# Application of the Iot Network and the Deep Learning Concept in Music Teaching

Authors: Gai Shuangling; Wu Yafen
Journal: Security and Communication Networks
 (2022)

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## Abstract

In order to realize students’ in-depth understanding of music teaching content, it is necessary to reasonably allocate teaching materials according to the teaching content. Therefore, this paper puts forward the application of the concept of the Iot network and deep learning in music teaching. After the teaching resources are vectorized, the distinguishing local registration method of the DLA model is used to extract their features. Based on the dimension of the teaching content, the features of the teaching content are output in the DLA model, the music teaching resources are allocated according to the minimum matching error criterion, the hyperbolic tangent function is taken as the activation function, and the feature error is filtered by maximum aggregation. The experimental results show that the design method can, according to the music teaching content, have more than 80% accuracy in the shallow distribution and deep distribution of teaching materials, and play a positive role in promoting students’ in-depth learning.


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## Body

## 1. Introduction

In the era of knowledge economy and information age, the growth rate and the renewal rate of information and knowledge are faster than any previous periods. Only remembering declarative and procedural knowledge can no longer meet the requirements of the development of the times. Learners need to learn integrated and practical knowledge rather than fragmented and situational facts, and make critical evaluation on knowledge and information. A deep understanding of complex concepts, mastering the ability to use complex concepts to create new products and new knowledge, and becoming a lifelong learner who can guide himself [1–3]are essential. With the development of times, one should learn to move from shallow to deep. The 21st Century Learning Cooperation Organization revised the “21st century learning framework.” As a programmatic document for learning in the 21st century, the new framework mainly includes learning results and support system. The learning results part emphasizes the cultivation of students’ learning and innovation skills (4Cs skills), namely critical thinking, problem-solving, communication skills, cooperation skills, and creativity. The support system includes supporting strategies such as standards and evaluation, curriculum and teaching, teachers’ professional development, and learning environment [4–6]. The new framework is the top-level design of deep learning. There are many overlaps between the deep learning ability and the 21st-century learning framework, which effectively integrates 4Cs skills and the support system content. In 2015, the National Association of state boards of education set deep learning as the national education policy of the 21st century. The horizon report on basic education for three consecutive years from 2014 to 2016 proposed to explore or turn to the “deep learning strategy.” which shows the important position of deep learning as an effective cognitive strategy in basic education [7, 8]. In recent years, deep learning has become a research hotspot in various fields. How to promote the development of students’ deep learning ability has become one of the important fields of educational reform and development.At present, there is still the current situation of “emphasizing results and neglecting process” in music classroom teaching. The teaching evaluation methods are mainly result evaluation and quantitative evaluation such as the paper and pen test, which pay too much attention to knowledge transfer. Teachers do not pay full attention to the students’ knowledge formation and thinking processes, and students’ learning methods tend to accept learning passively [9, 10]. Many students reflect that the music discipline is “difficult”. The fundamental reason is that they “can’t learn.” They only regard music knowledge as isolated and irrelevant facts for memory and simple understanding. They do’t fully understand the essence of the discipline, and can’t effectively transfer knowledge to new situations and solve complex problems [11, 12]. The internal composition of knowledge is composed of three inseparable parts: symbolic representation, logical form, and meaning system. The understanding of knowledge should go beyond a single symbolic representation to the understanding of the subject thought and the meaning carried by symbolic knowledge [13–15]. Shallow learning only stays at the simple surface of the symbolic learning of knowledge, which is difficult to stimulate students' learning initiative, affect students’ in-depth understanding of discipline ideas and methods, and the significance and value of discipline knowledge, and is not conducive to the development of students’ high-order thinking ability [16, 17]. It is necessary to select and screen the teaching materials more carefully if students’ in-depth learning of the teaching content is realized.Based on this, this paper puts forward the research on the application of the concept of deep learning in music teaching, studies the optimal allocation of music teaching resources based on the teaching objectives, promotes students to deepen their understanding of music knowledge from a deeper perspective, and finally verifies the effectiveness of the design method through experimental tests.


## 2. Feature Extraction of Music Teaching Resources and Teaching Content

In order to realize students’ in-depth study of music knowledge, we must first allocate the teaching resources reasonably. Therefore, it is necessary to fully understand the characteristics of the resources. This paper constructs a discriminative local registration model to analyze the adaptability of resource characteristics and in-depth teaching purpose.The DLA model adopts the structure of pcanet [18], in which pcanet learns convolution filter banks through the principal component analysis [19, 20]. Considering that the disadvantages of the principal component analysis will affect the performance of the model, in order to avoid these disadvantages and improve the performance, we use discriminative local registration to construct the DLA model, and use discriminative local registration to learn convolution filter banks to automatically find more effective features in music teaching resources.


### 2.1. Feature Extraction of Music Teaching Resources

Let the size ofN training and teaching resources be m × n. The category label corresponding to each type of resource is y ∈ {1, …, K}, where K is the number of categories. We continuously take the size i × i in each type of resource and vectorize each block. Thus, for the i resource, we have a data matrix:(1)Pj=pi,1,pi,2,⋯,pi,N.Pj is the j-th vectorized music teaching resource. Then, we normalize each resource, subtract their mean value from each resource, and obtain the normalized data matrix(2)Pj′=pi,1′,pi,2′,⋯,pi,N′.Pj′ is the normalized teaching resource with zero mean, and its label is consistent with the label of the whole teaching resource. For all training resources, we splice the corresponding normalized data matrix into a large matrix:(3)P=P1′,P2′,⋯,PN′.On this basis, this paper uses discriminant local registration to find the characteristics of music teaching resources. This feature learning process is shown in Figure1.Figure 1
Feature extraction of teaching resources based on discriminative local registration.According to the method shown in 1, other samples are divided into similar resources and heterogeneous resources according to the labels of other resource samples. This process is also the process of classifying the teaching resources.For classification tasks, samples close to the classification boundary are easy to be misclassified. Therefore, when looking for subspace, the samples close to the classification boundary are more important. In order to consider the influence of these samples and add their importance to the objective function, this paper sets the edge degreec for teaching resources. For the i sample, the edge degree is defined as(4)ci=exp1ni+δt,where ni is the number of heterogeneous resources in a given area near the corresponding feature, δ is the regularization parameter, and t is the scaling coefficient. Formula (4) shows the greater the ni, the greater the ci, which means the closer the sample is to the classification boundary.The results of resource classification based on the feature are obtained.


