# Effect of Evaluation of Popular Song Creation and Literature Based on RITNN Model

Authors: Rui Song
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

The era of modernity and information technology has entered people's lives, which has brought new challenges to innovative culture and art to a great extent, especially changing the relationship between traditional culture and pop music creation. In order to better automate music creation, this paper proposes a mixed model of cultural term evaluation of Chinese pop songs based on RITNN, which sets up RNN structure composition models of different channels. Through this method, a data reconstruction model of important cultural elements of simultaneous interpretation is established. The experiment fully proves that the pop music creation in traditional cultural materials not only integrates the essence of traditional cultural elements but also makes new creation. This scheme can evaluate the integration effect of traditional cultural elements in pop music creation.


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## Body

## 1. Introduction

The age of modernity and information technology has entered our lives. New materials and new technologies are emerging all the time, and we are overwhelmed by them to a large extent; this has brought new challenges to innovative culture and art [1–3]. The text therefore analyses the successful cases of pop music creation in China and abroad, analyses the fusion and innovation of traditional musical elements in pop music creation, and explores the essence of traditional culture to experience the fusion and creation of new musical concepts.In the course of the long development of society, people have created a brilliant culture and art. The art of singing is one of the oldest; from the ancient rock paintings of Dunhuang to the opera “Silk Road” [4, 5]; from the old records of the industrial revolution to the electronic music of high technology; from the early whispers of pop songs to the current star-studded pop scene. People have come a long way from ancient time. The creative arts have also progressed and developed; and these have never left the traditional culture in the journey of history [6]. Traditional culture has made to the present day. The achievements of today are the traditional culture of the future. Excellent traditional culture has always been the source of water on which humanity depends. So, while people are enjoying modern culture, they should cherish traditional culture, but the youth of today has a shallow understanding of traditional culture. After the reform and opening up, songs such as “I Only Care About You” by Teresa Teng and “The Fragrance of the Night” by Xu Xiaofeng were introduced to the mainland, but many people did not know that these songs were old pop songs from Shanghai [7–9].In fact, in the 1930s and 1940s, Chinese pop music spread both at home and abroad, with present-day Malaysian singers such as Wu Qixian and Hua Yi Bao, Pan Xiuqiong, and Sun Yanzhi, all of whom sang songs from this period. To this day, we can also hear the classic songs of this period in many film and television productions. It is easy to see that the creation of the so-called pop songs is based on traditional culture. For example, as Lai Jinhui, the founder of pop music [10–12], said: “I took as my material the poetry of famous writers from the past and present, Western poetry, folk ditties, songs in the native style, as well as Western ditties and love songs from the South Pacific area.” So the main characteristic of early Chinese pop songs is that “Chinese pop songs are the Chinese version of world pop music, and they are directly or indirectly connected with the international community, not only maintaining the traditional nature of their own national music culture, but also cleverly combining with foreign culture.”In the mid-nineteenth century, the song “Shanghai by Night,” with lyrics by Fan Yanqiao and music by Chen Gexin, became an iconic piece of music in Shanghai. The lyrics of “Shanghai by Night” reflect the complex, helpless, miserable, uncertain, and awakening inner activities of the protagonist in the midst of the glamour of the nightclubs and the monologue of his heart. The lyrics of “Shanghai by Night” are also based on the stanza structure of song lyrics and Yuan songs, and fully express the helplessness and awakening of the old Shanghai dancers. This is why in the late 1990s, Hong Kong's Great Wall Pictures brought “Shanghai by Night” to the big screen [13]. This is a classic song that can be found here. This is why in the last eighty years of Chinese pop music and its songwriting, there has been a strong sense of nationalism and an openness to embrace elements of global culture. In many major international song competitions, very little traditional music has won awards. On the contrary, those who create music that incorporates traditional culture and ethnicity are more likely to attract the attention of the judges. So, there is no such thing as a single traditional culture, only a fusion of innovative culture and modernity can be truly innovative.


