# Research on Children’s Aesthetic Needs of Multinational Children’s Songs Based on Information Security

Authors: Mingru Liu
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

In the context of information security, the aesthetics and data privacy security of multinational children’s songs are studied. The research collects the data of foreign children’s songs, and on the basis of the collected data, the mathematical model of the children’s song text is constructed by using a random matrix. An industry consisting of various media entities that disseminate information is analyzed. In addition, vocal performance is used to improve children’s aesthetic ability and comprehensive quality. An efficient information hiding scheme based on the random matrix is proposed. Furthermore, by establishing a random matrix to optimize the information scheme hidden in the multinational children’s songs, the textual expression efficiency of children’s songs is improved. The results reveal that when the model is iterated for 200 times, the traditional algorithm has only a recognition accuracy of 0.73 for children’s songs, and the recognition accuracy of the convolution neural network (CNN) model can reach 0.76, while the proposed improved deep neural networks (DNN) algorithm model has the best recognition accuracy, which can reach 0.80. It explores how to better meet children’s aesthetic and cultural needs from the perspective of information security by optimizing the semantic indexing algorithm of random matrix mapping elements. Through the performance test of the algorithm, on account of reducing the time complexity of the algorithm, the classification accuracy of children’s song text can be effectively improved. This exploration has practical reference value for cultivating children’s comprehensive quality and aesthetic ability.


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## Body

## 1. Introduction

With the progress of the socio-political economy, the public has a greater ability to consume leisure [1–3]. At the same time, popular culture utilizing the Internet, TV, music, books, and other media has become the basis of all cultures in this era and actually affects people’s consumption aesthetics. In the long run, the future is sure to be a world dominated by aesthetic consciousness. Under the guidance of science and technology, people’s living standards have been greatly improved. The emergence of a planned economy will make aesthetic consciousness the most competitive feature in the future [4, 5]. However, information security needs to attract people’s attention in this era. For the dissemination of multinational children’s songs, on the one hand, it can meet the development of children’s aesthetic needs, and on the other hand, it should also pay attention to the security of information and privacy data in the process of dissemination of children’s songs. A country can make progress in higher fields only if it persistently pursues economic and aesthetic needs. Aesthetic culture needs continuous cultivation. Therefore, the cultivation of aesthetic expression ability is very important.The current research on aesthetic consciousness mainly focuses on the construction of specific disciplines, ignoring the emotion of students as the subject of education [6]. The research and analysis of the current children’s aesthetic needs found that aesthetics is a way of thinking, and it is the psychological and emotional cognition of people’s perception of things. There are differences in the influence of different factors on children’s aesthetic literacy of different genders and school stages, and the current research on children’s aesthetic needs does not fully consider these factors. It attempts to summarize the characteristics and laws of children’s aesthetic psychology by investigating the current situation of children’s song use in three kindergartens in A, B, and C in a city. By combining the dissemination of children’s songs with the cultivation of aesthetic achievements, corresponding strategies for cultivating children’s aesthetic achievements are put forward to enrich theoretical resources and facilitate development. Moreover, combined with the analysis of the existing aesthetic culture resources, the basic theories, principles, and methods of aesthetic culture can be understood, paving the way for its application in the field of children, and deepening and sublimating the research on children’s aesthetic culture.Under the background of Internet of Things (IoT) information security, the research data of foreign children’s song are collected, and the method of questionnaire survey is used to investigate the current situation of children’s song use in three kindergartens in A, B, and C in a city. Based on the collected data, the mathematical model of the children’s song text is constructed by using a random matrix. An industry consisting of various media entities that disseminate information is analyzed. Moreover, vocal performance is used to improve children’s aesthetic ability and comprehensive quality. An efficient information hiding scheme based on random matrix is proposed. The significance is to implement a research model of children’s aesthetic needs through mathematical models and information security, which has practical application value for the development of children’s aesthetic needs and information and data security. The first chapter introduces the research background of information security and children’s aesthetic needs. The second chapter sorts out the recent literature on children’s aesthetic needs and information security and compares the commonalities of research methods. The third chapter establishes the mathematical model of children’s song recognition and classification of random matrix and carries out the experimental simulation combined with the deep learning (DL) algorithm. The fourth chapter is the statistics of the research data. The fifth chapter draws research conclusions through comprehensive analysis. The research has practical guiding significance for the development of children’s songs and the cultivation of children’s cultural aesthetic consciousness.


