# Application of Artificial Neural Network in College-Level Music Teaching Quality Evaluation

Authors: Yantao Shi
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Art is a very practical activity, especially music art. Quality music education at colleges and universities is vital for the ideological and moral schooling of students. Owing to the rapid expansion of music education at graduate level, the building of therapeutic evaluation of music instruction is critical. In fact, most of the music instruction in colleges and universities has not built a scientific and appropriate evaluation system based on the actual teaching quality of the classroom. This work combines the emerging neural network (NN) technology with standard method of music teaching evaluation and proposes a novel method—Music Teaching Quality Evaluation Network (MTQEN). To effectively improve performance of the model, the method uses a one-dimensional convolutional neural network (1D-CNN) being optimized from three aspects: expanding the receptive field, reducing training parameters, and enhancing operationality. Instead of the traditional convolution layer, the dilated convolution layer is utilized to increase scope of local receptive field and to improve the feature extraction efficacy. To improve training rate and to eliminate dependency on batch size, the filter response normalization (FRN) is used. Moreover, global pooling is used to reduce the requisite training parameters and to improve the training efficiency. Results of the evaluation show that performance of MTQEN in term accuracy (95.6%) and recall (93.3%) is better than the other contemporary models. The method proposed has great significance in pedagogy of music and arts, whereas the network designed may be enhanced to effectively evaluate teaching in related domains as well.


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## Body

## 1. Introduction

Higher music education started long before, but its development was relatively slower than other arts education [1]. In the late 1970s, music education in ordinary colleges and universities sprung up like mushrooms after a spring rain. Art education is the primary content and method of implementing aesthetic education in schools, and it is also a powerful means to strengthen the construction of spiritual civilization, subtly improve the moral standard of students, cultivate noble sentiments, and promote the healthy development of intelligence and body. Art education plays a vital role in the education of students across the curriculum. One of the most significant components of a humanistic education is the inclusion of art education as a means of imparting aesthetic instruction to pupils. Its primary goal is to increase students’ creative quality and aesthetic ability, foster students’ humanistic spirit, cultivate students’ moral emotion, develop students’ intelligence and innovative capacity, and promote total physical and mental development. Instead of improving the musical abilities and humanistic qualities of all students, music education at ordinary colleges and universities focuses on popularizing music education rather than on professional music education [2–6].Today’s music education in colleges is booming, but some aspects of the construction are still immature. Music classroom teaching objectives, teaching content selection, and teaching effect evaluation have different degrees of defects in classroom teaching performance evaluation and research. How to optimize evaluation of music classroom teaching in colleges is top priority of current college music education. In fact, most of music teaching has not established a scientific evaluation system based on actual teaching quality of the classroom. It is why the evaluation criteria of some music colleges are not satisfactory [7–11].Music education is an educational subject offered at college level for the students who are not majoring in music. The pedagogy of music includes classroom teaching, music art practice, and campus music cultural activities. Music classroom teaching has become the most popular way of school music education for ordinary college students because of its strong systematic teaching content, wide knowledge coverage, and gradual knowledge difficulties. To a certain extent, evaluation of teaching quality of music classrooms has changed the method of summative evaluation that only emphasized the results and despised the process in the past. But in view of current situation, university admission is mainly based on the result obtained through examination-oriented education. Therefore, students who are long been accustomed to the performance-based evaluation system often succeed in getting university admission. However, it is difficult to apply formative evaluation methods to classroom teaching evaluation [12–16]. Hence, a systematic research on evaluation method for music teaching quality in colleges is pressingly needed.This research work intends to carry out an in-depth systematic evaluation method by mingling theory and practice in the domain of music teaching. The quality evaluation methods of music teaching are combined with neural networks to design an effective teaching quality evaluation system. The method proposed will allow administrators to adjust teaching plan according to quality evaluation and to improve quality of music teaching and will help teacher to instruct more comprehensively.