## 2. Integration and Innovation of Traditional Cultural Elements and Popular Songwriting

The fusion of traditional culture and pop music is mainly based on the word “new.” This innovation lies in the fact that the compositions are drawn from tradition, but are not simply repetitions, copies, or displaced variations, but rather extracts some of the cultural essence from traditional cultural elements, expresses it with a modern sense of innovation, and recreates it [14–16]. As a result, new works are created that not only leave behind the traditional artistic flavour but are also very contemporary and have a new popular artistic image. In 1986, the first concert of the singers was a great success. The song “Let the world be full of love” became a national hit. From then on, pop music composers were emerging and the pop stars were gathering, and pop music began to diversify and explore. The song has been around for more than a decade, but “Sea grows the sky” still has the spirit of the band beyond. This is what beyond had to endure in their songwriting. But that is what made beyond the avant-garde band that they are, and their appeal has remained intact for decades. As the lyrics go, “The sea is wide, you and I, but how many times have we faced the scorn and ridicule? We've never given up on our ideals.” It is for this reason that beyond has created music based on traditional cultural elements that are contemporary and encourage dreamers to follow their dreams. They are at the forefront of music creation, both spiritually and mentally, and it is their innovation that gives us the spiritual strength to pursue and innovate, and they have created a work that will be remembered and remembered as the most valuable for us to learn from.


### 2.1. Robust Input Training Neural Network

The RITNN consists of 3 parts: the input layer, the hidden layer, and the output layer, as shown in Figure1 [17].Figure 1
Illustration of the RITNN network structure.The RITNN network is trained using the error backpropagation gradient descent method to adjust the internal weights of the network as well as the input values, andm sets of training samples containing n variables are normalized to obtain tpk, and the network training objective function is(1)E=12∑p=1m∑k=1nzpk−tpk2,where zpk is the network output value.The activation function of the hidden layer of the network uses a single-level Sigmoidal functionσx=1/1+e−x, while the input and output layers use a linear activation function fx=x so that zpk can be expressed as(2)zpk=∑jw2jkσbj+∑jw1ijxpi,where w1ij and w2jk are the network weights between the input layer and the hidden layer, respectively; bj is the jth hidden layer node threshold, and the network parameters are adjusted as follows:(3)Δxpi=∑jw1ijσ′bj+∑iw1ijxpi∑kw2jktpk−zpk,where σ′ is the derivative of σ.(4)Δw1ij=∑pxpiσ′bj+∑ixpiw1ij∑kw2jktpk−zpk,Δw2jk=∑pσbj+∑ixpiw1ijtpk−zpk.In general, when the reliability of a cultural element is high and the probability of occurrence of a cultural element is small, it corresponds to a larger reliability coefficient; conversely, a cultural element parameter that is prone to occurrence of a cultural element corresponds to a smaller reliability coefficient [18–20]. When a cultural element occurs, RITNN reduces the weight of the cultural element parameter error in the objective function through the reliability coefficient, reducing the residual contamination of other parameters and providing a reliable guarantee for the next influential factor function.


## 2.1. Robust Input Training Neural Network

The RITNN consists of 3 parts: the input layer, the hidden layer, and the output layer, as shown in Figure1 [17].Figure 1
Illustration of the RITNN network structure.The RITNN network is trained using the error backpropagation gradient descent method to adjust the internal weights of the network as well as the input values, andm sets of training samples containing n variables are normalized to obtain tpk, and the network training objective function is(1)E=12∑p=1m∑k=1nzpk−tpk2,where zpk is the network output value.The activation function of the hidden layer of the network uses a single-level Sigmoidal functionσx=1/1+e−x, while the input and output layers use a linear activation function fx=x so that zpk can be expressed as(2)zpk=∑jw2jkσbj+∑jw1ijxpi,where w1ij and w2jk are the network weights between the input layer and the hidden layer, respectively; bj is the jth hidden layer node threshold, and the network parameters are adjusted as follows:(3)Δxpi=∑jw1ijσ′bj+∑iw1ijxpi∑kw2jktpk−zpk,where σ′ is the derivative of σ.(4)Δw1ij=∑pxpiσ′bj+∑ixpiw1ij∑kw2jktpk−zpk,Δw2jk=∑pσbj+∑ixpiw1ijtpk−zpk.In general, when the reliability of a cultural element is high and the probability of occurrence of a cultural element is small, it corresponds to a larger reliability coefficient; conversely, a cultural element parameter that is prone to occurrence of a cultural element corresponds to a smaller reliability coefficient [18–20]. When a cultural element occurs, RITNN reduces the weight of the cultural element parameter error in the objective function through the reliability coefficient, reducing the residual contamination of other parameters and providing a reliable guarantee for the next influential factor function.


