Cetacean acoustic signal detection methods. This study compared the performance of a manual method, short-time Fourier transform (STFT), and wavelet transform (WT) in Cetacean acoustic signal detection. The results showed that WT performs better in click detection. According to this research, we propose using STFT for whistle and burst-pulse marking and WT for click marking in dataset building. This research will be helpful in facilitating research on the habits and behaviors of groups and individuals, thus providing information to develop methods for protecting species and developing biological resources.

This article presents a summary of three common false-bottom occurrences in echosounder imaging based on an analysis of echosounder data. Utilizing the imaging principle of the echosounder, a comprehensive analysis was conducted and an explanation of each situation’s causes, imaging characteristics, impacts, and solutions is presented. Additionally, the article includes calculations to determine the precise location of the false bottom, which were subsequently validated through actual data collection. To address the two most impactful false-bottom scenarios in target detection, solutions are proposed from two perspectives. By accurately judging the position and imaging characteristics of these false bottoms, the article concludes with an analysis of the causes of false bottoms and presents corresponding solutions. The article aims to facilitate the rapid identification and elimination of false bottoms, thus mitigating their adverse effects on target detection.