## 2. Related Work

Literature believes that aesthetic music education philosophy is a music education philosophy that unifies music education behavior and thought, and its appearance is closely related to social economy, cultural background, and education status quo. Based on its many educational functions, literature [17] thinks that music aesthetics is becoming more and more highly recognized by professionals in the field of human education. Aesthetic education in music will grow in importance and reach new heights as educational standards improve. The research study [18] examines the current state of music education at traditional colleges and universities, as well as the underlying causes of this state of affairs, with the goal of developing a more scientific approach to music education. In addition, educational principles must be rethought, teachers must be better trained, and textbooks for college students must be chosen. Studies [19] highlight the main issues and shortcomings of present public music education at colleges and universities, as well as reform proposals. Literature [20] investigated the present state of music education in colleges and universities and discovered certain factors that are preventing the growth of music education. In light of today’s society’s development trends and the need for all-around development of high-quality talent, certain solutions are presented. Musical knowledge and abilities obtained by students may only be shown by participation in music practice activities [21]. In addition, they are able to identify and resolve issues in the activities so that the overall quality of pupils may be improved.Hamann et al. [22] propose to use the concept of development to evaluate music teaching and to explore in combination with actual situation to promote development. Literature [23] emphasizes need to conduct independent evaluation of music teaching and believes that teaching evaluation is for screening and selection and should focus on individuality and comprehensiveness. Literature [24] takes the psychological characteristics of different ages as a perspective and proposes a music teaching evaluation system that adapts to the characteristics of students’ psychological development. Literature [25] focuses on content of music education philosophy, and the main content is combined with the case analysis of music education and teaching. Its main point of view is to evaluate quality of music education in education administrative departments at all levels and in various schools. The focus of the evaluation is on teachers, including teachers’ specifications, knowledge, ability structure, psychological quality, and teacher training and training. The work in [26] focuses on an overall overview of music teaching evaluation, introducing the definition, characteristics, principles, and methods of evaluation. For a systematic assessment, the research introduces the use of art growth record bag evaluation, the detection of students’ music learning level, and the reevaluation of the assessment. Regarding the teaching evaluation of music teachers, it firstly emphasizes the establishment of evaluation standards, followed by an overview of the content of music teachers’ teaching evaluation. Finally, the evaluation methods are proposed. Literature [27] proposes to pay attention to students and achieve comprehensive diversification of evaluation indicators and content. The evaluation content indicators focus on the learning process rather than specific outcomes. Moreover, detailed implementations of teaching evaluation, from the perspective of students and teachers, are clearly mentioned. Different means like questionnaires, tests, and mutual evaluation are exploited to gather students’ evaluation information. Literature [28] made a theoretical exposition of music teaching evaluation and comprehensively analyzed what methods teachers and students use to give corresponding evaluations to music teaching. Literature [29] selected three perspectives to guide the evaluation of music teaching and gave a comprehensive exploration and analysis of the teaching evaluation implemented in music classrooms of various natures. Literature [30] focused on analyzing the new requirements for teachers and students’ evaluation methods under the change from focusing on paper and pencil scores to focusing on the impact of comprehensive quality. From the perspective of music classroom teaching evaluation, it discusses the importance of music teaching evaluation to curriculum reform. Literature [31] deeply explored the requirements for teaching evaluation concepts, methods, and characteristics under the new curriculum standards and emphatically expounded the suggestions for reasonable and effective teaching evaluation methods. In the current AI era, focus has been shifted to the use of improved methods to enhance music teachings and to evaluate candidate machine learning algorithms for optimum performance [32, 33]. The research work in [34] investigates the effectiveness of deep learning for feasible assessing the methodology of music teaching. Also, the authors have used the four well-known machine learning classifiers for the evaluation of students’ curriculum. To improve music majors, a Music Education and Teaching based on AI (MET-AI) technique is suggested in [35]. Similarly, to generate various genres of music and to distinguish music signals, an LSTM-based method is proposed in [36].


## 3. Method

This work designs a one-dimensional convolutional neural network model for the quality evaluation of music teaching in colleges. The optimized network has greatly improved in terms of training performance, convergence speed, and test accuracy.


### 3.1. Convolutional Neural Network

Normally, CNN is preferred for 2D data; however, as stated in [37], a 2D-CNN model with a number of parameters and complexity is likely to suffer from overfitting. Therefore, the 1D-CNN is used with ReLU to speed up the forward propagation and to avoid gradient explosion. The first half of CNN model stacks several groups of convolutional layers and pooling layers to form the feature extraction part, and finally, the FC layer is generally connected as a classifier to form feature output part. The basic structure of CNN is similar to that of ordinary neural network, and its basic structure is composed of neurons. However, the convolutional neural network has improved the form and function of the layer, which greatly improves the classification and recognition ability of the network model.Data features are extracted by filtering input data via the convolutional layer. Input data studied in this paper is a one-dimensional signal. The convolution kernel of the current convolution layer performs a convolution operation on input. The operation is(1)yji=∑i,jxiwji+b,where w is the weight of tensor and b is the bias value. Taking the operation process of the first convolution kernel as an example, the convolution kernel starts from the first point of the one-dimensional signal and multiplies the corresponding coefficient value of the selected area to obtain an eigenvalue. The convolution operation is performed by repeatedly sliding down until the first convolution kernel traverses all the input signals and a feature value is obtained. Then, the second convolution kernel also performs the same operation until all the convolution kernel calculations are completed.Pooling layer’s primary job is to reduce the spatial dimension of the feature information in the preceding layer in order to pick and filter features. This reduces parameters in subsequent layers, thereby reducing the computational cost. The main pooling methods used in convolutional neural networks are max pooling and average pooling (see equations (2) and (3)). The global average pooling is used to avoid the flatten layers in CNN, whereas the max pooling is used to downsample the input representation. It is why in the proposed method, the softmax activation is preceded by global average pooling, while the max pooling is performed prior to FRN.
(2)pmaxi=MaxX,(3)pavei=AveX.By performing nonlinear modification on the convolution layer’s output, the activation function is able to translate the multidimensional linear characteristics to a new location. The network model’s performance is greatly influenced by the activation function, which can improve the neural network’s expressive and learning capabilities. Sigmoid, tanh, and ReLU are the common activation functions. With the sigmoid function (S), output is mapped between (0, 1). The hyperbolic tangent function tanh is similar in shape to sigmoid, except that tanh maps the output between (-1, 1). The above two activation functions can easily lead to the problem of gradient disappearance, because when the curve is saturated, the gradient is close to 0. In such case, the weights cannot be updated effectively. ReLU is a computationally inexpensive activation function that can effectively suppress the gradient disappearance problem [38]. Moreover, the ReLU activation function is handier to accelerate the training efficiency of the network model; hence, the activation function is utilized in MTQEN.FC is basic structure of traditional feedforward neural network, and its connection method is that each neuron is connected to all the neurons. The last FC layer reorganizes the previously extracted features. After several convolutional layers and pooling layer calculations,(4)yil=∑jwijx+b.Convolutional neural networks use a loss function to evaluate the consistency of input and output. In this work, CE loss is selected as loss, which is usually used together with softmax. The real category labels of the dataset are processed with one-hot encoding; that is, the probability predicted by the model and the one-hot form of the real category are calculated by cross-entropy loss.The receptive field is a very core concept in CNN. The local receptive field in CNN mainly describes the area of the input layer covered by convolution kernel and finally integrates local information together. The range of the receptive field is the area in input layer corresponding to neurons in output result of a certain layer, and the primary features are extracted from the input signal, and then, the primary features are integrated to obtain further comprehensive features. Compared with fully connected neural network, CNN requires fewer parameters to be trained, and the operation efficiency is improved. Weight sharing is another important feature of convolutional neural networks, and its purpose is to further reduce training parameters. When performing a convolution operation, each convolution kernel in the convolution layer acts repeatedly on the entire input signal, and weight sharing is to design each convolution kernel to share the same parameters, including the same weight value and bias. Compared with fully connected neural network, training parameters of CNN using the weight sharing mechanism are greatly reduced.


