Narwhal (Monodon monoceros) is one of the most elusive marine mammals, due to its isolated habitat in the Arctic region. Tagging is a technology that has the potential to explore the activities of this species, where behavioral information can be collected from instrumented individuals. This includes accelerometer data, diving and acoustic data as well as GPS positioning. An essential element in understanding the ecological role of toothed whales is to characterize their feeding behavior and estimate the amount of food consumption. Buzzes are sounds emitted by toothed whales that are related directly to the foraging behaviors. It is therefore of interest to measure or estimate the rate of buzzing to estimate prey intake. The main goal of this paper is to find a way to detect prey capture attempts directly from accelerometer data, and thus be able to estimate food consumption without the need for the more demanding acoustic data. We develop three automated buzz detection methods based on accelerometer and depth data solely. We use a dataset from five narwhals instrumented in East Greenland in 2018 to train, validate and test a logistic regression model and the state-of-the art machine learning algorithms random forest and deep learning, using the buzzes detected from acoustic data as the ground truth. The deep learning algorithm performed best among the tested methods. We conclude that reliable buzz detectors can be derived from high-frequency-sampling, back-mounted accelerometer tags, thus providing an alternative tool for studies of foraging ecology of marine mammals in their natural environments. We also compare buzz detection with certain movement patterns, such as sudden changes in acceleration (jerks), found in other marine mammal species for estimating prey capture. We find that narwhals do not seem to make big jerks when foraging and conclude that their hunting patterns in that respect might differ from other marine mammals.

独角鲸（Monodon monoceros）是最难以捉摸的海洋哺乳动物之一，因为它在北极地区具有孤立的栖息地。标记是一种有潜力探索该物种活动的技术，在该技术中，可以从有仪器的个人那里收集行为信息。这包括加速度计数据，潜水和声学数据以及GPS定位。了解带齿鲸的生态作用的一个基本要素是表征它们的摄食行为并估计食物消耗量。嗡嗡声是齿鲸发出的声音，与觅食行为直接相关。因此，有兴趣测量或估计嗡嗡声的速率以估计猎物的摄入量。本文的主要目标是找到一种直接从加速度计数据中检测猎物捕获企图的方法，这样就可以估计食物的消耗量，而无需更苛刻的声学数据。我们仅基于加速度计和深度数据开发了三种自动蜂鸣检测方法。我们使用来自2018年在东格陵兰岛的五个独角鲸的数据集，使用从声学数据中检测到的嗡嗡声来训练，验证和测试逻辑回归模型以及最新的机器学习算法随机森林和深度学习真相。在测试的方法中，深度学习算法表现最佳。我们得出的结论是，可靠的蜂鸣器可以从高频采样，背面安装的加速度计标签中获得，从而为研究海洋哺乳动物在自然环境中觅食的生态学提供了另一种工具。我们还将嗡嗡声检测与某些运动模式进行比较，例如在其他海洋哺乳动物物种中发现的加速（突跳）突然变化（急动），以估计猎物的捕获情况。我们发现独角鲸在觅食时似乎并不会造成巨大的抽搐，并得出结论，在这方面，独角鲸的狩猎方式可能与其他海洋哺乳动物不同。