<i>Wolfset: A High-Quality Underwater Acoustic Dataset for Algorithm Development and Analysis</i>

As data becomes increasingly available, relying on quality datasets for algorithm analysis and development is essential. However, data gathering can be expensive and time-consuming, and this process must be optimized to allow others to reuse data with simplicity and accuracy. The Wolfset is an acoustic dataset gathered using a Bruel &amp; Kjaer type 8104 hydrophone in an anechoic tank usually used for ships' sonar calibration. The name Wolfset is inspired by the Seawolf submarine class, renowned for its advanced sound source detection and classification capabilities. Using an anechoic tank, we can obtain a high-quality dataset representing acoustic sources without undesired external perturbations. In many operating conditions, several outboard motors and an electric motor from a basic remotely controlled ship model were used as sound sources, usually called targets. Then, external transients and noise sources were added to approximate the dataset to the sounds present in real-world conditions. This dataset uses a systematic approach to demonstrate the diversity and accuracy needed for effective algorithm development.随着数据资源日益充裕，依托高质量数据集开展算法分析与研发已成为不可或缺的核心前提。然而，数据采集工作往往成本高昂且耗时耗力，因此亟需优化采集流程，以实现数据的便捷复用与精准共享。Wolfset是一款声学数据集，其采集工作依托布鲁埃尔&克耶尔（Bruel & Kjaer）8104型水听器，在专用于船舶声呐校准的消声水池中完成。该数据集的名称灵感源自海狼级潜艇——该级潜艇以先进的声源探测与分类能力享誉业界。借助消声水池的无回声环境，研究团队可获取无额外外部干扰的高质量声源声学数据集。在多种实验工况下，团队选用多款舷外机与一款基础遥控船模的电动机作为待测声源（即通常所称的目标声源），随后添加外部瞬态噪声与干扰源，使数据集更贴近真实海洋环境中的声学特征。本数据集采用系统化采集方案，充分展现了高效算法研发所需的样本多样性与数据精准度。

搜集汇总

背景与挑战

背景概述

Wolfset是一个专为算法开发和分析设计的高质量水下声学数据集，包含多种操作条件下的声源数据，模拟真实世界的声音条件，适用于声源分类等研究。