## 2. Recent Related Work

### 2.1. Information Security and IoT Data Transmission

Regarding information security, many scholars have conducted related research. Guhr et al. [7] researched the impact of employees’ expected information security behaviors. It uses the perspective of interaction psychology to link the dimensions of the omnidirectional leadership model with employees’ willingness to be compliant with safety and willing to participate in safety. The findings suggest new directions in information security and leadership research and implications for leadership practice, and the importance of transformational leaders. Wiley et al. [8] conducted a study on examining the relationship between culture and information security awareness, using the human information security questionnaire to measure information security awareness. The results showed that organizations should focus on security culture rather than organizational culture to improve information security awareness. Jalali et al. [9] researched on telemedicine, privacy, and information security technologies in the period of Corona Virus Disease 2019 (COVID-19). The findings signify that telemedicine has become an important part of clinical care delivery as the use of healthcare and telemedicine technology has grown substantially. Ali et al. [10] reviewed information security behaviors and policies and found that employees perceive internal and external motivations from their social circles, management behaviors, and organizational culture to take security-conscious behaviors. Cultural and information security awareness play a vital role in converting employee noncompliance into compliance.Besides, some scholars have studied the development of data transmission technology (DTT) in the context of IoT. For example, Liang et al. [11] studied the DTT of industrial IoT and proposed a secure DTT of industrial by fabric blockchain. The technology uses the sharing mode of the power blockchain to realize a reliable trading center, and experiments testify that the optimized fabric power data storage and transmission have high security and reliability. The proposed technique can increase the transmission rate and packet reception rate by 12% and 13%, respectively. Hui et al. [12] designed and researched the security data transmission scheme of the industrial IoT and applied a new chaotic secure communication scheme to solve the security problem of data transmission, and the research has important reference value for improving the security and speed of data transmission. Sujitha et al. [13] investigated optimal DL-based DTT for industrial IoT, using a convolutional neural network (CNN) to learn a compact representation of raw images, which contain structural data. The results clearly demonstrate the effectiveness of the proposed method with an average peak signal-to-noise ratio (SNR) of 49.90 dB. Kathamuthu et al. [14] conducted research on privacy protection methods and IoT data transmission security by using neural network (NN). The results indicate that the proposed method achieves 93.74% accuracy and 92% sensitivity.


### 2.2. Children’s Aesthetic Needs and Multinational Children’s Songs

Regarding the development of children’s aesthetic needs in the context of IoT, Amen, et al. [15] conducted a study on the painting of children with autism by demonstrating ancillary activities, especially the art of drawing, which helps to develop a sensory perception in children with autism and use it as an alternative means of communication. Adjusting and using their drawings digitally designed through computer software to produce printed fabrics for children. The design has some adequacy in fulfilling the aesthetic and functional requirements. The results denote a statistically significant difference at a significance level of 0. Nordenstam and Widhe [16] analyzed the use of children’s literature in political settings and explored uses. In discussing the political uses of literature, they focus on the interrelationship between context and aesthetics. In this tradition, art does not need to prove and does not need to serve any political, educational, or other purposes. Dursun and Sener Pedgley [17] inspired children’s expectations for hand prosthetics through generative design tools. When designing assistive devices for children with disabilities, designers mostly consider technical and functional aspects from the child’s perspective, ignoring the factors that affect their use. As a result, assistive devices do not fully meet children’s needs in most cases, and research supports children’s physical, emotional, and social well-being by helping designers improve assistive devices. Simpson and Cremin [18] explored children’s literature and social justice issues. Shifts in teachers’ perceptions gained from reading literary texts through scaffolding introductions are illuminated in accordance with comments in several vignettes extracted in professional learning settings. Insights gained by teachers reveal the reconceptualization of reading and the role of literature in primary education.For related research on multinational children’s songs, Brockevelt et al. [19] studied activities in an ecological model of children’s subjective well-being. It summarizes and analyzes the interviews of 17 children aged 8 to 13 and obtains a few subjective well-being indicators, which are divided into six areas. The consequences have important reference value for the improvement of children’s subjective well-being. Kim and Choi [20] conducted research on the formation of tolerance ability of lower grade students and found that in the process of elementary school children’s song teaching, students can improve their aesthetic ability and sensory value by using rich experience in pedagogy. Abidinovich [21] studied the factors that guarantee the effectiveness of performance education of choral works for music education majors in colleges and universities. In the light of higher music education students, the factors are discussed that ensure the effectiveness of performance education based on choral works. It has momentous implications for the education and preservation of children’s voices. Johnson et al. [22] investigated children’s drawings and understanding of children’s ethnic identity, using an approach that provides visually provocative results. The researchers collected pictures drawn by children in Poland and the United States in 1991 and 2019. The findings help understand how children construct the meaning of America and demonstrate children’s ability to incorporate abstract and critical thought into their drawings. And it can inspire people to think about how best to empower children to be creative and critical citizens.To sum up, in the context of the IoT era, enough attention should be paid to the cultivation of children’s aesthetic needs. It takes the multinational children’s songs as the main form to study the aesthetic needs of children, and combined with the theoretical background of information security, the research gap of the children’s aesthetic intelligence training system is filled.


