Abstract
Knowledge distillation has been successfully applied to various audio tasks, but its potential in underwater passive sonar target classification remains relatively unexplored. Existing methods often focus on high-level contextual information while overlooking essential low-level audio texture features needed to capture local patterns in sonar data. To address this gap, the Structural and Statistical Audio Texture Knowledge Distillation (SSATKD) framework is proposed for passive sonar target classification. SSATKD combines high-level contextual information with low-level audio textures by utilizing an Edge Detection Module for structural texture extraction and a Statistical Knowledge Extractor Module to capture signal variability and distribution. Experimental results confirm that SSATKD improves classification accuracy while optimizing memory and computational resources, making it well-suited for resource-constrained environments.
Abstract (translated)
知识蒸馏技术已经成功应用于各种音频任务，但在水下被动声纳目标分类领域的应用潜力尚有待开发。现有方法往往侧重于高层次的上下文信息，而忽视了捕捉声纳数据中局部模式所必需的基本低层次音频纹理特征。为解决这一问题，提出了结构和统计音频纹理知识蒸馏（SSATKD）框架用于被动声纳目标分类。SSATKD通过利用边缘检测模块提取结构化纹理，并采用统计知识抽取模块捕获信号的变化性和分布特性，将高层次的上下文信息与低层次的音频纹理相结合。实验结果证实，SSATKD在提高分类准确性的同时还能优化内存和计算资源，使其非常适合于资源受限的环境。
URL
https://arxiv.org/abs/2501.01921