## 3. Collaborative Songwriting Based on Multiple RITNN Models

### 3.1. Multiple RITNN Model Data Reconstruction

Although RITNN has a certain suppression effect on the residual effects of cultural element data when dealing with multiple cultural elements, the number of cultural elements and pop song composition cultural elements is large, and if numerous measurement parameters are built into only 1 RITNN model, it will lead to a decrease in modelling accuracy; in addition, the number of high reliability measurement points in the field is very limited. In order to ensure the accuracy of reconstructed data in the case of multiple cultural elements occurring at the same time, this paper adopts the collaborative data reconstruction method of multiple RITNN models, grouping cultural elements of popular song composition through the analysis of the mechanistic relationship between parameters and the correlation of historical data, establishing multiple RITNN models [21–23], and dividing the model priorities according to the order of data reconstruction, in series and parallel manner to form a complete data reconstruction module.The modelling process of the multi-RITNN model collaborative data reconstruction method is shown in Figure2, where the first step is to establish a high reliability measurement point dataset, which only contains high reliability measurement point data in the initial state. The parameters of the selected and completed cultural elements will become new reliable measurement points, which will be updated to the high reliability measurement point dataset. The process is repeated until a data reconstruction model is built for all cultural elements. As each round of modelling is completed, the priority of the reconstructed model is set, with the earlier the model is built, the higher the priority will be.Figure 2
Modelling process.Cultural elements and pop songwriting operate in a hostile environment for long periods of time and are prone to degradation and other cultural elements that can lead to inconsistent relationships. Therefore, it is necessary to update the model parameters to eliminate the effects of changes in degraded cultural elements on the fusion of popular songwriting.


## 3.1. Multiple RITNN Model Data Reconstruction

Although RITNN has a certain suppression effect on the residual effects of cultural element data when dealing with multiple cultural elements, the number of cultural elements and pop song composition cultural elements is large, and if numerous measurement parameters are built into only 1 RITNN model, it will lead to a decrease in modelling accuracy; in addition, the number of high reliability measurement points in the field is very limited. In order to ensure the accuracy of reconstructed data in the case of multiple cultural elements occurring at the same time, this paper adopts the collaborative data reconstruction method of multiple RITNN models, grouping cultural elements of popular song composition through the analysis of the mechanistic relationship between parameters and the correlation of historical data, establishing multiple RITNN models [21–23], and dividing the model priorities according to the order of data reconstruction, in series and parallel manner to form a complete data reconstruction module.The modelling process of the multi-RITNN model collaborative data reconstruction method is shown in Figure2, where the first step is to establish a high reliability measurement point dataset, which only contains high reliability measurement point data in the initial state. The parameters of the selected and completed cultural elements will become new reliable measurement points, which will be updated to the high reliability measurement point dataset. The process is repeated until a data reconstruction model is built for all cultural elements. As each round of modelling is completed, the priority of the reconstructed model is set, with the earlier the model is built, the higher the priority will be.Figure 2
Modelling process.Cultural elements and pop songwriting operate in a hostile environment for long periods of time and are prone to degradation and other cultural elements that can lead to inconsistent relationships. Therefore, it is necessary to update the model parameters to eliminate the effects of changes in degraded cultural elements on the fusion of popular songwriting.