## 2.1. Information Security and IoT Data Transmission

Regarding information security, many scholars have conducted related research. Guhr et al. [7] researched the impact of employees’ expected information security behaviors. It uses the perspective of interaction psychology to link the dimensions of the omnidirectional leadership model with employees’ willingness to be compliant with safety and willing to participate in safety. The findings suggest new directions in information security and leadership research and implications for leadership practice, and the importance of transformational leaders. Wiley et al. [8] conducted a study on examining the relationship between culture and information security awareness, using the human information security questionnaire to measure information security awareness. The results showed that organizations should focus on security culture rather than organizational culture to improve information security awareness. Jalali et al. [9] researched on telemedicine, privacy, and information security technologies in the period of Corona Virus Disease 2019 (COVID-19). The findings signify that telemedicine has become an important part of clinical care delivery as the use of healthcare and telemedicine technology has grown substantially. Ali et al. [10] reviewed information security behaviors and policies and found that employees perceive internal and external motivations from their social circles, management behaviors, and organizational culture to take security-conscious behaviors. Cultural and information security awareness play a vital role in converting employee noncompliance into compliance.Besides, some scholars have studied the development of data transmission technology (DTT) in the context of IoT. For example, Liang et al. [11] studied the DTT of industrial IoT and proposed a secure DTT of industrial by fabric blockchain. The technology uses the sharing mode of the power blockchain to realize a reliable trading center, and experiments testify that the optimized fabric power data storage and transmission have high security and reliability. The proposed technique can increase the transmission rate and packet reception rate by 12% and 13%, respectively. Hui et al. [12] designed and researched the security data transmission scheme of the industrial IoT and applied a new chaotic secure communication scheme to solve the security problem of data transmission, and the research has important reference value for improving the security and speed of data transmission. Sujitha et al. [13] investigated optimal DL-based DTT for industrial IoT, using a convolutional neural network (CNN) to learn a compact representation of raw images, which contain structural data. The results clearly demonstrate the effectiveness of the proposed method with an average peak signal-to-noise ratio (SNR) of 49.90 dB. Kathamuthu et al. [14] conducted research on privacy protection methods and IoT data transmission security by using neural network (NN). The results indicate that the proposed method achieves 93.74% accuracy and 92% sensitivity.


## 2.2. Children’s Aesthetic Needs and Multinational Children’s Songs

Regarding the development of children’s aesthetic needs in the context of IoT, Amen, et al. [15] conducted a study on the painting of children with autism by demonstrating ancillary activities, especially the art of drawing, which helps to develop a sensory perception in children with autism and use it as an alternative means of communication. Adjusting and using their drawings digitally designed through computer software to produce printed fabrics for children. The design has some adequacy in fulfilling the aesthetic and functional requirements. The results denote a statistically significant difference at a significance level of 0. Nordenstam and Widhe [16] analyzed the use of children’s literature in political settings and explored uses. In discussing the political uses of literature, they focus on the interrelationship between context and aesthetics. In this tradition, art does not need to prove and does not need to serve any political, educational, or other purposes. Dursun and Sener Pedgley [17] inspired children’s expectations for hand prosthetics through generative design tools. When designing assistive devices for children with disabilities, designers mostly consider technical and functional aspects from the child’s perspective, ignoring the factors that affect their use. As a result, assistive devices do not fully meet children’s needs in most cases, and research supports children’s physical, emotional, and social well-being by helping designers improve assistive devices. Simpson and Cremin [18] explored children’s literature and social justice issues. Shifts in teachers’ perceptions gained from reading literary texts through scaffolding introductions are illuminated in accordance with comments in several vignettes extracted in professional learning settings. Insights gained by teachers reveal the reconceptualization of reading and the role of literature in primary education.For related research on multinational children’s songs, Brockevelt et al. [19] studied activities in an ecological model of children’s subjective well-being. It summarizes and analyzes the interviews of 17 children aged 8 to 13 and obtains a few subjective well-being indicators, which are divided into six areas. The consequences have important reference value for the improvement of children’s subjective well-being. Kim and Choi [20] conducted research on the formation of tolerance ability of lower grade students and found that in the process of elementary school children’s song teaching, students can improve their aesthetic ability and sensory value by using rich experience in pedagogy. Abidinovich [21] studied the factors that guarantee the effectiveness of performance education of choral works for music education majors in colleges and universities. In the light of higher music education students, the factors are discussed that ensure the effectiveness of performance education based on choral works. It has momentous implications for the education and preservation of children’s voices. Johnson et al. [22] investigated children’s drawings and understanding of children’s ethnic identity, using an approach that provides visually provocative results. The researchers collected pictures drawn by children in Poland and the United States in 1991 and 2019. The findings help understand how children construct the meaning of America and demonstrate children’s ability to incorporate abstract and critical thought into their drawings. And it can inspire people to think about how best to empower children to be creative and critical citizens.To sum up, in the context of the IoT era, enough attention should be paid to the cultivation of children’s aesthetic needs. It takes the multinational children’s songs as the main form to study the aesthetic needs of children, and combined with the theoretical background of information security, the research gap of the children’s aesthetic intelligence training system is filled.