### 2.2. Feature Extraction of Music Teaching Content

After identifying the characteristics of the teaching resources, this paper uses DLA to extract the characteristics of the teaching content. Assuming that the given music teaching content isx, the binary function is defined as(5)Bx=0,x<0,1,x≥0,where B(x) represents the binarization function of the teaching content x. On this basis, the way of feature extraction is(6)fx=histBx,where hist () is the calculation feature operation. Therefore, we get the characteristics of the teaching content output by a local DLA model, and its dimension depends on its depth.Because the features extracted by the DLA model will be encoded by linear constrained local coding, which is based on over complete basis, the dictionary size is required to be much larger than the dimension of the features; thus, we need to reduce the dimension of the features extracted by the DLA model. Principal component analysis is a general method to reduce dimensions. In this paper, the projection matrix obtained by the principal component analysis is weighted by the reciprocal of the square root of the corresponding eigenvalue(7)U=Λ−1/2fx.In this way, the characteristics of music teaching content after dimensionality reduction are obtained.


## 2.1. Feature Extraction of Music Teaching Resources

Let the size ofN training and teaching resources be m × n. The category label corresponding to each type of resource is y ∈ {1, …, K}, where K is the number of categories. We continuously take the size i × i in each type of resource and vectorize each block. Thus, for the i resource, we have a data matrix:(1)Pj=pi,1,pi,2,⋯,pi,N.Pj is the j-th vectorized music teaching resource. Then, we normalize each resource, subtract their mean value from each resource, and obtain the normalized data matrix(2)Pj′=pi,1′,pi,2′,⋯,pi,N′.Pj′ is the normalized teaching resource with zero mean, and its label is consistent with the label of the whole teaching resource. For all training resources, we splice the corresponding normalized data matrix into a large matrix:(3)P=P1′,P2′,⋯,PN′.On this basis, this paper uses discriminant local registration to find the characteristics of music teaching resources. This feature learning process is shown in Figure1.Figure 1
Feature extraction of teaching resources based on discriminative local registration.According to the method shown in 1, other samples are divided into similar resources and heterogeneous resources according to the labels of other resource samples. This process is also the process of classifying the teaching resources.For classification tasks, samples close to the classification boundary are easy to be misclassified. Therefore, when looking for subspace, the samples close to the classification boundary are more important. In order to consider the influence of these samples and add their importance to the objective function, this paper sets the edge degreec for teaching resources. For the i sample, the edge degree is defined as(4)ci=exp1ni+δt,where ni is the number of heterogeneous resources in a given area near the corresponding feature, δ is the regularization parameter, and t is the scaling coefficient. Formula (4) shows the greater the ni, the greater the ci, which means the closer the sample is to the classification boundary.The results of resource classification based on the feature are obtained.


## 2.2. Feature Extraction of Music Teaching Content

After identifying the characteristics of the teaching resources, this paper uses DLA to extract the characteristics of the teaching content. Assuming that the given music teaching content isx, the binary function is defined as(5)Bx=0,x<0,1,x≥0,where B(x) represents the binarization function of the teaching content x. On this basis, the way of feature extraction is(6)fx=histBx,where hist () is the calculation feature operation. Therefore, we get the characteristics of the teaching content output by a local DLA model, and its dimension depends on its depth.Because the features extracted by the DLA model will be encoded by linear constrained local coding, which is based on over complete basis, the dictionary size is required to be much larger than the dimension of the features; thus, we need to reduce the dimension of the features extracted by the DLA model. Principal component analysis is a general method to reduce dimensions. In this paper, the projection matrix obtained by the principal component analysis is weighted by the reciprocal of the square root of the corresponding eigenvalue(7)U=Λ−1/2fx.In this way, the characteristics of music teaching content after dimensionality reduction are obtained.


