Simultaneous source separation of unknown numbers of single-channel underwater acoustic signals based on deep neural networks with separator-decoder structure

TL;DR

This paper introduces a deep learning-based method for separating unknown numbers of single-channel underwater acoustic signals using a separator-decoder structure, effectively handling permutation issues and mute channels.

Contribution

It proposes a novel fixed-output neural network model for unknown signal counts and a new evaluation method for mute channels, advancing underwater acoustic source separation.

Findings

Achieves comparable performance to models with known signal numbers

Handles mute channels effectively in separation tasks

Sets new state-of-the-art results in this framework

Abstract

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.