## 4. Example Analysis and Validation

### 4.1. Fusion Composition of Popular Songs

The most recent historical data were selected as the test data, with a sampling interval of 10 s. A total of 3,000 sets of data were collected, and a random error of 10% to 50% was superimposed on the 1,300 to 2,200 sets of data as the culture element test sample [24, 25]. The cultural element types included accuracy degradation, drift, constant deviation, and complete failure.RMSE (F) and RMSE (H) were calculated separately for the cultural element data segment of the test sample to measure the reconstruction accuracy of the cultural element variables and the extent to which the health variables were contaminated by residuals, as shown in equations (5) and (6). The smaller the RMSE (F), the higher the reconstruction accuracy of the cultural element variables; the smaller the RMSE (H), the less the health variables are contaminated by the residuals of the cultural element parameters, and the lower the error detection rate of the corresponding healthy cultural elements [26–28].(5)RMSEF=1m1nF∑p=1m∑k=1nFzpk−tpk2,(6)RMSEH=1m1nH∑p=1m∑k=1nHzpk−tpk2,where nF is the number of failed sensors in one test sample; nH is the number of healthy cultural elements in the same test sample; m is the number of cultural element test samples; tpk is the raw data normalisation result for the cultural element samples; zpk is the network output value.5 cultural elements and concurrent cultural elements. The RMSE (F) distribution, RMSE (H) distribution, and natural logarithm of error detection rate ln (T) for each group of 10 randomly selected parameter combinations of 5 cultural elements for the multiple RITNN model synergy approach, single RITNN model approach, and single ITNN model approach are shown in Figure 3.Figure 3
5 comparisons of cultural elements and concurrent cultural elements.10 sensor concurrent failure. The RMSE (F) distribution, RMSE (H) distribution, and natural logarithm of the error detection rate ln (T) for each of the 10 groups of 10 cultural elements selected randomly for the multi-RITNN model synergy approach, the single RITNN model approach, and the single ITNN model approach are shown in Figure 4.Figure 4
10 comparisons of cultural elements and concurrent cultural elements.20 sensors with concurrent failure. The distribution of RMSE (F) [29], RMSE (H), and the natural logarithm of the error detection rate ln (T) for each of the 10 sets of 20 randomly selected cultural element parameter combinations for the multiple RITNN model synergy approach, the single RITNN model approach, and the single ITNN model approach are shown in Figure 5.Figure 5
Comparison of 20 cultural elements and concurrent cultural elements.A status determination was made for the ITNN failed data segments [29–32] of the song parametric test samples, and the results of the 3 methods of diagnosis are shown in Table 1. Figure 6 shows the data reconstruction of the four cultural element variables for one of the 20 culture element concurrent cultural element groups, with the culture element types of reduced precision, complete failure, drift, and constant deviation.Table 1
Results of the 3 methods of popular songwriting.
Average detection numberAverage number of missed inspectionsAverage number of false detectionsAverage detection rate%Average false detection rateSingle-ITNN model5 fault6.070.0291.1099.42.210 fault17.450.0267.4799.716.620 fault29.670.0529.7299.727.8Single-RITNN model5 fault5.80.0310.8399.41.710 fault11.620.0621.6899.43.720 fault23.110.0263.1499.99.0Collaborative multi model5 fault5.640.0000.67100.01.310 fault10.900.0130.9199.92.020 fault21.160.0211.2499.93.5Figure 6
Reconstruction results for 3,000 sets of test data for 4 cultural element variables. (a) Qf, (b) T7L, (c) Pht, and (d) Th.
(a)(b)(c)(d)As can be seen from Figures4–6 and Table 1, the accuracy of the single RITNN model method and the single ITNN model method for reconstructing data decreases in the case of simultaneous failure of multiple cultural elements, and although a certain detection rate of cultural elements can still be guaranteed, the residual contamination of cultural element data has a greater impact, resulting in a higher false detection rate of health data. The detection rate of cultural elements in this paper was found to be high. The paper's series-parallel multiple RITNN models can effectively overcome the reconstruction difficulties caused by multiple target cultural elements, and the reconstruction accuracy of cultural element parameters is maintained at a high level, and the detection rate of cultural elements is nearly 100%, and the influence of significant errors in cultural element data is well suppressed, which greatly reduces the residual pollution and keeps the false detection rate of health data in a low range, and significantly improves the accuracy of the accuracy of popular songwriting.