## 3. Allocation of Music Teaching Resources Based on Minimum Matching Error Criterion

After obtaining the characteristics of the teaching resources and teaching content, in order to enable students to realize in-depth understanding and learning of music teaching knowledge, this paper matches them with the minimum matching error criterion. Deep neural networks (DNNS) have attracted more and more attention. The most important reason is that it can automatically learn low-level and high-level features at the same time, and get good results on a variety of datasets. Different from traditional neural networks, deep neural networks are generally obtained by stacking multiple restricted Boltzmann machines (RBMs) or constructing regularized automatic coding machines. Using the greedy, layer-by-layer pretraining method and the back propagation (BP) algorithm, the deep neural network obtains better results than the traditional neural network. In addition, when processing data, we often need to consider the topology. A convolution neural network generally uses convolution and aggregation operations as the basic operations, but unlike deep networks, it does not need unsupervised layer-by-layer pretraining strategy. For a deep convolution neural network, it needs to learn many parameters and consume a lot of computation. Therefore, the graphics processing units (GPUs) with the characteristics of parallel high-performance computing are used to train large-scale deep convolution networks. In fact, whether restricted Boltzmann machine or convolutional neural network, back propagation plays a very prominent role in the whole training process. Another appropriate loss function can improve the training speed and the final result at the same time. Generally speaking, Softmax is used as the output layer of a deep neural network, and cross entropy is used as the criterion for constructing the loss function, which makes the posterior probability distribution of Softmax output in the output layer close to the target probability distribution (label class is 1 and other classes are 0). The loss function calculated by Softmax and cross entropy can be understood as the extension of the logistic loss function in multiclass problems. Although this loss function has been widely used in different tasks, it has a disadvantage that it treats all nonlabeled classes consistently. In order to reduce the probability of misclassification, the minimum classification error (MCE) is introduced in the process of training traditional neural networks. In fact, the shallow neural network with minimum classification error has been applied to some recognition tasks and achieved the best results. however, the sigmoid function contained in the construction of the minimum classification error is easy to be saturated, and hence not suitable for the current deep neural network.To this end, we analyze and replace the sigmoid function. The maximum interval minimum classification error uses the hyperbolic tangent function, so that more identification information can be transmitted back to the bottom layer.


### 3.1. Characteristic Back Propagation

Firstly, the obtained features are back propagated in the neural network, and its mode is shown in Figure2.Figure 2
Schematic diagram of the error back propagation algorithm.In the characteristic error back propagation algorithm for teaching materials and teaching contents, combined with the multi-layer stacking structure of the neural network, the chain rule of function derivation is used, and the gradient is reused, such that the parameters of each layer in the neural network can be obtained conveniently and efficiently. With the re-emergence of the neural network, and compared with the previous multi-layer perceptron and other networks with only one hidden layer, the backpropagation algorithm plays a greater role in the network training process.This paper adds the activation function to the neural network, which is also the main means to increase the nonlinearity of the neural network. It is the activation function that makes the neural network different from other linear models and has a more powerful function fitting ability. However, it is also a nonlinear activation function, which makes the objective function of the neural network highly nonconvex, and it is easy to fall into the local minimum point in optimization. Therefore, this paper selects the hyperbolic tangent function as the activation function.The hyperbolic tangent function is another common activation function. It is defined as(8)σε=expε−exp−εexpε+exp−ε,where ε represents the characteristic error of the teaching materials and teaching contents.And whenε = 1, σε has the maximum value of the second derivative, and the effective gain of σε is close to 1. The output variance of the activation function is close to 1, and can keep the output of each layer stable, and can also make the network converge faster in the later stage of training. Unlike sigmoid functions, hyperbolic tangent functions are not always differentiable or bounded. However, in the deep neural network, the hyperbolic tangent function’s activation function has more advantages. When ε ≤ 0, the error is truncated; when ε > 0, the error remains unchanged. In deep networks, larger responses may correspond to useful information, but these responses will not have information back because the error is 0.


### 3.2. Output Connection

When the matching task is input into the convolutional neural network, it should be designed according to the characteristics of music teaching, establish the local connection, and global sharing between the resources and the teaching content. At this time, each neuron of the former layer is connected with the neurons of the latter layer, while the convolution layer is the local area neuron of the former layer connected with the neurons of the latter layer, and the same set of connection parameters are used at different positions of the data. This connection method can fully consider the topological structure and statistical characteristics of the data, and is also consistent with the local receptive field in the anthropology department system. At the same time, it also reduces the number of parameters and connections.Letxi be the i-th feature of layer l − 1, Ki represent the convolution template corresponding to the i-th feature of layer l − 1 to the j-th feature of layer 1, and b is the j-th feature offset of layer l; then,(9)xi=ε∑i=1nxi∗Ki+b,where ∗ is the convolution operation, and the size of the obtained matrix is related not only to K, but also to the convolution step size and the number of edge fills. Here, ’let’s assume that the convolution step is 1 without edge filling; then, the gradient of the loss function of the convolution operation is(10)W=∂ε∂xi.Thus, it provides a basis for subsequent matching.


### 3.3. Feature Aggregation

Based on the above, this paper obtains the final matching result through the feature aggregation operation. By mapping the value in a region of the feature to a value through certain rules, it is mainly to obtain the translation invariance of the space and reduce the size of the feature. Common aggregation operations include average aggregation, maximum aggregation, random aggregation, and fractional aggregation. In the matching task, maximum aggregation can make the network obtain better results; thus, this paper adopts maximum aggregation. As shown in Figure3 the “of the same color” is an aggregation area, and the maximum value is selected as the output. All inputs of the characteristics of the teaching materials form the teaching content according to the position relationship of the corresponding area.Figure 3
Schematic diagram of the maximum aggregation operation.Generally, the aggregation regions do not overlap, and hence the size of the features will be greatly reduced. At the same time, because the polymerization layer has no parameters, the polymerization layer only needs to transmit the error back according to the position where the maximum value is obtained, and the other position errors are zero. In this way, the screening of the matching error is realized by maximum aggregation.