## 3. The Classification and Recognition Model of Children’s Song Based on Random Matrix

### 3.1. Information Security and Random Matrix

The elements of personal information security mainly include three elements: confidentiality, availability, and integrity. Confidentiality means that personal information cannot be stolen. To ensure confidentiality, professional technology is required to encrypt personal information. Availability is that the protection of personal information should be set according to the needs, and the measures taken must be able to truly play a protective effect, to achieve practical results. Integrity refers that the content of personal information should be complete, accurate, and true, and not tampered with by others. At present, the most common technical means to ensure consistency is digital signature. The above three elements are people who need to be fully considered in the process of ensuring personal information security. According to the random matrix, combined with the finite Markov chain to describe the probability data, the recognition and classification model of children’s songs is realized on the basis of computer DL [23]. To make the upper-layer program run on different lower-layer computing platforms, each array interface provided by the system encapsulates the implementation of corresponding array operations on different lower-layer computing platforms. The array interface in the system records the relationship between the operation type of an array and its dependent arrays. According to the relationship between these registers, a calculation flow chart matrix is formed. The random matrix model is used to classify and recognize different types of children’s songs, and the information security of users and songs is stored in the system and model. The frame structure of the mathematical model based on the random matrix is exhibited in Figure 1.Figure 1
The frame structure of the mathematical model based on the random matrix.


### 3.2. Children’s Aesthetic Training Strategy and Structure of Classification and Recognition System of Songs

From the perspective of children’s songs, to cultivate children’s aesthetic consciousness and further enrich students’ aesthetic experience, it is necessary to strengthen children’s aesthetic consciousness by adding aesthetic activities to form their aesthetic sense and creativity [24]. Children and adolescents are in the transition stage from imaginal thinking to abstract thinking. A reasonable gap between aesthetic subject and aesthetic object should be correct [25]. DL algorithm is adopted to identify and learn children’s songs, and a relatively simple children’s song technology is used to imitate the creation process of human composers to achieve the purpose of computer-assisted song recognition and classification. In addition, to express people’s subjective feelings, human-computer interaction (HCI) technology and learning mechanism are added to make music more distinctive. The music produced by many composition systems lacks sufficient musicality and appreciation. To overcome this problem, a learning mechanism is introduced. First, a certain number of domestic and internationally famous children’s songs are systematically studied, and some key parameters are obtained. It includes song length, number of beats, key, audio, the interval between notes, and tempo. These parameters are then applied in the music recognition system to control the operation of the model. That is, each recognition is compared by different song types. The evaluation value and learning parameters are calculated to determine the genetic factors of the next generation. In this way, the final music can ensure that the recognition degree of children’s songs is consistent with traditional children’s songs to a certain extent.In this algorithm model, the discrete input features are converted into dense numerical vectors through the embedding layer, and the input features are combined with numerical features. The input data are nonlinear, and the data in the system are analyzed, wherein the characteristic representation of the input functionx0 of the connection layer of the network data is shown in the following equation (1):(1)x0=xembed,1T…,xembed,kT,xdenseT,where xembed,kT represents the k th transposed eigenvector after the system performs data operations, and xdenseT means the transposed eigenvector of continuous values. For the analysis of the input of the connection layer in the NN, the connection layer Dl+1 of each layer of the network and the complexity Pdl of the total network parameter are expressed as the following equations:(2)Dl+1=fWlDl+bl,(3)Pdl=d×m+m+m2+m×Ld−1,where Wl denotes the weight item, Dl refers to the output of the previous layer of the network, bl stands for the bias item, d expresses the input dimension, l shows the total number of layers in the network, and m is the number of neurons in each layer. For the analysis of different cross-network layers, the network learning features xl+1 and model complexity Pcl are indicated in the following equations:(4)xl+1=x0xlTwl+bl+xl,(5)Pcl=d∗l∗2,where xl represents the output of the previous network layer. wl and bl are the connection parameters between the two-layer networks. x0xlTwl demonstrates the feature cross-parameter in the network, by obtaining a one-dimensional embedding linear vector and storing it in the connections of the two-layer model. The algorithm recognition process of the optimized song classification and recognition system is displayed in Figure 2.Figure 2
The algorithm recognition process of the optimized song classification and recognition system.


