## 4 Methods and Models for Sound Classification in Concrete Pouring Work Monitoring
### Classifiers (CNN and RNN)  
The sound data preprocessed through the procedure described in Section 4.2 are learned through a deep-learning algorithm, thereby building a classification model. Deep-learning algorithms typically used in sound and voice recognition are convolution neural networks (CNNs), recurrent neural networks (RNNs), and long-short-term memory (LSTM). The classification methods and characteristics of the three algorithms were analyzed, and the model most suitable for classification of actual site sound data was determined, applied, and then compared and analyzed in this study. A CNN consists of an input layer, an output layer, and several hidden layers. Because convolution calculations are performed in the hidden layer, this neural network is called a CNN. The major difference from the general neural network structure is that an image is regarded as the input of CNN. In addition, the feature is captured in the image. For example, when looking at a face, it is recognized as a face because there are ears, eyes, nose, and head. Even if the ears are covered with hair, it is recognized as a face based on the consideration of other probable factors on the face. However, the lines and edges, textures, and shapes in an image can be distinguished and contrasted by the eyes or nose that are generally known because it must be able to recognize the eyes and nose in reality. It is also about determining the relative size and location of features. Therefore, the CNN classifies and determines the unique features of an object by learning, regardless of the specific coordinates (location). CNN [45] can be divided into two parts, one that extracts the features of an image and another that classifies the class, as shown in Figure 8. The feature extraction area is composed of several layers of convolution and pooling layers, and the two convolution layers are essential elements that reflect the activation function after applying a filter to the input data. The pooling layers next to the convolution layers are optional. A fully connected layer for image classification is added at the end of the CNN, and a flattened layer that converts image data to an array is located between the part that extracts features of the image and the part that classifies the image. Figure 8: Convolution neural network (CNN). Figure 7: Concrete pouring sound data at a construction site: mel power spectrogram. In CNN, the filter convolutes the input data to extract image features, calculates convolutions, and creates a feature map using the calculation results (Figure 8). The shape of the output data in the convolution layer differs depending on the filter size, stride, applied padding, and maximum pooling size. Consequently, CNN is extremely suitable for feature extraction and training of an image with plural filters, and it is determined that instantaneous sound data can be recognized quickly owing to its effectiveness in recognizing features with the adjacent images while retaining the spatial information of the image. RNNs differ from CNNs as they contain'memory' (in other words, hidden state). Network memory is the information that summarizes the input data so far, in which the network gradually modifies its own memory whenever a new input is available, and the memory left to the network after processing all the inputs becomes the information summarizing the entire sequence. This is similar to the way humans process sequences (Figure 9). RNN is an effective deep-learning technique [46] to learn the sequence through a structure in which a certain part is repeated, which is a basic structure that has W (recurrent weight) from the hidden layer to itself (Figure 10). It is a neural network algorithm that has recurrent weight indicating itself by the directed cycle formed by the connection between units, in which the value(s) of the previous state becomes the input of the next calculation, thereby affecting the result. For example, when recognizing a word or sentence, the RNN refers to the preceding word and letter.