### 3.2. MTQEN Network

This work designs an MTQEN network for the quality evaluation of music teaching in colleges and uses different optimization strategies to promote network.


#### 3.2.1. Dilated Convolution

Dilated convolution adds a parameter expansion coefficient to the original convolution. Main purpose of dilation is to expand the range of feature special zones by expanding the receptive field. Figure1(a) is a schematic diagram of ordinary convolution. The dilation coefficient of the dilated convolution in Figure 1(b) is set to 2, which is equivalent to performing a convolution operation with a 6×1 convolution kernel, adding a point of 0 to each row. The receptive field of the network becomes larger; that is, the dilated convolution obtains a larger receptive field with a smaller convolution kernel, and the parameters of dilated convolution are less than those of the ordinary convolution.Figure 1
Ordinary convolution and dilated convolution.
(a)(b)


#### 3.2.2. Residual Block

Usually, the neural network improves the nonlinear fitting ability for model by increasing network layers. With increase of network layers, the numbers of neurons are increased exponentially. This will cause gradient explosion and difficulty in optimizing the network. In such cases, as accuracy of a model decreases, the phenomenon is called model performance degradation. To solve the issue, typically, the network layers are increased to approach to an approximate identity mapping parameter. The deep residual network transforms the deep neural network into a relatively shallow network, solves issue of gradient dispersion and performance degradation of the deep neural network, and improves recognition accuracy. Residual blocks are designed to satisfy the identity mapping, which skips multiple layers mainly by adding connections to the neural network. This cross-feature layer structure is helpful for the spatial information interaction between different feature layers; that is, more features can be extracted under the same amount of computation, which improves network performance and training results.The traditional neural network module directly fits the current function by stacking the network layers, while the residual module allows multiple consecutively stacked convolutional layers to fit the residual between input and mapped output. By fitting residuals, it is easier to find small changes, which is more conducive to the parameter adjustment of the overall network. The residual network output is given as(5)Hx=x+Fx.In this work, the residual block is improved, and the structure is demonstrated in Figure2.Figure 2
The improved residual block.


#### 3.2.3. Normalization

As the depth of the network model continues to increase, various problems will be encountered in training process. The purpose of introducing the normalization layer is to make network training more efficient and to make the training process easier. The currently commonly used method introduces BN after the convolution layer and before activation layer. Output is normalized by additional scaling and displacement, and then input to the activation layer, and then input to the next convolution layer. Batch normalization has become one of the indispensable methods for training deep learning network models. It alleviates the problems encountered during training by reducing internal covariate shifts in data while enabling the network to provide better accuracy performance.In reference, a filter response normalization (FRN) method was proposed. Compared with the commonly used batch normalization layer, the FRN layer has better advantages in model training. It first eliminates the dependence of the network model on the batch size, and secondly, it performs better than the BN layer in terms of the learning rate of the model. The specific operation steps are as follows. First, take mean square value of input; features are then normalized. To minimize data abnormalities, the maximum-minimum normalization method is used:(6)xnom=x−AminAmax−Amin.For the evaluation criterion, the mean absolute percentage error (MAPE) is utilized given as(7)MAPE=∑i=1Ny^i−yiyi100N,where yi is the truth value and y^i the predicted value. N is the total number of prediction experiments. A smaller value of MAPE indicates minor difference between the predicted and the true value.After scaling and transformation of data, a threshold ReLU function with TLU part is adopted after the FRN (see Figure3).
(8)a2=1N∑ixi2,x′=xa2+ε,y′=cx′+d,z=ReLUy′−τ+τ.Figure 3
Schematic of the FRN layer.


#### 3.2.4. Global Pooling Layer

The convolution layer, activation layer, pooling layer, and the fully connected layer make up the bulk of the CNN architecture. Each neuron in the completely connected layer is linked to the neurons in the preceding layer, allowing the fully connected layer to synthesize the previously retrieved characteristics. The parameters in the fully connected layer, especially the fully connected layer, are connected to the last convolutional layer, which is a fatal flaw of the layer. To put it another way, the quantity of training and prediction calculation is enhanced, while the operation time is decreased. When it comes to overfitting, an extremely high number of parameters can easily lead to the network being overfit, which is not favorable to future practical applications. Flattening may be used to convert a multidimensional object to one-dimensional; however, it merely rearranged the elements. Pooling is comparatively a preferred method for better representation of vector. With pooling, actually a parser window moves across the object, averaging is performed, and a max value is directly picked.To avoid the above problems, a global pooling layer is introduced to replace FC layer. The global pooling layer is considered as a new technology that can replace FC layer. FC layer expands the convolutional layers and classifies each feature map. Global average pooling is to average pool each feature map to form a feature point and combine these feature points into final feature vector for classification.The global pooling layer has the following advantages. First, the conversion between the features and the final classification is simpler and more natural in the global pooling layer. Second, the global pooling layer does not require many parameters for training; this makes model more robust.


