论文标题
基于深层神经网络具有分离器 - 模块结构的深度神经网络的单通道水下声学信号的同时源分离
Simultaneous source separation of unknown numbers of single-channel underwater acoustic signals based on deep neural networks with separator-decoder structure
论文作者
论文摘要
单通道水下声学信号的分离是一个具有挑战性的问题，具有实际意义。现有的研究很少关注信号数量未知的源分离问题，以及如何评估系统性能尚不清楚。在本文中，提出了一个基于深度学习的同时分离解决方案，固定数量的输出通道等于可能的最大目标数量，以解决这两个问题。该解决方案避免了由输出与目标对齐所引起的置换问题所造成的维度灾难。具体而言，我们提出了一个具有分离器解码器结构的两步基于学习的分离模型。还提出了一种具有分离系统的两个定量指标的绩效评估方法，用于在输出通道中使用静音通道的情况，这些情况还提出了不包含目标信号的情况。在辐射船舶噪声的模拟混合物上进行的实验表明，所提出的解决方案可以实现与已知数量信号达到的相似的分离性能。提出的具有分离器 - 编码器结构的分离模型作为针对已知数量的信号开发的两个模型，实现了竞争性能，这是高度可解释的和可扩展的，并在此框架下获得了最新的状态。
The separation of single-channel underwater acoustic signals is a challenging problem with practical significance. Few existing studies focus on the source separation problem with unknown numbers of signals, and how to evaluate the performance of the systems is not yet clear. In this paper, a deep learning-based simultaneous separating solution with a fixed number of output channels equal to the maximum number of possible targets is proposed to address these two problems. This solution avoids the dimensional disaster caused by the permutation problem induced by the alignment of outputs to targets. Specifically, we propose a two-step learning-based separation model with a separator-decoder structure. A performance evaluation method with two quantitative metrics of the separation system for situations with mute channels in the output channels that do not contain target signals is also proposed. Experiments conducted on simulated mixtures of radiated ship noise show that the proposed solution can achieve similar separation performance to that attained with a known number of signals. The proposed separation model with separator-decoder structure achieved competitive performance as two models developed for known numbers of signals, which is highly explainable and extensible and gets the state of the art under this framework.