## 3.1. Characteristic Back Propagation

Firstly, the obtained features are back propagated in the neural network, and its mode is shown in Figure2.Figure 2
Schematic diagram of the error back propagation algorithm.In the characteristic error back propagation algorithm for teaching materials and teaching contents, combined with the multi-layer stacking structure of the neural network, the chain rule of function derivation is used, and the gradient is reused, such that the parameters of each layer in the neural network can be obtained conveniently and efficiently. With the re-emergence of the neural network, and compared with the previous multi-layer perceptron and other networks with only one hidden layer, the backpropagation algorithm plays a greater role in the network training process.This paper adds the activation function to the neural network, which is also the main means to increase the nonlinearity of the neural network. It is the activation function that makes the neural network different from other linear models and has a more powerful function fitting ability. However, it is also a nonlinear activation function, which makes the objective function of the neural network highly nonconvex, and it is easy to fall into the local minimum point in optimization. Therefore, this paper selects the hyperbolic tangent function as the activation function.The hyperbolic tangent function is another common activation function. It is defined as(8)σε=expε−exp−εexpε+exp−ε,where ε represents the characteristic error of the teaching materials and teaching contents.And whenε = 1, σε has the maximum value of the second derivative, and the effective gain of σε is close to 1. The output variance of the activation function is close to 1, and can keep the output of each layer stable, and can also make the network converge faster in the later stage of training. Unlike sigmoid functions, hyperbolic tangent functions are not always differentiable or bounded. However, in the deep neural network, the hyperbolic tangent function’s activation function has more advantages. When ε ≤ 0, the error is truncated; when ε > 0, the error remains unchanged. In deep networks, larger responses may correspond to useful information, but these responses will not have information back because the error is 0.


## 3.2. Output Connection

When the matching task is input into the convolutional neural network, it should be designed according to the characteristics of music teaching, establish the local connection, and global sharing between the resources and the teaching content. At this time, each neuron of the former layer is connected with the neurons of the latter layer, while the convolution layer is the local area neuron of the former layer connected with the neurons of the latter layer, and the same set of connection parameters are used at different positions of the data. This connection method can fully consider the topological structure and statistical characteristics of the data, and is also consistent with the local receptive field in the anthropology department system. At the same time, it also reduces the number of parameters and connections.Letxi be the i-th feature of layer l − 1, Ki represent the convolution template corresponding to the i-th feature of layer l − 1 to the j-th feature of layer 1, and b is the j-th feature offset of layer l; then,(9)xi=ε∑i=1nxi∗Ki+b,where ∗ is the convolution operation, and the size of the obtained matrix is related not only to K, but also to the convolution step size and the number of edge fills. Here, ’let’s assume that the convolution step is 1 without edge filling; then, the gradient of the loss function of the convolution operation is(10)W=∂ε∂xi.Thus, it provides a basis for subsequent matching.


## 3.3. Feature Aggregation

Based on the above, this paper obtains the final matching result through the feature aggregation operation. By mapping the value in a region of the feature to a value through certain rules, it is mainly to obtain the translation invariance of the space and reduce the size of the feature. Common aggregation operations include average aggregation, maximum aggregation, random aggregation, and fractional aggregation. In the matching task, maximum aggregation can make the network obtain better results; thus, this paper adopts maximum aggregation. As shown in Figure3 the “of the same color” is an aggregation area, and the maximum value is selected as the output. All inputs of the characteristics of the teaching materials form the teaching content according to the position relationship of the corresponding area.Figure 3
Schematic diagram of the maximum aggregation operation.Generally, the aggregation regions do not overlap, and hence the size of the features will be greatly reduced. At the same time, because the polymerization layer has no parameters, the polymerization layer only needs to transmit the error back according to the position where the maximum value is obtained, and the other position errors are zero. In this way, the screening of the matching error is realized by maximum aggregation.


## 4. Experimental Test

### 4.1. Dataset Settings

The allocation of music teaching resources in this evaluation adopts a supervised classification method; the correct selection of the training set plays a vital role in the classification results. The components of the labeled training set corpus include:(1)
4681 teaching resources with answers in the training set and the test set of COAE 2009 evaluation.(2)
29381 task data with answers in the training set and the test set in COAE 2011 evaluation.(3)
2400 E-teaching resources with answers in the training set and the test set of COAE 2012 evaluation.(4)
3876 teaching resources with answers of 22 music teaching resources in the training set and the test set in 2012nlpcc evaluation.(5)
A certain amount of emotion-related data were collected on the network, and 10000 pieces of training were obtained by manual labeling; after the data of the above parts are combined, a total of 44017 training data are used for the task of teaching resource allocation. The specific information of the dataset is shown in Table1.Table 1
Experimental dataset information.
Dataset groupPositive emotion/articleNegative emotion/articleTotal/piece15000500010000260006000120003700070001400048000800016000The data in Table1 show that the size of the dataset is increasing step by step, and the datasets of the second, third, and fourth groups are increased on the basis of the previous dataset. According to the feature calculation method given in this paper, five groups of experiments are designed to find the best feature combination. The default value of parameter c is 0.01, and the size of the development set is 44017 teaching resources.