## 4.1. Fusion Composition of Popular Songs

The most recent historical data were selected as the test data, with a sampling interval of 10 s. A total of 3,000 sets of data were collected, and a random error of 10% to 50% was superimposed on the 1,300 to 2,200 sets of data as the culture element test sample [24, 25]. The cultural element types included accuracy degradation, drift, constant deviation, and complete failure.RMSE (F) and RMSE (H) were calculated separately for the cultural element data segment of the test sample to measure the reconstruction accuracy of the cultural element variables and the extent to which the health variables were contaminated by residuals, as shown in equations (5) and (6). The smaller the RMSE (F), the higher the reconstruction accuracy of the cultural element variables; the smaller the RMSE (H), the less the health variables are contaminated by the residuals of the cultural element parameters, and the lower the error detection rate of the corresponding healthy cultural elements [26–28].(5)RMSEF=1m1nF∑p=1m∑k=1nFzpk−tpk2,(6)RMSEH=1m1nH∑p=1m∑k=1nHzpk−tpk2,where nF is the number of failed sensors in one test sample; nH is the number of healthy cultural elements in the same test sample; m is the number of cultural element test samples; tpk is the raw data normalisation result for the cultural element samples; zpk is the network output value.5 cultural elements and concurrent cultural elements. The RMSE (F) distribution, RMSE (H) distribution, and natural logarithm of error detection rate ln (T) for each group of 10 randomly selected parameter combinations of 5 cultural elements for the multiple RITNN model synergy approach, single RITNN model approach, and single ITNN model approach are shown in Figure 3.Figure 3
5 comparisons of cultural elements and concurrent cultural elements.10 sensor concurrent failure. The RMSE (F) distribution, RMSE (H) distribution, and natural logarithm of the error detection rate ln (T) for each of the 10 groups of 10 cultural elements selected randomly for the multi-RITNN model synergy approach, the single RITNN model approach, and the single ITNN model approach are shown in Figure 4.Figure 4
10 comparisons of cultural elements and concurrent cultural elements.20 sensors with concurrent failure. The distribution of RMSE (F) [29], RMSE (H), and the natural logarithm of the error detection rate ln (T) for each of the 10 sets of 20 randomly selected cultural element parameter combinations for the multiple RITNN model synergy approach, the single RITNN model approach, and the single ITNN model approach are shown in Figure 5.Figure 5
Comparison of 20 cultural elements and concurrent cultural elements.A status determination was made for the ITNN failed data segments [29–32] of the song parametric test samples, and the results of the 3 methods of diagnosis are shown in Table 1. Figure 6 shows the data reconstruction of the four cultural element variables for one of the 20 culture element concurrent cultural element groups, with the culture element types of reduced precision, complete failure, drift, and constant deviation.Table 1
Results of the 3 methods of popular songwriting.
Average detection numberAverage number of missed inspectionsAverage number of false detectionsAverage detection rate%Average false detection rateSingle-ITNN model5 fault6.070.0291.1099.42.210 fault17.450.0267.4799.716.620 fault29.670.0529.7299.727.8Single-RITNN model5 fault5.80.0310.8399.41.710 fault11.620.0621.6899.43.720 fault23.110.0263.1499.99.0Collaborative multi model5 fault5.640.0000.67100.01.310 fault10.900.0130.9199.92.020 fault21.160.0211.2499.93.5Figure 6
Reconstruction results for 3,000 sets of test data for 4 cultural element variables. (a) Qf, (b) T7L, (c) Pht, and (d) Th.
(a)(b)(c)(d)As can be seen from Figures4–6 and Table 1, the accuracy of the single RITNN model method and the single ITNN model method for reconstructing data decreases in the case of simultaneous failure of multiple cultural elements, and although a certain detection rate of cultural elements can still be guaranteed, the residual contamination of cultural element data has a greater impact, resulting in a higher false detection rate of health data. The detection rate of cultural elements in this paper was found to be high. The paper's series-parallel multiple RITNN models can effectively overcome the reconstruction difficulties caused by multiple target cultural elements, and the reconstruction accuracy of cultural element parameters is maintained at a high level, and the detection rate of cultural elements is nearly 100%, and the influence of significant errors in cultural element data is well suppressed, which greatly reduces the residual pollution and keeps the false detection rate of health data in a low range, and significantly improves the accuracy of the accuracy of popular songwriting.


## 5. Conclusions

The above analysis of the songs shows that modern pop music carries all the ideas of today's life. It is the most typical, direct, and quickest way of creating music that maps out the reality of people's lives, using Koranic media technology to awaken and inspire self-confidence and pride. The creation of today's pop music is a sign of cultural progress. People are putting a lot of information into music. The visualisation of notes and words is with simple, individual words. There is a strong call to create a new atmosphere of a spirit. The main trend in today's society is the exchange, fusion, and innovation of Chinese and foreign cultures. In our pluralistic and integrated society, it is important to have not only the characteristics of the local traditional culture but also the pioneering ideas of modern music creation. It is only in this way that the creators of popular music can continue to innovate and develop, as there is a collision of new elements. Only then can they stand on the platform at the time, thus enriching their cultural heritage and drawing on various genes. This is how traditional guitars and pop music can be fused and innovated. Inheriting traditional cultural elements and developing advanced pop music culture, this will lead to innovative forms of music art and promote the development of Chinese music art.



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*Source: 4643674-2022-05-14.xml*