# Rock Crack Types Identification by Machine Learning on the Sound Signal+
## 2 Automatic Recognition Method-Based Spectrograms for Hard Rock Crack Types  
### Basic Idea of the Method  
To perform recognition of tensile and shear cracks based on sound signals, the proposed method in our study employs spectrograms with the combination of the deep learning method and shallow Gaussian process machine learning. The basic idea of this method is shown in Figure 2, which is described in detail as follows. First, spectrograms are chosen as the input indicator for crack type recognition. In traditional sound recognition, time-domain parameters such as the Mel-frequency cepstral coefficient (MFCC) and short-term energy are used as indicators. Compared with the traditional indicators, the spectrogram, which can visualize the time-frequency distribution of the sound signal, is more comprehensive [19; 20]. In addition, sound recognition with spectrogram can decrease the adverse influence of many factors, for example, the artificially chosen step lengths of the sound signals. Consequently, the spectrogram is more powerful than other indicators during sound recognition. Second, the characteristics of the spectrogram are extracted using the pre-trained deep learning method. Deep learning methods perform excellently in the field of image recognition. In traditional deep learning, complex network structures are used to find reasonable classification explanations from a large amount of image data without specifying the specific characteristics of the image. In other words, the best parameters of deep learning models are identified based on a tremendous amount of training samples. If a small training sample is considered, the generalization ability of the models will be weakened, and overfitting problems will be encountered. However, in the field of rock engineering, only a limited number of signals can be obtained, which results in small sample problems. Using small samples to train deep learning networks is not possible. Figure 2: Basic idea of automatic recognition method-based spectrograms: input spectrograms, extract deep characteristics, and construct machine learning model. Compared with shallow machine learning, the greatest advantage of deep learning is the ability to transfer features. For deep learning methods, pre-trained deep learning network models established by a large data set can be used to easily extract features for other datasets, which is called feature-based migration learning. The pre-trained deep learning networks can be conveniently applied to other classification problems. [PERSON] and [PERSON] [21] pointed out that the convolutional neural network model pre-trained on the ImageNet dataset can be used as a good feature extractor for unknown image sets. Many researchers [22; 23; 24; 25] have suggested that using the deep characteristics of images extracted by pre-trained deep learning models results in a better classification performance than that using most artificially described features such as textures and edges. The pre-trained deep learning model can be used as an automatic feature extractor to not only obtain rich features describing the image but also to reduce the burden of manual extraction and facilitate image classification. Third, Gaussian process classification (GPC), a classical machine learning method, is used to establish the implicit mapping relations between the input (the deep characteristic of the tensile and shear spectrograms from the pre-trained deep learning networks) and the output (tensile and shear crack types). Unlike deep learning methods requiring the classification of large data samples, machine learning has a strict statistical theory. Even with small sample problems, GPC exhibits excellent robustness and generalization ability. Thus, based on the output characteristic of the spectrograms, GPC is employed for determining the crack types of the spectrograms. Through the above strategy, the proposed method combines the deep learning method and shallow machine learning method to complement each other.