#### 3.2.5. Adam Optimizer

Research on convolutional neural networks has found that there are no strict rules for setting the learning rate. The commonly used optimization algorithms mainly include SGD, RMSProp, Momentum SGD, Momentum RMSProp, and Adam. In the process of network model training, a very important part of the work is to use the optimization algorithm to adjust and update the parameters to minimize the value of the objective function.(9)θ=argminLoss.The deep learning algorithm model often uses SGD, and the SGD optimization algorithm achieves better results in shallow neural networks. However, with the increase of the depth of the model and the complexity of the structure, the number of model parameters and hyperparameters becomes huge, and it is easy to make the training of the network model fall into the local optimum. Adam is suitable for dealing with nonstationary targets, is generally robust to the selection of hyperparameters, and has great advantages in parameter tuning of deep neural networks. Stochastic gradient descent uses a single learning rate to update weights. Similarly, though RMSProp works well for nonstationary targets, but momentum on gradient needs to be rescaled. Unlike RMSProp, Adam estimates a running average. The first- and second-order moment estimations are performed to create separate adaptive learning rates for multiple parameters. Hence, this work adopts the Adam optimization algorithm with the parameters presented in Table1.Table 1
Training parameters of the model.
Predictive modelsParameter nameParametersMTQENOptimization algorithmAdamAlpha/learning rate0.001Beta 1/first moment exponential decay rate0.9Beta 2/second moment exponential decay rate1Dropout rate0.2


#### 3.2.6. MTQEN Network for College Music Teaching Quality Evaluation

MTQEN structure designed in this work is illustrated in Figure4. Two layers of dilated convolutional layers are designed after input, the purpose of which is to expand the receptive field, thereby expanding range of feature extraction. The FRN layer is added after the empty convolution layer for data normalization, which eliminates the dependence of model training on the batch size and improves the training rate of the model. Two consecutive residual modules are introduced after the dilated convolutional layer, and the FRN layer is used in the modules. Residual module enhances the nonlinear expression ability of the network model and avoids the problem of performance degradation of the network model. Finally, connect the global pooling layer to reduce training parameters and improve training speed.Figure 4
The MTQEN structure.


## 3.1. Convolutional Neural Network

Normally, CNN is preferred for 2D data; however, as stated in [37], a 2D-CNN model with a number of parameters and complexity is likely to suffer from overfitting. Therefore, the 1D-CNN is used with ReLU to speed up the forward propagation and to avoid gradient explosion. The first half of CNN model stacks several groups of convolutional layers and pooling layers to form the feature extraction part, and finally, the FC layer is generally connected as a classifier to form feature output part. The basic structure of CNN is similar to that of ordinary neural network, and its basic structure is composed of neurons. However, the convolutional neural network has improved the form and function of the layer, which greatly improves the classification and recognition ability of the network model.Data features are extracted by filtering input data via the convolutional layer. Input data studied in this paper is a one-dimensional signal. The convolution kernel of the current convolution layer performs a convolution operation on input. The operation is(1)yji=∑i,jxiwji+b,where w is the weight of tensor and b is the bias value. Taking the operation process of the first convolution kernel as an example, the convolution kernel starts from the first point of the one-dimensional signal and multiplies the corresponding coefficient value of the selected area to obtain an eigenvalue. The convolution operation is performed by repeatedly sliding down until the first convolution kernel traverses all the input signals and a feature value is obtained. Then, the second convolution kernel also performs the same operation until all the convolution kernel calculations are completed.Pooling layer’s primary job is to reduce the spatial dimension of the feature information in the preceding layer in order to pick and filter features. This reduces parameters in subsequent layers, thereby reducing the computational cost. The main pooling methods used in convolutional neural networks are max pooling and average pooling (see equations (2) and (3)). The global average pooling is used to avoid the flatten layers in CNN, whereas the max pooling is used to downsample the input representation. It is why in the proposed method, the softmax activation is preceded by global average pooling, while the max pooling is performed prior to FRN.
(2)pmaxi=MaxX,(3)pavei=AveX.By performing nonlinear modification on the convolution layer’s output, the activation function is able to translate the multidimensional linear characteristics to a new location. The network model’s performance is greatly influenced by the activation function, which can improve the neural network’s expressive and learning capabilities. Sigmoid, tanh, and ReLU are the common activation functions. With the sigmoid function (S), output is mapped between (0, 1). The hyperbolic tangent function tanh is similar in shape to sigmoid, except that tanh maps the output between (-1, 1). The above two activation functions can easily lead to the problem of gradient disappearance, because when the curve is saturated, the gradient is close to 0. In such case, the weights cannot be updated effectively. ReLU is a computationally inexpensive activation function that can effectively suppress the gradient disappearance problem [38]. Moreover, the ReLU activation function is handier to accelerate the training efficiency of the network model; hence, the activation function is utilized in MTQEN.FC is basic structure of traditional feedforward neural network, and its connection method is that each neuron is connected to all the neurons. The last FC layer reorganizes the previously extracted features. After several convolutional layers and pooling layer calculations,(4)yil=∑jwijx+b.Convolutional neural networks use a loss function to evaluate the consistency of input and output. In this work, CE loss is selected as loss, which is usually used together with softmax. The real category labels of the dataset are processed with one-hot encoding; that is, the probability predicted by the model and the one-hot form of the real category are calculated by cross-entropy loss.The receptive field is a very core concept in CNN. The local receptive field in CNN mainly describes the area of the input layer covered by convolution kernel and finally integrates local information together. The range of the receptive field is the area in input layer corresponding to neurons in output result of a certain layer, and the primary features are extracted from the input signal, and then, the primary features are integrated to obtain further comprehensive features. Compared with fully connected neural network, CNN requires fewer parameters to be trained, and the operation efficiency is improved. Weight sharing is another important feature of convolutional neural networks, and its purpose is to further reduce training parameters. When performing a convolution operation, each convolution kernel in the convolution layer acts repeatedly on the entire input signal, and weight sharing is to design each convolution kernel to share the same parameters, including the same weight value and bias. Compared with fully connected neural network, training parameters of CNN using the weight sharing mechanism are greatly reduced.