### 3.3. Parameter Setting and Experimental Research

The research uses the optimized and improved hybrid deep neural networks (DNN) model to train the children’s song data, and the training and test data set use the Million Song Dataset to filter the children’s song data contained in the data set. For parameters such as the number of neurons and the number of network layers in the network model, the initial value of the output dimension is 100, the initial connection layer of the network is 4 layers, the number of neurons in each layer is 128, and the number of cross-neurons is 64. The epochs are set to 5, and the batch size is set to 10. Modeling training usually takes a long time and requires predictions on the modeled data. Since each training will waste a lot of time, the trained model should be kept for the next time. The save_model function included in the template is used to save the generated template to a Python file and convert it to binary CatBoost format for faster template loading. The different modules of the established song recognition system such as music melody, style, and emotion are analyzed, and the results are displayed in Figure3.Figure 3
Analysis of different modules of children’s song recognition system.To compare the performance of the proposed recognition and classification system of children’s song, a questionnaire survey is used to investigate the current situation of children’s song usage in three kindergartens A, B, and C in city A. A total of 200 questionnaires are distributed, and 180 questionnaires are effectively returned. The questionnaire is a collection of questions about the teaching and dissemination of children’s songs, mainly including questions such as “what type of children’s songs do you like?” “what emotions are conveyed in children’s songs?” and “what problems exist in the singing of children’s songs?”. SPSS software is used to test the reliability and validity of the questionnaire. The overall reliability coefficient of the scale is 0.93, and the combined validity of the measurement factors is 0.87, illustrating that the reliability and validity of the questionnaire have been verified. The performance of the proposed improved DNN model is compared with that of the traditional model and the convolution neural network (CNN) model, respectively. The performance is analyzed and compared in terms of the accuracy, precision, mean squared error (MSE), and root mean square error (RMSE) of model recognition, and furthermore, analysis is done after statistical results. The calculation methods of MSE and RMSE are analyzed, which are indicated in the following equations:(6)MSE=1n∑i=1ny^i−yi2,(7)RMSE=1n∑i=1ny^i−yi2,yi refers to the actual value, y^i stands for the predicted value, and n represents a total of n sets of data.


## 3.1. Information Security and Random Matrix

The elements of personal information security mainly include three elements: confidentiality, availability, and integrity. Confidentiality means that personal information cannot be stolen. To ensure confidentiality, professional technology is required to encrypt personal information. Availability is that the protection of personal information should be set according to the needs, and the measures taken must be able to truly play a protective effect, to achieve practical results. Integrity refers that the content of personal information should be complete, accurate, and true, and not tampered with by others. At present, the most common technical means to ensure consistency is digital signature. The above three elements are people who need to be fully considered in the process of ensuring personal information security. According to the random matrix, combined with the finite Markov chain to describe the probability data, the recognition and classification model of children’s songs is realized on the basis of computer DL [23]. To make the upper-layer program run on different lower-layer computing platforms, each array interface provided by the system encapsulates the implementation of corresponding array operations on different lower-layer computing platforms. The array interface in the system records the relationship between the operation type of an array and its dependent arrays. According to the relationship between these registers, a calculation flow chart matrix is formed. The random matrix model is used to classify and recognize different types of children’s songs, and the information security of users and songs is stored in the system and model. The frame structure of the mathematical model based on the random matrix is exhibited in Figure 1.Figure 1
The frame structure of the mathematical model based on the random matrix.


