Source Separation of Unknown Numbers of Single-Channel Underwater Acoustic Signals Based on Autoencoders

Due to the influences of ocean environment noise and sea water channels, the separation of underwater acoustic signals is a challenging problem. Some studies have researched the separation of underwater signals by separating the different components of signals with different characteristics, such as spatial orientation information and category differences, in a certain signal transformation domain. Some methods separate signals directly on the feature domain based on expert knowledge [1-3]. The wrap transform was used to separate dispersive time-frequency components in [1]. A depth-based method was proposed in [2], where the modified Fourier transformation of the output power of a plane-wave beamformer was used to separate the signals obtained from a vertical line array. In [3], rigid and elastic acoustic scattering components of underwater target echoes were separated in the fractional Fourier transform domain based on a target echo highlight model. Most other algorithms rely on blind signal separation (BSS) methods [4-10]. In [4], the frequency components of the Detection of Envelope Modulation on Noise (DEMON) spectrum were used to separate signals in different directions via independent component analysis (ICA). According to the main frequency bands of different signals in a linear superposition signal, in [5], bandpass filters were used first, and then eigenvalue decomposition was employed for separation purposes [6] and [7] used the Sawada algorithm and ideal binary masking to separate artificially mixed whale songs.