## 3.2. MTQEN Network

This work designs an MTQEN network for the quality evaluation of music teaching in colleges and uses different optimization strategies to promote network.


### 3.2.1. Dilated Convolution

Dilated convolution adds a parameter expansion coefficient to the original convolution. Main purpose of dilation is to expand the range of feature special zones by expanding the receptive field. Figure1(a) is a schematic diagram of ordinary convolution. The dilation coefficient of the dilated convolution in Figure 1(b) is set to 2, which is equivalent to performing a convolution operation with a 6×1 convolution kernel, adding a point of 0 to each row. The receptive field of the network becomes larger; that is, the dilated convolution obtains a larger receptive field with a smaller convolution kernel, and the parameters of dilated convolution are less than those of the ordinary convolution.Figure 1
Ordinary convolution and dilated convolution.
(a)(b)


### 3.2.2. Residual Block

Usually, the neural network improves the nonlinear fitting ability for model by increasing network layers. With increase of network layers, the numbers of neurons are increased exponentially. This will cause gradient explosion and difficulty in optimizing the network. In such cases, as accuracy of a model decreases, the phenomenon is called model performance degradation. To solve the issue, typically, the network layers are increased to approach to an approximate identity mapping parameter. The deep residual network transforms the deep neural network into a relatively shallow network, solves issue of gradient dispersion and performance degradation of the deep neural network, and improves recognition accuracy. Residual blocks are designed to satisfy the identity mapping, which skips multiple layers mainly by adding connections to the neural network. This cross-feature layer structure is helpful for the spatial information interaction between different feature layers; that is, more features can be extracted under the same amount of computation, which improves network performance and training results.The traditional neural network module directly fits the current function by stacking the network layers, while the residual module allows multiple consecutively stacked convolutional layers to fit the residual between input and mapped output. By fitting residuals, it is easier to find small changes, which is more conducive to the parameter adjustment of the overall network. The residual network output is given as(5)Hx=x+Fx.In this work, the residual block is improved, and the structure is demonstrated in Figure2.Figure 2
The improved residual block.


### 3.2.3. Normalization

As the depth of the network model continues to increase, various problems will be encountered in training process. The purpose of introducing the normalization layer is to make network training more efficient and to make the training process easier. The currently commonly used method introduces BN after the convolution layer and before activation layer. Output is normalized by additional scaling and displacement, and then input to the activation layer, and then input to the next convolution layer. Batch normalization has become one of the indispensable methods for training deep learning network models. It alleviates the problems encountered during training by reducing internal covariate shifts in data while enabling the network to provide better accuracy performance.In reference, a filter response normalization (FRN) method was proposed. Compared with the commonly used batch normalization layer, the FRN layer has better advantages in model training. It first eliminates the dependence of the network model on the batch size, and secondly, it performs better than the BN layer in terms of the learning rate of the model. The specific operation steps are as follows. First, take mean square value of input; features are then normalized. To minimize data abnormalities, the maximum-minimum normalization method is used:(6)xnom=x−AminAmax−Amin.For the evaluation criterion, the mean absolute percentage error (MAPE) is utilized given as(7)MAPE=∑i=1Ny^i−yiyi100N,where yi is the truth value and y^i the predicted value. N is the total number of prediction experiments. A smaller value of MAPE indicates minor difference between the predicted and the true value.After scaling and transformation of data, a threshold ReLU function with TLU part is adopted after the FRN (see Figure3).
(8)a2=1N∑ixi2,x′=xa2+ε,y′=cx′+d,z=ReLUy′−τ+τ.Figure 3
Schematic of the FRN layer.


### 3.2.4. Global Pooling Layer

The convolution layer, activation layer, pooling layer, and the fully connected layer make up the bulk of the CNN architecture. Each neuron in the completely connected layer is linked to the neurons in the preceding layer, allowing the fully connected layer to synthesize the previously retrieved characteristics. The parameters in the fully connected layer, especially the fully connected layer, are connected to the last convolutional layer, which is a fatal flaw of the layer. To put it another way, the quantity of training and prediction calculation is enhanced, while the operation time is decreased. When it comes to overfitting, an extremely high number of parameters can easily lead to the network being overfit, which is not favorable to future practical applications. Flattening may be used to convert a multidimensional object to one-dimensional; however, it merely rearranged the elements. Pooling is comparatively a preferred method for better representation of vector. With pooling, actually a parser window moves across the object, averaging is performed, and a max value is directly picked.To avoid the above problems, a global pooling layer is introduced to replace FC layer. The global pooling layer is considered as a new technology that can replace FC layer. FC layer expands the convolutional layers and classifies each feature map. Global average pooling is to average pool each feature map to form a feature point and combine these feature points into final feature vector for classification.The global pooling layer has the following advantages. First, the conversion between the features and the final classification is simpler and more natural in the global pooling layer. Second, the global pooling layer does not require many parameters for training; this makes model more robust.