## 3.2. Children’s Aesthetic Training Strategy and Structure of Classification and Recognition System of Songs

From the perspective of children’s songs, to cultivate children’s aesthetic consciousness and further enrich students’ aesthetic experience, it is necessary to strengthen children’s aesthetic consciousness by adding aesthetic activities to form their aesthetic sense and creativity [24]. Children and adolescents are in the transition stage from imaginal thinking to abstract thinking. A reasonable gap between aesthetic subject and aesthetic object should be correct [25]. DL algorithm is adopted to identify and learn children’s songs, and a relatively simple children’s song technology is used to imitate the creation process of human composers to achieve the purpose of computer-assisted song recognition and classification. In addition, to express people’s subjective feelings, human-computer interaction (HCI) technology and learning mechanism are added to make music more distinctive. The music produced by many composition systems lacks sufficient musicality and appreciation. To overcome this problem, a learning mechanism is introduced. First, a certain number of domestic and internationally famous children’s songs are systematically studied, and some key parameters are obtained. It includes song length, number of beats, key, audio, the interval between notes, and tempo. These parameters are then applied in the music recognition system to control the operation of the model. That is, each recognition is compared by different song types. The evaluation value and learning parameters are calculated to determine the genetic factors of the next generation. In this way, the final music can ensure that the recognition degree of children’s songs is consistent with traditional children’s songs to a certain extent.In this algorithm model, the discrete input features are converted into dense numerical vectors through the embedding layer, and the input features are combined with numerical features. The input data are nonlinear, and the data in the system are analyzed, wherein the characteristic representation of the input functionx0 of the connection layer of the network data is shown in the following equation (1):(1)x0=xembed,1T…,xembed,kT,xdenseT,where xembed,kT represents the k th transposed eigenvector after the system performs data operations, and xdenseT means the transposed eigenvector of continuous values. For the analysis of the input of the connection layer in the NN, the connection layer Dl+1 of each layer of the network and the complexity Pdl of the total network parameter are expressed as the following equations:(2)Dl+1=fWlDl+bl,(3)Pdl=d×m+m+m2+m×Ld−1,where Wl denotes the weight item, Dl refers to the output of the previous layer of the network, bl stands for the bias item, d expresses the input dimension, l shows the total number of layers in the network, and m is the number of neurons in each layer. For the analysis of different cross-network layers, the network learning features xl+1 and model complexity Pcl are indicated in the following equations:(4)xl+1=x0xlTwl+bl+xl,(5)Pcl=d∗l∗2,where xl represents the output of the previous network layer. wl and bl are the connection parameters between the two-layer networks. x0xlTwl demonstrates the feature cross-parameter in the network, by obtaining a one-dimensional embedding linear vector and storing it in the connections of the two-layer model. The algorithm recognition process of the optimized song classification and recognition system is displayed in Figure 2.Figure 2
The algorithm recognition process of the optimized song classification and recognition system.


## 3.3. Parameter Setting and Experimental Research

The research uses the optimized and improved hybrid deep neural networks (DNN) model to train the children’s song data, and the training and test data set use the Million Song Dataset to filter the children’s song data contained in the data set. For parameters such as the number of neurons and the number of network layers in the network model, the initial value of the output dimension is 100, the initial connection layer of the network is 4 layers, the number of neurons in each layer is 128, and the number of cross-neurons is 64. The epochs are set to 5, and the batch size is set to 10. Modeling training usually takes a long time and requires predictions on the modeled data. Since each training will waste a lot of time, the trained model should be kept for the next time. The save_model function included in the template is used to save the generated template to a Python file and convert it to binary CatBoost format for faster template loading. The different modules of the established song recognition system such as music melody, style, and emotion are analyzed, and the results are displayed in Figure3.Figure 3
Analysis of different modules of children’s song recognition system.To compare the performance of the proposed recognition and classification system of children’s song, a questionnaire survey is used to investigate the current situation of children’s song usage in three kindergartens A, B, and C in city A. A total of 200 questionnaires are distributed, and 180 questionnaires are effectively returned. The questionnaire is a collection of questions about the teaching and dissemination of children’s songs, mainly including questions such as “what type of children’s songs do you like?” “what emotions are conveyed in children’s songs?” and “what problems exist in the singing of children’s songs?”. SPSS software is used to test the reliability and validity of the questionnaire. The overall reliability coefficient of the scale is 0.93, and the combined validity of the measurement factors is 0.87, illustrating that the reliability and validity of the questionnaire have been verified. The performance of the proposed improved DNN model is compared with that of the traditional model and the convolution neural network (CNN) model, respectively. The performance is analyzed and compared in terms of the accuracy, precision, mean squared error (MSE), and root mean square error (RMSE) of model recognition, and furthermore, analysis is done after statistical results. The calculation methods of MSE and RMSE are analyzed, which are indicated in the following equations:(6)MSE=1n∑i=1ny^i−yi2,(7)RMSE=1n∑i=1ny^i−yi2,yi refers to the actual value, y^i stands for the predicted value, and n represents a total of n sets of data.


