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The separation of single-channel underwater acoustic signals is a challengingproblem with practical significance. Few existing studies focus on the sourceseparation problem with unknown numbers of signals, and how to evaluate theperformance of the systems is not yet clear. In this paper, a deeplearning-based simultaneous separating solution with a fixed number of outputchannels equal to the maximum number of possible targets is proposed to addressthese two problems. This solution avoids the dimensional disaster caused by thepermutation problem induced by the alignment of outputs to targets.Specifically, we propose a two-step learning-based separation model with aseparator-decoder structure. A performance evaluation method with twoquantitative metrics of the separation system for situations with mute channelsin the output channels that do not contain target signals is also proposed.Experiments conducted on simulated mixtures of radiated ship noise show thatthe proposed solution can achieve similar separation performance to thatattained with a known number of signals. The proposed separation model withseparator-decoder structure achieved competitive performance as two modelsdeveloped for known numbers of signals, which is highly explainable andextensible and gets the state of the art under this framework.

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