### 4.2. Experimental Results

#### 4.2.1. Shallow Characteristic Experiment and Result Analysis

In the experiment, the four groups of data in Table1 are divided into a training set and a test set in two different ways, so as to obtain the classification experimental results. In the first group of experiments, the number of training sets and test sets is increased at the same time, but the proportion remains unchanged. The size of the training set and the test set of each group of data is shown in Table 2.Table 2
Capacity of the training set and the test set in the first group of experiments.
Dataset groupTraining set/pieceTest set/piece190001000212000120031300013004150001500The second group of experiments kept the size of the test set unchanged and only increased the size of the training set. The capacity of the training set and the test set of each group of data is shown in Table3.Table 3
Capacity of the training set and the test set in the second group of experiments.
Dataset groupTraining set/pieceTest set/piece190001000212000100031300010004150001000The experimental results of emotional expression classification in teaching resources are given in turn below.In the first group of experiments, four random number files are generated at the same time. According to the random number file, each group of datasets in Table1 is randomly selected according to the proportion of 9 : 1 to obtain the training set and the test set, such that each pair of training set and test set data in the experiment is not completely consistent, but the proportion is the same. When parameter c takes the optimal value, the classification accuracy results of each dataset are shown in Table 4.Table 4
Experimental results of adding the training set and the test set at the same time.
Statistical informationDataset 1Dataset 2Dataset 3Dataset 4Optimalc value1.561.562.452.45Accuracy (%)82.2686.2485.9286.55It is obvious from Table4 that the accuracy of datasets 2, 3, and 4 is much higher than that of dataset 1, which shows that the classification accuracy can be significantly improved when increasing the data volume of the training set and the test set at the same time, and the accuracy of dataset 4 with the largest increase is also the highest. In addition, the accuracy of dataset 3 in Table 4 is slightly lower than that of dataset 2. The reason is likely that the language phenomenon in the newly added data is not covered by the training set data.In the second group of experiments, only one random number file is used to randomly extract the same 1000 pieces of data from the four groups of datasets as the test set, so as to ensure that the test sets obtained by each group of datasets are exactly the same, and the data volume of the corresponding training set is increased by 2000 pieces in turn. When the parameterc takes the optimal value, the results of the classification accuracy are shown in Table 5.Table 5
Experimental results of adding only training sets.
Statistical informationDataset 1Dataset 2Dataset 3Dataset 4Optimalc value1.562.452.452.45Accuracy (%)82.5682.5984.0685.22From the data changes in Table5, it can be found that the classification accuracy of dataset 2 remains unchanged when 2000 training data are added to dataset 1. The reason may be that there is no language phenomenon of misclassified data in the test set in the newly added training samples. However, under the condition of continuously increasing dataset 3 and set 4 of the training set, the accuracy has been improved to a certain extent. This shows that under the condition that the test set remains unchanged, increasing the amount of data in the training set can significantly improve the classification accuracy.


#### 4.2.2. Deep Characteristic Experiment and Result Analysis

In the process of deep feature extraction, the dimension of the feature needs to be determined. Because its dimension directly affects the effect of classification, this section designs an experiment to determine the best feature dimension. This experiment randomly extracts the first set of dataset in Table1 according to the ratio of 9 : 1 to obtain the training set and the test set. In addition, the characteristic dimension values were set as 25, 50, and 75100150200 in the experiment. Table 6 shows the accuracy results of the first set of datasets under different feature dimensions.Table 6
Experimental results of different characteristic dimensions.
Feature dimension255075100150200Optimalc value5.441.253.625.445.445.44Accuracy (%)76.8677.0376.5078.1579.5678.20It is obvious from Table6 that when the feature dimension is set to 150, the accuracy rate is the highest and the classification effect is the best. When the feature dimension increases to 200, the accuracy begins to decline and the classification effect decreases. Moreover, the larger the feature dimension value in the experiment, the longer the feature learning time. Considering the learning time and the effect at the same time, the best dimension is 150 when using this kind of deep learning features in research. Based on the relationship between the accuracy of deep learning features and the size of the dataset, the experiment is the same as the experiment of the shallow learning feature method. The four groups of data in Table 1 are divided into training sets and test sets, respectively, so as to obtain the classification experimental results. In the same way, it was verified by two groups of experiments.In the first group of experiments, four random number files in the SVM method are used to randomly extract each group of datasets in Table1 according to the ratio of 9 : 1 to obtain the training set and test set, so that each pair of training set and test set data is not completely consistent, but the ratio is the same. When the feature dimension is 150, the classification accuracy results of each dataset are shown in Table 7.Table 7
Experimental results of adding training set and test set at the same time.
Statistical informationDataset 1Dataset 2Dataset 3Dataset 4Optimalc value2.455.445.445.44Accuracy (%)79.0583.2483.7584.09It can be found from Table7 that the classification accuracy of dataset 2 is improved by 4% when training data and test data are added, and the accuracy of datasets 3 and 4 is also improved by 4%∼5% compared with that of dataset 1 under the condition of continuously increasing the amount of training data and test data. These results show that under the condition of only deep learning feature vectors, at the same time, increasing the data volume of the training set and the test set can greatly improve the accuracy of the classification results. When adding the same proportion of data, the change rate of the accuracy of datasets 3 and 4 is much lower than that of dataset 2, which shows that the different specific information of the added data also has an impact on the results. When adding data, you can’t add it blindly. You need to select data with better quality to help improve the accuracy.In the second group of experiments, the random number file in the shallow feature experiment is used. The same 1000 pieces of data are randomly extracted from the four groups of datasets as the test set to ensure that the test sets obtained by each group of datasets are exactly the same, and the data volume of the corresponding training set is increased by 2000 pieces in turn. When the parameterc takes the optimal value and the feature dimension is set to 150, the results of the classification accuracy are shown in Table 8.Table 8
Experimental results of adding only the training set.
Statistical informationDataset 1Dataset 2Dataset 3Dataset 4Optimalc value2.455.442.455.44Accuracy (%)81.5981.5881.4281.93It can be found from the data in Table8 that the classification accuracy of dataset 2 is basically unchanged when 2000 training data are added to dataset 1. The reason may be that there is no misclassified data in the test set in the newly added training samples. In the results of dataset 3, the accuracy rate decreases slightly with the increase of training set, which may be due to the conflict between the new data and the original data and the over fitting phenomenon. However, under the condition of continuing to increase the data of the training set to set 4, the accuracy rate has been slightly improved. This shows that under the condition that the test set remains unchanged, increasing the amount of data in the training set is also a way to improve the classification accuracy, but the over fitting phenomenon should be avoided when increasing the data.