## 4. Results and Discussion

### 4.1. Comparison and Analysis of Model Performance

Figure4 refers to the results of sorting out the data on the accuracy of children’s song recognition by the system. In addition, when the number of experimental iterations increases, the data of the MSE and RMSE of the system for children’s song recognition are compared, and the results are demonstrated in Figure 5.Figure 4
The data change curve of the recognition accuracy and precision of the system for children’s song. (a)The change curve of the system’s recognition accuracy for children’s song with the increase of the number of model iterations. (b)The change curve of the system’s recognition precision for children’s song with the increase of the number of model iterations.
(a)(b)Figure 5
Comparison and analysis of the MSE and RMSE recognized by the system for children’s song. (a)The change curve of the MSE is recognized by the children’s song with the increase in the number of experimental iterations. (b)The change curve of the RMSE is recognized by the children’s song with the increase in the number of experimental iterations.
(a)(b)Figure4 manifests that with the increase in the number of model iterations, the recognition accuracy and precision of several algorithms are significantly improved. When the model is iterated 200 times, the recognition accuracy of the traditional algorithm for children’s song is only 0.73, and the recognition accuracy of the CNN model can reach 0.76, but the proposed improved DNN algorithm model has the best recognition accuracy, which can reach 0.80. When the model is iterated 400 times, the recognition precision of the system is only 0.78. After 1200 iterations of the model, the recognition precision of the system can reach 0.82. The research results signify that the proposed children’s song recognition and classification system have high recognition accuracy and precision, and the speed and accuracy of recognition processing of system task can be greatly improved.It expresses from the comparison results of the MSE and RMSE of the children’s song recognized by the system in Figure5 that the change trends of the MSE and RMSE of the two algorithms in the system are relatively stable. When the model is 200 iterations, the MSE of the traditional algorithm and the proposed DNN algorithm is all 0.7. With the increase in the number of model iterations, when the model is 1200 iterations, the MSE of the traditional algorithm increases to 0.8, while the MSE of the improved DNN algorithm can be stabilized at about 0.7. Besides, the average RMSE of the traditional algorithm is 0.7, while the average RMSE of the improved DNN algorithm is about 0.65, which can be greatly improved compared with the traditional algorithm.


### 4.2. Evaluation of Sound Quality and Data Information Transmission Rate of Children’s Song

In the system model, to compare the performance of different algorithm models, it is necessary to analyze and evaluate the sound quality of different children’s songs. The change data of the children’s song’s sound quality and data information transmission rate are sorted out, and the results are indicated in Figure6.Figure 6
Evaluation of the sound quality and data information transmission rate of children’s song. (a)The change curve of the sound quality of children’s song with the increase of the number of model iterations. (b)The change data curve of data information transmission rate of the system with the increase of the number of model iterations.
(a)(b)The change curve of the sound quality of children’s song with the number of model iterations in Figure6 refers that with the increase in the number of model iterations in the system, the sound quality of the song can be improved to a certain extent. When the model is iterated for 200 times, the traditional algorithm can improve the sound quality of the song to a certain extent. The quality score of children’s song is only 0.5. When the model is 200 iterations, the information data transmission rate of the two algorithms is about 0.25. With the increase in the number of model iterations, when the model is 1200 iterations, the information data transmission rate of the traditional algorithm is 1.75, while the DNN algorithm can reach 2.0, and the improvement of algorithm performance can complete more data transmission tasks in a shorter time.