### 3.2.5. Adam Optimizer

Research on convolutional neural networks has found that there are no strict rules for setting the learning rate. The commonly used optimization algorithms mainly include SGD, RMSProp, Momentum SGD, Momentum RMSProp, and Adam. In the process of network model training, a very important part of the work is to use the optimization algorithm to adjust and update the parameters to minimize the value of the objective function.(9)θ=argminLoss.The deep learning algorithm model often uses SGD, and the SGD optimization algorithm achieves better results in shallow neural networks. However, with the increase of the depth of the model and the complexity of the structure, the number of model parameters and hyperparameters becomes huge, and it is easy to make the training of the network model fall into the local optimum. Adam is suitable for dealing with nonstationary targets, is generally robust to the selection of hyperparameters, and has great advantages in parameter tuning of deep neural networks. Stochastic gradient descent uses a single learning rate to update weights. Similarly, though RMSProp works well for nonstationary targets, but momentum on gradient needs to be rescaled. Unlike RMSProp, Adam estimates a running average. The first- and second-order moment estimations are performed to create separate adaptive learning rates for multiple parameters. Hence, this work adopts the Adam optimization algorithm with the parameters presented in Table1.Table 1
Training parameters of the model.
Predictive modelsParameter nameParametersMTQENOptimization algorithmAdamAlpha/learning rate0.001Beta 1/first moment exponential decay rate0.9Beta 2/second moment exponential decay rate1Dropout rate0.2


### 3.2.6. MTQEN Network for College Music Teaching Quality Evaluation

MTQEN structure designed in this work is illustrated in Figure4. Two layers of dilated convolutional layers are designed after input, the purpose of which is to expand the receptive field, thereby expanding range of feature extraction. The FRN layer is added after the empty convolution layer for data normalization, which eliminates the dependence of model training on the batch size and improves the training rate of the model. Two consecutive residual modules are introduced after the dilated convolutional layer, and the FRN layer is used in the modules. Residual module enhances the nonlinear expression ability of the network model and avoids the problem of performance degradation of the network model. Finally, connect the global pooling layer to reduce training parameters and improve training speed.Figure 4
The MTQEN structure.


## 3.2.1. Dilated Convolution

Dilated convolution adds a parameter expansion coefficient to the original convolution. Main purpose of dilation is to expand the range of feature special zones by expanding the receptive field. Figure1(a) is a schematic diagram of ordinary convolution. The dilation coefficient of the dilated convolution in Figure 1(b) is set to 2, which is equivalent to performing a convolution operation with a 6×1 convolution kernel, adding a point of 0 to each row. The receptive field of the network becomes larger; that is, the dilated convolution obtains a larger receptive field with a smaller convolution kernel, and the parameters of dilated convolution are less than those of the ordinary convolution.Figure 1
Ordinary convolution and dilated convolution.
(a)(b)


## 3.2.2. Residual Block

Usually, the neural network improves the nonlinear fitting ability for model by increasing network layers. With increase of network layers, the numbers of neurons are increased exponentially. This will cause gradient explosion and difficulty in optimizing the network. In such cases, as accuracy of a model decreases, the phenomenon is called model performance degradation. To solve the issue, typically, the network layers are increased to approach to an approximate identity mapping parameter. The deep residual network transforms the deep neural network into a relatively shallow network, solves issue of gradient dispersion and performance degradation of the deep neural network, and improves recognition accuracy. Residual blocks are designed to satisfy the identity mapping, which skips multiple layers mainly by adding connections to the neural network. This cross-feature layer structure is helpful for the spatial information interaction between different feature layers; that is, more features can be extracted under the same amount of computation, which improves network performance and training results.The traditional neural network module directly fits the current function by stacking the network layers, while the residual module allows multiple consecutively stacked convolutional layers to fit the residual between input and mapped output. By fitting residuals, it is easier to find small changes, which is more conducive to the parameter adjustment of the overall network. The residual network output is given as(5)Hx=x+Fx.In this work, the residual block is improved, and the structure is demonstrated in Figure2.Figure 2
The improved residual block.


## 3.2.3. Normalization

As the depth of the network model continues to increase, various problems will be encountered in training process. The purpose of introducing the normalization layer is to make network training more efficient and to make the training process easier. The currently commonly used method introduces BN after the convolution layer and before activation layer. Output is normalized by additional scaling and displacement, and then input to the activation layer, and then input to the next convolution layer. Batch normalization has become one of the indispensable methods for training deep learning network models. It alleviates the problems encountered during training by reducing internal covariate shifts in data while enabling the network to provide better accuracy performance.In reference, a filter response normalization (FRN) method was proposed. Compared with the commonly used batch normalization layer, the FRN layer has better advantages in model training. It first eliminates the dependence of the network model on the batch size, and secondly, it performs better than the BN layer in terms of the learning rate of the model. The specific operation steps are as follows. First, take mean square value of input; features are then normalized. To minimize data abnormalities, the maximum-minimum normalization method is used:(6)xnom=x−AminAmax−Amin.For the evaluation criterion, the mean absolute percentage error (MAPE) is utilized given as(7)MAPE=∑i=1Ny^i−yiyi100N,where yi is the truth value and y^i the predicted value. N is the total number of prediction experiments. A smaller value of MAPE indicates minor difference between the predicted and the true value.After scaling and transformation of data, a threshold ReLU function with TLU part is adopted after the FRN (see Figure3).
(8)a2=1N∑ixi2,x′=xa2+ε,y′=cx′+d,z=ReLUy′−τ+τ.Figure 3
Schematic of the FRN layer.