## 4.1. Dataset Settings

The allocation of music teaching resources in this evaluation adopts a supervised classification method; the correct selection of the training set plays a vital role in the classification results. The components of the labeled training set corpus include:(1)
4681 teaching resources with answers in the training set and the test set of COAE 2009 evaluation.(2)
29381 task data with answers in the training set and the test set in COAE 2011 evaluation.(3)
2400 E-teaching resources with answers in the training set and the test set of COAE 2012 evaluation.(4)
3876 teaching resources with answers of 22 music teaching resources in the training set and the test set in 2012nlpcc evaluation.(5)
A certain amount of emotion-related data were collected on the network, and 10000 pieces of training were obtained by manual labeling; after the data of the above parts are combined, a total of 44017 training data are used for the task of teaching resource allocation. The specific information of the dataset is shown in Table1.Table 1
Experimental dataset information.
Dataset groupPositive emotion/articleNegative emotion/articleTotal/piece15000500010000260006000120003700070001400048000800016000The data in Table1 show that the size of the dataset is increasing step by step, and the datasets of the second, third, and fourth groups are increased on the basis of the previous dataset. According to the feature calculation method given in this paper, five groups of experiments are designed to find the best feature combination. The default value of parameter c is 0.01, and the size of the development set is 44017 teaching resources.


## 4.2. Experimental Results

### 4.2.1. Shallow Characteristic Experiment and Result Analysis

In the experiment, the four groups of data in Table1 are divided into a training set and a test set in two different ways, so as to obtain the classification experimental results. In the first group of experiments, the number of training sets and test sets is increased at the same time, but the proportion remains unchanged. The size of the training set and the test set of each group of data is shown in Table 2.Table 2
Capacity of the training set and the test set in the first group of experiments.
Dataset groupTraining set/pieceTest set/piece190001000212000120031300013004150001500The second group of experiments kept the size of the test set unchanged and only increased the size of the training set. The capacity of the training set and the test set of each group of data is shown in Table3.Table 3
Capacity of the training set and the test set in the second group of experiments.
Dataset groupTraining set/pieceTest set/piece190001000212000100031300010004150001000The experimental results of emotional expression classification in teaching resources are given in turn below.In the first group of experiments, four random number files are generated at the same time. According to the random number file, each group of datasets in Table1 is randomly selected according to the proportion of 9 : 1 to obtain the training set and the test set, such that each pair of training set and test set data in the experiment is not completely consistent, but the proportion is the same. When parameter c takes the optimal value, the classification accuracy results of each dataset are shown in Table 4.Table 4
Experimental results of adding the training set and the test set at the same time.
Statistical informationDataset 1Dataset 2Dataset 3Dataset 4Optimalc value1.561.562.452.45Accuracy (%)82.2686.2485.9286.55It is obvious from Table4 that the accuracy of datasets 2, 3, and 4 is much higher than that of dataset 1, which shows that the classification accuracy can be significantly improved when increasing the data volume of the training set and the test set at the same time, and the accuracy of dataset 4 with the largest increase is also the highest. In addition, the accuracy of dataset 3 in Table 4 is slightly lower than that of dataset 2. The reason is likely that the language phenomenon in the newly added data is not covered by the training set data.In the second group of experiments, only one random number file is used to randomly extract the same 1000 pieces of data from the four groups of datasets as the test set, so as to ensure that the test sets obtained by each group of datasets are exactly the same, and the data volume of the corresponding training set is increased by 2000 pieces in turn. When the parameterc takes the optimal value, the results of the classification accuracy are shown in Table 5.Table 5
Experimental results of adding only training sets.
Statistical informationDataset 1Dataset 2Dataset 3Dataset 4Optimalc value1.562.452.452.45Accuracy (%)82.5682.5984.0685.22From the data changes in Table5, it can be found that the classification accuracy of dataset 2 remains unchanged when 2000 training data are added to dataset 1. The reason may be that there is no language phenomenon of misclassified data in the test set in the newly added training samples. However, under the condition of continuously increasing dataset 3 and set 4 of the training set, the accuracy has been improved to a certain extent. This shows that under the condition that the test set remains unchanged, increasing the amount of data in the training set can significantly improve the classification accuracy.


