  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.