## 3.2.4. Global Pooling Layer

The convolution layer, activation layer, pooling layer, and the fully connected layer make up the bulk of the CNN architecture. Each neuron in the completely connected layer is linked to the neurons in the preceding layer, allowing the fully connected layer to synthesize the previously retrieved characteristics. The parameters in the fully connected layer, especially the fully connected layer, are connected to the last convolutional layer, which is a fatal flaw of the layer. To put it another way, the quantity of training and prediction calculation is enhanced, while the operation time is decreased. When it comes to overfitting, an extremely high number of parameters can easily lead to the network being overfit, which is not favorable to future practical applications. Flattening may be used to convert a multidimensional object to one-dimensional; however, it merely rearranged the elements. Pooling is comparatively a preferred method for better representation of vector. With pooling, actually a parser window moves across the object, averaging is performed, and a max value is directly picked.To avoid the above problems, a global pooling layer is introduced to replace FC layer. The global pooling layer is considered as a new technology that can replace FC layer. FC layer expands the convolutional layers and classifies each feature map. Global average pooling is to average pool each feature map to form a feature point and combine these feature points into final feature vector for classification.The global pooling layer has the following advantages. First, the conversion between the features and the final classification is simpler and more natural in the global pooling layer. Second, the global pooling layer does not require many parameters for training; this makes model more robust.


## 3.2.5. Adam Optimizer

Research on convolutional neural networks has found that there are no strict rules for setting the learning rate. The commonly used optimization algorithms mainly include SGD, RMSProp, Momentum SGD, Momentum RMSProp, and Adam. In the process of network model training, a very important part of the work is to use the optimization algorithm to adjust and update the parameters to minimize the value of the objective function.(9)θ=argminLoss.The deep learning algorithm model often uses SGD, and the SGD optimization algorithm achieves better results in shallow neural networks. However, with the increase of the depth of the model and the complexity of the structure, the number of model parameters and hyperparameters becomes huge, and it is easy to make the training of the network model fall into the local optimum. Adam is suitable for dealing with nonstationary targets, is generally robust to the selection of hyperparameters, and has great advantages in parameter tuning of deep neural networks. Stochastic gradient descent uses a single learning rate to update weights. Similarly, though RMSProp works well for nonstationary targets, but momentum on gradient needs to be rescaled. Unlike RMSProp, Adam estimates a running average. The first- and second-order moment estimations are performed to create separate adaptive learning rates for multiple parameters. Hence, this work adopts the Adam optimization algorithm with the parameters presented in Table1.Table 1
Training parameters of the model.
Predictive modelsParameter nameParametersMTQENOptimization algorithmAdamAlpha/learning rate0.001Beta 1/first moment exponential decay rate0.9Beta 2/second moment exponential decay rate1Dropout rate0.2


## 3.2.6. MTQEN Network for College Music Teaching Quality Evaluation

MTQEN structure designed in this work is illustrated in Figure4. Two layers of dilated convolutional layers are designed after input, the purpose of which is to expand the receptive field, thereby expanding range of feature extraction. The FRN layer is added after the empty convolution layer for data normalization, which eliminates the dependence of model training on the batch size and improves the training rate of the model. Two consecutive residual modules are introduced after the dilated convolutional layer, and the FRN layer is used in the modules. Residual module enhances the nonlinear expression ability of the network model and avoids the problem of performance degradation of the network model. Finally, connect the global pooling layer to reduce training parameters and improve training speed.Figure 4
The MTQEN structure.


## 4. Experiment

For experimentation and analysis, all the models were trained by the same dataset, with the same parameters on a system running with Intel Corei7-7700 CPU, Nvidia GTX-1070Ti GPU, and with 32G RAM. The Keras deep learning framework was used with TensorFlow at the backend. Details about the dataset and the evaluations performed are presented in the following subheadings.


### 4.1. Dataset

This work collects data related to the quality of music teaching in colleges to form required dataset. The training set contains 20,381 samples, and the test set contains 12,049 samples. The feature index of each sample is shown in Table2. As the softmax activation function is used, therefore the inputs/logits are transformed into probabilities representing class/target prediction. For an input instance, the model is supposed to predict one of the five music teaching (MT) classes (MT1=best, MT2=good, MT3=normal, MT4=satisfactory, and MT5=poor).Table 2
Feature indexes of the data.
FeatureIndexTeaching objectivex1Passion for teachingx2Rich contentx3Expanding horizonx4Various meansx5Teacher-student interactionx6Teaching with aptitudex7Clear contentx8Easy to understandx9Rigorous teachingx10


### 4.2. Evaluation on MTQEN

The training and testing of the proposed MTQEN model is comparatively analyzed with the three contemporary models: multilayer BP NN, CNN, and ResNet. The experimental results obtained are demonstrated in Figure5.Figure 5
The training process of MTQEN.As the training iteration progresses, the loss of the MTQEN network gradually decreases and finally converges to a fixed value. After that, this work compares MTQEN with other machine learning methods horizontally, and the experimental results are demonstrated in Table3.Table 3
Other method comparison.
MethodAccuracyRecallMultilayer BP NN89.687.3CNN91.990.2ResNet93.591.6MTQEN95.693.3Compared with other evaluation methods, MTQEN has the highest accuracy and recall rate in the evaluation of music teaching quality in colleges, which verifies the effectiveness of proposed method.


