## 1 Introduction  
The distribution of coal and rock in coal mines is intricate. Improper cutting operations will affect the process of coal mining. Coal and rock identification technology is a prerequisite for the realization of shearer automation, which is vital to ensure the safety of personnel and to improve the efficiency of coal mining [1]. Traditional coal and rock identification technologies mainly include \(\gamma\)-ray, image recognition, infrared, and radar detection [2, 3, 4, 5]. These methods are affected by harsh mining environments and immature detection technology. Therefore, there are problems such as difficulty in signal acquisition, low recognition accuracy, and small application range. Because the physical properties of coal and rock are different, there are apparent differences in the sound signals generated by shearer cutting. In the actual process, experienced workers will reasonably adjust the shearer according to the different sound signals generated during cutting. People can collect cutting sound signals in a non-contact way through the sensors [6]. It is conducive to the acquisition and processing of cutting signals. However, the traditional processing methods are not applicable due to the nonlinear and non-stationary characteristics of the collected sound signal [7]. In order to effectively extract information from sound signals, a large number of experts and scholars have proposed many time-frequency domain analysis methods such as wavelet transform (WT), empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD) [8; 9; 10]. [PERSON] applied the wavelet packet decomposition method to process the cutting sound signal and used the fuzzy C-means method for recognition [11]. Meanwhile, she proposed a cutting sound signal recognition method based on EEMD and neural networks [12]. Subject to the selection of wavelet basis, WT is essentially a non-adaptive analysis method. EMD avoids the problem of wavelet basis selection. It is a widely used nonlinear signal processing method. However, this method lacks a rigorous theoretical basis, and the decomposed intrinsic mode function (IMF) has a severe mode mixing problem [13]. The same IMF contains different frequency components. Alternatively, the same frequency component is reflected in different IMFs. The authors of [14] eliminated the mode aliasing problem of EMD by mixing and reconstructing IMF. However, it had not been applied to the actual processing of noisy signals. Aiming at the shortcomings of EMD, the EEMD method suppresses mode mixing by adding white noise in the decomposition process [15]. [PERSON] used EEMD to process the collected vibration signals to realize the fault diagnosis of rolling bearing under random noise [16]. This method has achieved good results to a certain extent. Nevertheless, it can not eliminate the influence of introduced noise, and the effect of signal reconstruction is poor [17]. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) adds adaptive noise to the processing process. It obtains the IMF components by calculating the unique residual signal [18]. [PERSON] applied CEEMDAN to process the arc signal generated by welding and obtained a series of IMF components [19]. When the CEEMDAN method handles signals, the IMF component is easily affected by noise. Therefore, [PERSON] proposed a noise reduction strategy through the reweighting and reconstruction of IMF components [20]. The CEEMDAN algorithm effectively reduces the signal reconstruction error. However, it still has the problems of residual noise and false mode components. Therefore, this paper adopts an improved CEEMDAN (ICEEMDAN) algorithm [21]. This method can effectively solve the problems in the decomposition process and obtain better IMF components. In recent years, nonlinear dynamic methods such as fuzzy entropy, energy entropy, and permutation entropy can effectively extract the characteristic information of the signals.