## 1 Introduction  
Sound source separation is an essential part of machine listening and is beneficial to many real-life audio applications. Speech separation often serves as the front end of high-level processing, such as automatic speech recognition [1, 2] and hearing-aids system [3]. Environmental sound separation is critical in anomalous sound monitoring [4, 5, 6] and intelligent noise assessment [7]. Music separation is also necessary for audio information retrieval and automatic music transcription [8]. Many source separation models with different characteristics have been developed in recent decades and can be divided into two types. One, the computational auditory scene analysis (CASA), mimics the auditory system to separate sources [9], while the other treats the source separation as a supervised learning problem and solves it through developing statistical models [10]. The first approach (CASA models) tends to separate sources based on auditory separation mechanisms. Early models were focused on the extraction of some acoustic attributes, such as pitch [11], onset [12], and amplitude modulation [13] and were subsequently based on the proximity, similarity, or common fate of these attributes. Most of these models are biologically plausible and easy to be explained. However, the current understanding of auditory neuroscience is insufficient to develop a system as intelligent as humans. These models are usually effective for simple stimuli but cannot adapt to natural sources in complex acoustic scenes. The second approach develops supervised models based on the task optimization on a specific training dataset. Thanks to the development of deep learning, recently proposed deep source separation systems have made significant progress [10; 14; 15; 16], which has begun to perform at the human level for natural source separation. The second approach has attracted significant attention because of its excellent performance, and it is also used here to solve the problem of two source separation from monaural recordings. However, unlike many studies focused on pursuing better network structures or parameters for improving separation performances, this study centers on the underlying separation mechanisms of these deep supervised networks. Source separation is one typical example of an ill-posed perceptual problem, especially in monaural recordings without directional cues. There are many assignment solutions of components in a mixture, only one of which matches reality. Therefore, implicit separation mechanisms must be exploited by networks to solve this problem. The research on the network separation mechanism is still in its infancy, especially for deep networks. What about the separation mechanism of trained networks? Do networks rely on general principles similar to humans or categorical patterns to separate sources? In our previous efforts to explain the deep separation network [17; 18] from the perspective of auditory perception, results showed that some general principles had been learned by one specific network spontaneously. However, only one parameter setting and one network structure were tested [17], leaving the open question of whether the auditory behavior of networks is an accident or an inevitable choice when facing the separation task. This paper will answer this question by exploring the mechanisms of the three most advanced deep separation networks. The auditory separation mechanisms are briefly introduced in Section 2. In Section 3, three deep networks with different structures are trained by a universal dataset, including speech, music, and environmental sound. Then the separation mechanisms of different networks are explored through a series of classical psychoacoustic experiments in Section 4. Results demonstrate, to our knowledge for the first time, that different deep networks will spontaneously learn similar auditory-like separation mechanisms without any other prior auditory knowledge on networks.