### 4.3. Evaluation on Optimization Strategy

First, this work compares the effectiveness of dilated convolution, comparing the performance without dilated convolution and when dilated convolution is used, and the results are illustrated in Figure6.Figure 6
Comparison of dilated convolution and ordinary convolution.Compared with ordinary convolution, the corresponding performance improvement can be obtained after using dilated convolution, which shows the feasibility of using dilated convolution.Secondly, this work evaluates the effectiveness of the improved residual block and compares the performance when using the improved block and the normal residual block. The experimental results are illustrated in Figure7.Figure 7
Comparison of improved residual block and ordinary residual block.Compared with ordinary residual block, the corresponding performance improvement can be obtained after using improved residual block, which shows the feasibility of improving residual block.The proposed method evaluates the effectiveness of FRN normalization and compares the performance when using FRN normalization and BN normalization, respectively. The experimental results are demonstrated in Figure8.Figure 8
Comparison of FRN normalization and BN normalization.Compared with BN normalization, the corresponding performance improvement can be obtained after using FRN normalization, which shows the feasibility of FRN normalization.Finally, this work evaluates the effectiveness of the global pooling layer and compares the performance when using the global pooling layer and FC, respectively. The experimental results are illustrated in Figure9.Figure 9
Comparison of global pooling layer and fully connected layer.Compared with the traditional FC layer, after using the global pooling layer, the parameters and training time of the network are less. At the same time, the precision and recall rates have improved.


## 4.1. Dataset

This work collects data related to the quality of music teaching in colleges to form required dataset. The training set contains 20,381 samples, and the test set contains 12,049 samples. The feature index of each sample is shown in Table2. As the softmax activation function is used, therefore the inputs/logits are transformed into probabilities representing class/target prediction. For an input instance, the model is supposed to predict one of the five music teaching (MT) classes (MT1=best, MT2=good, MT3=normal, MT4=satisfactory, and MT5=poor).Table 2
Feature indexes of the data.
FeatureIndexTeaching objectivex1Passion for teachingx2Rich contentx3Expanding horizonx4Various meansx5Teacher-student interactionx6Teaching with aptitudex7Clear contentx8Easy to understandx9Rigorous teachingx10


## 4.2. Evaluation on MTQEN

The training and testing of the proposed MTQEN model is comparatively analyzed with the three contemporary models: multilayer BP NN, CNN, and ResNet. The experimental results obtained are demonstrated in Figure5.Figure 5
The training process of MTQEN.As the training iteration progresses, the loss of the MTQEN network gradually decreases and finally converges to a fixed value. After that, this work compares MTQEN with other machine learning methods horizontally, and the experimental results are demonstrated in Table3.Table 3
Other method comparison.
MethodAccuracyRecallMultilayer BP NN89.687.3CNN91.990.2ResNet93.591.6MTQEN95.693.3Compared with other evaluation methods, MTQEN has the highest accuracy and recall rate in the evaluation of music teaching quality in colleges, which verifies the effectiveness of proposed method.


## 4.3. Evaluation on Optimization Strategy

First, this work compares the effectiveness of dilated convolution, comparing the performance without dilated convolution and when dilated convolution is used, and the results are illustrated in Figure6.Figure 6
Comparison of dilated convolution and ordinary convolution.Compared with ordinary convolution, the corresponding performance improvement can be obtained after using dilated convolution, which shows the feasibility of using dilated convolution.Secondly, this work evaluates the effectiveness of the improved residual block and compares the performance when using the improved block and the normal residual block. The experimental results are illustrated in Figure7.Figure 7
Comparison of improved residual block and ordinary residual block.Compared with ordinary residual block, the corresponding performance improvement can be obtained after using improved residual block, which shows the feasibility of improving residual block.The proposed method evaluates the effectiveness of FRN normalization and compares the performance when using FRN normalization and BN normalization, respectively. The experimental results are demonstrated in Figure8.Figure 8
Comparison of FRN normalization and BN normalization.Compared with BN normalization, the corresponding performance improvement can be obtained after using FRN normalization, which shows the feasibility of FRN normalization.Finally, this work evaluates the effectiveness of the global pooling layer and compares the performance when using the global pooling layer and FC, respectively. The experimental results are illustrated in Figure9.Figure 9
Comparison of global pooling layer and fully connected layer.Compared with the traditional FC layer, after using the global pooling layer, the parameters and training time of the network are less. At the same time, the precision and recall rates have improved.


## 5. Conclusion

As a vital component in the development of students’ overall personality, quality music education and instilling instruction go hand in hand. As an important way of cultivating college students’ musical quality, music teaching in colleges has attracted more attention from educational circles. At present, most colleges have not established a complete music teaching quality evaluation method, following the traditional manual evaluation method. This work considers the emerging artificial neural network in the designing of a neural network for quality evaluation of music teaching. A one-dimensional optimized CNN network model is proposed covering the three key aspects: expanding the receptive field, reducing training parameters, and optimizing the model’s operationality. First, the dilated convolution layer is utilized to replace traditional convolution layer, which increases the scope of the local receptive field and makes the network model’s ability to extract features better. Second, the use of filter response normalization eliminates the dependence on batch size, which significantly improves training rate and stability. Finally, the introduction of global pooling reduces the parameters required for model training, resulting in significant improvements in training efficiency and parameter comparison. The proposed optimization measures improve feature extraction and learning ability of the model. A key limitation of the model is the time consumed in training, particularly in normalization. Unlike batch normalization where input values in the minibatch are normalized, in the layer normalization, input values for all neurons in the same layer are normalized. As a future direction, the model will be enhanced and the approach of layer normalization will be used instead of batch normalization.



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*Source: 7370015-2022-08-24.xml*