### 4.2.2. Deep Characteristic Experiment and Result Analysis

In the process of deep feature extraction, the dimension of the feature needs to be determined. Because its dimension directly affects the effect of classification, this section designs an experiment to determine the best feature dimension. This experiment randomly extracts the first set of dataset in Table1 according to the ratio of 9 : 1 to obtain the training set and the test set. In addition, the characteristic dimension values were set as 25, 50, and 75100150200 in the experiment. Table 6 shows the accuracy results of the first set of datasets under different feature dimensions.Table 6
Experimental results of different characteristic dimensions.
Feature dimension255075100150200Optimalc value5.441.253.625.445.445.44Accuracy (%)76.8677.0376.5078.1579.5678.20It is obvious from Table6 that when the feature dimension is set to 150, the accuracy rate is the highest and the classification effect is the best. When the feature dimension increases to 200, the accuracy begins to decline and the classification effect decreases. Moreover, the larger the feature dimension value in the experiment, the longer the feature learning time. Considering the learning time and the effect at the same time, the best dimension is 150 when using this kind of deep learning features in research. Based on the relationship between the accuracy of deep learning features and the size of the dataset, the experiment is the same as the experiment of the shallow learning feature method. The four groups of data in Table 1 are divided into training sets and test sets, respectively, so as to obtain the classification experimental results. In the same way, it was verified by two groups of experiments.In the first group of experiments, four random number files in the SVM method are used to randomly extract each group of datasets in Table1 according to the ratio of 9 : 1 to obtain the training set and test set, so that each pair of training set and test set data is not completely consistent, but the ratio is the same. When the feature dimension is 150, the classification accuracy results of each dataset are shown in Table 7.Table 7
Experimental results of adding training set and test set at the same time.
Statistical informationDataset 1Dataset 2Dataset 3Dataset 4Optimalc value2.455.445.445.44Accuracy (%)79.0583.2483.7584.09It can be found from Table7 that the classification accuracy of dataset 2 is improved by 4% when training data and test data are added, and the accuracy of datasets 3 and 4 is also improved by 4%∼5% compared with that of dataset 1 under the condition of continuously increasing the amount of training data and test data. These results show that under the condition of only deep learning feature vectors, at the same time, increasing the data volume of the training set and the test set can greatly improve the accuracy of the classification results. When adding the same proportion of data, the change rate of the accuracy of datasets 3 and 4 is much lower than that of dataset 2, which shows that the different specific information of the added data also has an impact on the results. When adding data, you can’t add it blindly. You need to select data with better quality to help improve the accuracy.In the second group of experiments, the random number file in the shallow feature experiment is used. The same 1000 pieces of data are randomly extracted from the four groups of datasets as the test set to ensure that the test sets obtained by each group of datasets are exactly the same, and the data volume of the corresponding training set is increased by 2000 pieces in turn. When the parameterc takes the optimal value and the feature dimension is set to 150, the results of the classification accuracy are shown in Table 8.Table 8
Experimental results of adding only the training set.
Statistical informationDataset 1Dataset 2Dataset 3Dataset 4Optimalc value2.455.442.455.44Accuracy (%)81.5981.5881.4281.93It can be found from the data in Table8 that the classification accuracy of dataset 2 is basically unchanged when 2000 training data are added to dataset 1. The reason may be that there is no misclassified data in the test set in the newly added training samples. In the results of dataset 3, the accuracy rate decreases slightly with the increase of training set, which may be due to the conflict between the new data and the original data and the over fitting phenomenon. However, under the condition of continuing to increase the data of the training set to set 4, the accuracy rate has been slightly improved. This shows that under the condition that the test set remains unchanged, increasing the amount of data in the training set is also a way to improve the classification accuracy, but the over fitting phenomenon should be avoided when increasing the data.


## 4.2.1. Shallow Characteristic Experiment and Result Analysis

In the experiment, the four groups of data in Table1 are divided into a training set and a test set in two different ways, so as to obtain the classification experimental results. In the first group of experiments, the number of training sets and test sets is increased at the same time, but the proportion remains unchanged. The size of the training set and the test set of each group of data is shown in Table 2.Table 2
Capacity of the training set and the test set in the first group of experiments.
Dataset groupTraining set/pieceTest set/piece190001000212000120031300013004150001500The second group of experiments kept the size of the test set unchanged and only increased the size of the training set. The capacity of the training set and the test set of each group of data is shown in Table3.Table 3
Capacity of the training set and the test set in the second group of experiments.
Dataset groupTraining set/pieceTest set/piece190001000212000100031300010004150001000The experimental results of emotional expression classification in teaching resources are given in turn below.In the first group of experiments, four random number files are generated at the same time. According to the random number file, each group of datasets in Table1 is randomly selected according to the proportion of 9 : 1 to obtain the training set and the test set, such that each pair of training set and test set data in the experiment is not completely consistent, but the proportion is the same. When parameter c takes the optimal value, the classification accuracy results of each dataset are shown in Table 4.Table 4
Experimental results of adding the training set and the test set at the same time.
Statistical informationDataset 1Dataset 2Dataset 3Dataset 4Optimalc value1.561.562.452.45Accuracy (%)82.2686.2485.9286.55It is obvious from Table4 that the accuracy of datasets 2, 3, and 4 is much higher than that of dataset 1, which shows that the classification accuracy can be significantly improved when increasing the data volume of the training set and the test set at the same time, and the accuracy of dataset 4 with the largest increase is also the highest. In addition, the accuracy of dataset 3 in Table 4 is slightly lower than that of dataset 2. The reason is likely that the language phenomenon in the newly added data is not covered by the training set data.In the second group of experiments, only one random number file is used to randomly extract the same 1000 pieces of data from the four groups of datasets as the test set, so as to ensure that the test sets obtained by each group of datasets are exactly the same, and the data volume of the corresponding training set is increased by 2000 pieces in turn. When the parameterc takes the optimal value, the results of the classification accuracy are shown in Table 5.Table 5
Experimental results of adding only training sets.
Statistical informationDataset 1Dataset 2Dataset 3Dataset 4Optimalc value1.562.452.452.45Accuracy (%)82.5682.5984.0685.22From the data changes in Table5, it can be found that the classification accuracy of dataset 2 remains unchanged when 2000 training data are added to dataset 1. The reason may be that there is no language phenomenon of misclassified data in the test set in the newly added training samples. However, under the condition of continuously increasing dataset 3 and set 4 of the training set, the accuracy has been improved to a certain extent. This shows that under the condition that the test set remains unchanged, increasing the amount of data in the training set can significantly improve the classification accuracy.


