Existing research on audio classification faces challenges in recognizing
attributes of passive underwater vessel scenarios and lacks well-annotated
datasets due to data privacy concerns. In this study, we introduce CLAPP
(Contrastive Language-Audio Pre-training in Passive Underwater Vessel
Classification), a novel model. Our aim is to train a neural network using a
wide range of vessel audio and vessel state text pairs obtained from an
oceanship dataset. CLAPP is capable of directly learning from raw vessel audio
data and, when available, from carefully curated labels, enabling improved
recognition of vessel attributes in passive underwater vessel scenarios.
Model's zero-shot capability allows predicting the most relevant vessel state
description for a given vessel audio, without directly optimizing for the task.
Our approach aims to solve 2 challenges: vessel audio-text classification and
passive underwater vessel audio attribute recognition. The proposed method
achieves new state-of-the-art results on both Deepship and Shipsear public
datasets, with a notable margin of about 7%-13% for accuracy compared to prior
methods on zero-shot task.

在这项研究中，我们介绍了一种名为 CLAPP（Contrastive Language-Audio Pre-training in Passive Underwater Vessel Classification）的新模型，旨在通过使用从一个海洋数据集中获取的广泛的船舶音频和船舶状态文本对来训练神经网络，从而改善对被动水下船舶情景中船舶属性的识别。该模型具有零样本能力，使其能够根据给定的船舶音频预测最相关的船舶状态描述，而无需直接针对该任务进行优化。我们的方法旨在解决船舶音频文本分类和被动水下船舶音频属性识别这两个挑战。所提出的方法在 Deepship 和 Shipsear 公共数据集上取得了新的最先进结果，在零样本任务的准确性上相对于先前方法提升了约 7%-13% 的显著优势。