## 4.1. Comparison and Analysis of Model Performance

Figure4 refers to the results of sorting out the data on the accuracy of children’s song recognition by the system. In addition, when the number of experimental iterations increases, the data of the MSE and RMSE of the system for children’s song recognition are compared, and the results are demonstrated in Figure 5.Figure 4
The data change curve of the recognition accuracy and precision of the system for children’s song. (a)The change curve of the system’s recognition accuracy for children’s song with the increase of the number of model iterations. (b)The change curve of the system’s recognition precision for children’s song with the increase of the number of model iterations.
(a)(b)Figure 5
Comparison and analysis of the MSE and RMSE recognized by the system for children’s song. (a)The change curve of the MSE is recognized by the children’s song with the increase in the number of experimental iterations. (b)The change curve of the RMSE is recognized by the children’s song with the increase in the number of experimental iterations.
(a)(b)Figure4 manifests that with the increase in the number of model iterations, the recognition accuracy and precision of several algorithms are significantly improved. When the model is iterated 200 times, the recognition accuracy of the traditional algorithm for children’s song is only 0.73, and the recognition accuracy of the CNN model can reach 0.76, but the proposed improved DNN algorithm model has the best recognition accuracy, which can reach 0.80. When the model is iterated 400 times, the recognition precision of the system is only 0.78. After 1200 iterations of the model, the recognition precision of the system can reach 0.82. The research results signify that the proposed children’s song recognition and classification system have high recognition accuracy and precision, and the speed and accuracy of recognition processing of system task can be greatly improved.It expresses from the comparison results of the MSE and RMSE of the children’s song recognized by the system in Figure5 that the change trends of the MSE and RMSE of the two algorithms in the system are relatively stable. When the model is 200 iterations, the MSE of the traditional algorithm and the proposed DNN algorithm is all 0.7. With the increase in the number of model iterations, when the model is 1200 iterations, the MSE of the traditional algorithm increases to 0.8, while the MSE of the improved DNN algorithm can be stabilized at about 0.7. Besides, the average RMSE of the traditional algorithm is 0.7, while the average RMSE of the improved DNN algorithm is about 0.65, which can be greatly improved compared with the traditional algorithm.


## 4.2. Evaluation of Sound Quality and Data Information Transmission Rate of Children’s Song

In the system model, to compare the performance of different algorithm models, it is necessary to analyze and evaluate the sound quality of different children’s songs. The change data of the children’s song’s sound quality and data information transmission rate are sorted out, and the results are indicated in Figure6.Figure 6
Evaluation of the sound quality and data information transmission rate of children’s song. (a)The change curve of the sound quality of children’s song with the increase of the number of model iterations. (b)The change data curve of data information transmission rate of the system with the increase of the number of model iterations.
(a)(b)The change curve of the sound quality of children’s song with the number of model iterations in Figure6 refers that with the increase in the number of model iterations in the system, the sound quality of the song can be improved to a certain extent. When the model is iterated for 200 times, the traditional algorithm can improve the sound quality of the song to a certain extent. The quality score of children’s song is only 0.5. When the model is 200 iterations, the information data transmission rate of the two algorithms is about 0.25. With the increase in the number of model iterations, when the model is 1200 iterations, the information data transmission rate of the traditional algorithm is 1.75, while the DNN algorithm can reach 2.0, and the improvement of algorithm performance can complete more data transmission tasks in a shorter time.


## 5. Conclusion

In the current era of IoT, data information has become a vital strategic resource for all walks of life. In this context, people’s aesthetic and spiritual needs pay more attention to protecting information security and private data. It takes children’s songs as the basic starting point and uses the questionnaire survey method to investigate the current situation of children’s song usage in three kindergartens in A, B, and C in a city. A total of 200 questionnaires are distributed, and 180 questionnaires are effectively returned. SPSS software is used to test the reliability and validity of the questionnaire. The overall reliability coefficient of the scale is 0.93, and the combined validity of the measurement factors is 0.87, indicating that the reliability and validity of the questionnaire are verified. An efficient information hiding scheme conforming to random matrix is proposed. By establishing a random matrix to optimize the information scheme hidden in the multinational children’s songs, the textual expression efficiency of children’s songs is improved. The performance test results of the algorithm prove that when the model is iterated 200 times, the quality score of the traditional children’s songs algorithm is only 0.5. When the model is iterated 1200 times, the quality score can reach 0.65. But when the DNN algorithm iterates 1000 times, the quality score of the children’s song can reach 0.7. However, some disadvantages are unavoidable. The main disadvantage lies in the use of matrix functions and mathematical models to recognize and classify children’s songs, but the logic of the algorithm needs to be further optimized. In future research, it is necessary to collect more children’s song data for the existing song classification and recognition system and optimize the model parameters, thereby further improving the accuracy of model recognition.



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*Source: 4521245-2022-08-09.xml*