# A Feature Extraction Method Using Auditory Nerve Response for Collapsing Coal-Gangue Recognition
## 1 Introduction  
There are about 1341 billion tons of proven coal resources in China, of which thick coal seam accounts for 44 percent [1]. The longwall top-coal caving (LTCC) mining method, which makes the upper part of the coal seam collapse under the action of gravity, is an efficient and productive method for mining thick coal seams [2]. During the top-coal caving mining process, the workers control the hydraulic support's window to cave the upper part coal based on the recognition of coal and gangue. Therefore, coal-gangue recognition (CGR) is one of key techniques to intelligentize the process of top-coal curing [3]. Since the 1960s, over 20 types of coal-gangue interface identification methods have been developed. Representative methods for coal and gangue interface identification contain artificial \(\gamma\)-ray, nature \(\gamma\)-ray [4, 5], and radar [6]. The artificial \(\gamma\)-ray method measures the top coal's thickness and recognizes the interface between coal and gangue by back scattering. However, researchers have progressivelyabandoned the artificial \(\gamma\)-ray method because it is harmful and has limited penetration [5]. By detecting the gamma ray energy through the remaining coal seam in the roof, a natural \(\gamma\)-ray detection method [7] can measure the thickness of the coal seam, according to the law of its intensity attenuation. Adopting the natural \(\gamma\)-ray detection method is relatively mature for coal-gangue interface recognition. However, the drawback of such a method is the high detection cost [5]. In addition, when the top coal contains few or no radioactive elements or consists of exorbitant gangue, natural \(\gamma\)-ray detection is not practical. In order to measure the top coal's thickness, radar detection [6] uses electromagnetic waves' reflection at the coal-gangue interface. Although radar detection has the advantage of a wide application range, such a method is not useful when the top coal is excessively thick. The problems of high cost of equipment and complex equipment operation impede the practical application of the above methods, and hence some scholars have explored recognition methods based on acoustic and vibration signals to identify coal and gangue. Primarily, three methods of coal-gangue identification have been investigated for top-coal caving mining. One method is based on acoustic signals acquisition, the second method is based on vibration signals technique, the last method is based on a combination of sound and vibration signals. In the first method, a coal-gangue interface identification method based on the mel-frequency cepstrum coefficient (MFCC) and back propagation (BP) neural network is introduced in top-coal caving using acoustic sensors mounted on the tail beam of a hydraulic support [8]. Furthermore, [PERSON] et al. [9] analyzed the permutation entropy obtained by the local mean decomposition of coal and gangue acoustic signals. In the second method, [PERSON] et al. proposed a vibration model of the tail beam using time series analysis models and employed the power spectrum as a feature to recognize coal and gangue [10]. Moreover, on the basis of vibration signals, [PERSON] et al. [11] and [PERSON] et al. [5] recognized coal and gangue by applying information entropy of Hilbert spectrum and stacked sparse autoencoders, respectively. [PERSON] et al. [12] extracted dimensionless parameters, i.e., peak factor, margin factor, and another seven features as feature vector to classify the coal and gangue. In the last method, [PERSON] et al. extracted vibration and acoustic signal features by employing multiple signal processing methods and adopted the multiclass F-score (MF-score) feature dimension reduction method to eliminate the redundant information of those features in top-coal caving [13].