## 4.2.2. Deep Characteristic Experiment and Result Analysis

In the process of deep feature extraction, the dimension of the feature needs to be determined. Because its dimension directly affects the effect of classification, this section designs an experiment to determine the best feature dimension. This experiment randomly extracts the first set of dataset in Table1 according to the ratio of 9 : 1 to obtain the training set and the test set. In addition, the characteristic dimension values were set as 25, 50, and 75100150200 in the experiment. Table 6 shows the accuracy results of the first set of datasets under different feature dimensions.Table 6
Experimental results of different characteristic dimensions.
Feature dimension255075100150200Optimalc value5.441.253.625.445.445.44Accuracy (%)76.8677.0376.5078.1579.5678.20It is obvious from Table6 that when the feature dimension is set to 150, the accuracy rate is the highest and the classification effect is the best. When the feature dimension increases to 200, the accuracy begins to decline and the classification effect decreases. Moreover, the larger the feature dimension value in the experiment, the longer the feature learning time. Considering the learning time and the effect at the same time, the best dimension is 150 when using this kind of deep learning features in research. Based on the relationship between the accuracy of deep learning features and the size of the dataset, the experiment is the same as the experiment of the shallow learning feature method. The four groups of data in Table 1 are divided into training sets and test sets, respectively, so as to obtain the classification experimental results. In the same way, it was verified by two groups of experiments.In the first group of experiments, four random number files in the SVM method are used to randomly extract each group of datasets in Table1 according to the ratio of 9 : 1 to obtain the training set and test set, so that each pair of training set and test set data is not completely consistent, but the ratio is the same. When the feature dimension is 150, the classification accuracy results of each dataset are shown in Table 7.Table 7
Experimental results of adding training set and test set at the same time.
Statistical informationDataset 1Dataset 2Dataset 3Dataset 4Optimalc value2.455.445.445.44Accuracy (%)79.0583.2483.7584.09It can be found from Table7 that the classification accuracy of dataset 2 is improved by 4% when training data and test data are added, and the accuracy of datasets 3 and 4 is also improved by 4%∼5% compared with that of dataset 1 under the condition of continuously increasing the amount of training data and test data. These results show that under the condition of only deep learning feature vectors, at the same time, increasing the data volume of the training set and the test set can greatly improve the accuracy of the classification results. When adding the same proportion of data, the change rate of the accuracy of datasets 3 and 4 is much lower than that of dataset 2, which shows that the different specific information of the added data also has an impact on the results. When adding data, you can’t add it blindly. You need to select data with better quality to help improve the accuracy.In the second group of experiments, the random number file in the shallow feature experiment is used. The same 1000 pieces of data are randomly extracted from the four groups of datasets as the test set to ensure that the test sets obtained by each group of datasets are exactly the same, and the data volume of the corresponding training set is increased by 2000 pieces in turn. When the parameterc takes the optimal value and the feature dimension is set to 150, the results of the classification accuracy are shown in Table 8.Table 8
Experimental results of adding only the training set.
Statistical informationDataset 1Dataset 2Dataset 3Dataset 4Optimalc value2.455.442.455.44Accuracy (%)81.5981.5881.4281.93It can be found from the data in Table8 that the classification accuracy of dataset 2 is basically unchanged when 2000 training data are added to dataset 1. The reason may be that there is no misclassified data in the test set in the newly added training samples. In the results of dataset 3, the accuracy rate decreases slightly with the increase of training set, which may be due to the conflict between the new data and the original data and the over fitting phenomenon. However, under the condition of continuing to increase the data of the training set to set 4, the accuracy rate has been slightly improved. This shows that under the condition that the test set remains unchanged, increasing the amount of data in the training set is also a way to improve the classification accuracy, but the over fitting phenomenon should be avoided when increasing the data.


## 5. Conclusion

Depth teaching is a teaching process in which all teachers and students deeply participate in the teaching process, and deeply understand and grasp the content of knowledge in the teaching process. Depth teaching does not pursue the depth and difficulty of the teaching content. It does not mean that the deeper the teaching content is, the better the teaching difficulty is. Depth teaching focuses on grasping the deep meaning of the knowledge structure. Depth teaching is not the teaching that stays on the surface of knowledge symbols, but is the teaching at the level of knowledge enrichment. Depth teaching is not a simple superposition of knowledge difficulty and knowledge quantity, but overcomes students’ simple, superficial, and performance learning of knowledge, guides students from simple symbolic learning to in-depth thinking of the discipline based on the internal structure of knowledge, and pays attention to the teaching of discipline literacy. The goal of in-depth teaching is to deeply understand the internal essence of knowledge, actively internalize knowledge, establish personal understanding and opinions, and effectively transfer and apply knowledge to solve practical problems. Students master knowledge through teaching is the basic purpose of teaching rather than the ultimate goal of teaching. Promoting students’ wisdom growth and the ability to solve practical problems is the ultimate goal of teaching. Today’s era is an era in which students are in the least position to lack knowledge than in the past; today’s era is also an era in which students are subjected to many crises that they have never encountered in any previous era. Telecommunications fraud, campus loans,campus bullying, suicide, depression, and other problems have been perplexing every student and parent. The ruthlessness of human nature and the incomplete and trivial cultural consumer goods have stalled the pace of students’ pursuit of happiness, such that they reverse their outlook on life and values and have no vision for the future. However, we can’t help but ask “why are there so many crises, accompanied by the growth of technical knowledge, but not accompanied by the growth of wisdom”? Depth teaching focuses on students’ increasing wisdom while mastering knowledge. It is real learning education. Analyzing depth teaching from the perspective of real learning, it can be clearly seen that depth teaching teaches students to use knowledge to solve all kinds of problems. Knowledge provides students with certain knowledge reserves through indirect ways and improves their own cultural cultivation. However, when students are in really complex real-life situations, they are at a loss in the face of many problems. Knowledge can be used to explain various complex social phenomena, but knowledge itself cannot cultivate students’ ability to deal with complex social phenomena. The flexible application of knowledge depends on the students’ wisdom. The value of in-depth teaching is to transform knowledge learning into personal wisdom. Depth teaching firmly holds the importance of the connection between teaching content and real life, cultivates students’ ability to think and solve problems, and makes students accustomed to looking for the contact point and relationship between these two aspects, so as to better cultivate their ability to deal with complex life problems in the future.



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*Source: 7325660-2022-04-07.xml*