# Construction and Application of Music Audio Database Based on Collaborative Filtering Algorithm

Authors: Jianfei Zheng
Journal: Discrete Dynamics in Nature and Society
 (2022)

---

## Abstract

In recent years, with the development of Big Data and dynamic modeling technology, people have gradually deepened the research on the efficient construction of music audio database. Based on this background, this paper first realizes the construction of multilevel music audio database using Big Data technology and collaborative filtering algorithm. Then, through the Big Data analysis technology based on collaborative filtering algorithm, the QRE (quick reaction estimate) model is constructed and the data query system is formed. Finally, experiments are designed to verify whether the music audio database based on collaborative filtering algorithm can correctly retrieve the target music audio data. The experimental results show that compared with the traditional single∗input stacked database, the music and audio database constructed by the discrete Big Data dynamic modeling technology based on collaborative filtering algorithm has faster search rate and higher accuracy and can accurately locate the data nodes in the database. This research uses Big Data and collaborative filtering algorithm technology to establish a new multilevel music audio database with discrete dynamic modeling characteristics of complex systems, which can greatly improve the data structure of each level of the database, so as to improve the efficiency and accuracy of retrieving various types of music and audio files in the database.


---

## Body

## 1. Introduction

At present, most music and audio database systems generally use digital simulation technology and database stacking method for completion, but the connection between the database and the outside world must also use LAN or Internet. Therefore, there are some problems, such as low database security, poor user experience, and low retrieval matching degree [1]. At present, more than 60% of all kinds of databases still use fuzzy cooperative matching tracking algorithm in the design process [2]. Although the scale proportion of analog part in digital simulation technology will not exceed 1/4, the error rate of text data bytes from analog part is as high as more than 42% [3]. This problem leads to a significant increase in the proportion of simulated retrieval time and finally directly reduces the retrieval efficiency of the database [4]. Due to the increasingly large data set and increasing internal correlation data of music and audio database, the controllability and predictability of each node data are not high, so it is difficult to judge and determine the wrong database node [5]. Based on this, this paper proposes a music audio database construction method based on Big Data analysis strategy and collaborative filtering algorithm.This paper studies the construction method and application idea of music audio database and puts forward the construction method of music audio database based on Big Data analysis strategy and collaborative filtering algorithm, which is mainly divided into four chapters. Section1 summarizes the research background and research content framework. Section 2 introduces the research status of music audio database construction and the research status and shortcomings of pure digital simulation music audio database and summarizes the research deficiencies of music audio database construction at present. Section 3 introduces Big Data technology, deep learning network, and QRE model, respectively. According to collaborative filtering algorithm and Big Data analysis strategy, a music audio database with discrete dynamic modeling technology is constructed. Section 4 uses the established database model to realize the in-depth learning and retrieval process practice of the database through a large number of music and audio data and LVQ data analysis strategies and designs confirmatory experiments to verify whether the optimized database can accurately locate the location of the target music and audio files.The innovation of this paper is that Big Data and collaborative filtering algorithm technology are used in the process of establishing database. Combined with Big Data analysis strategy and complex system discrete dynamic modeling technology, collaborative filtering algorithm can deeply analyze and optimize the hierarchical structure of the database and solve the cost and efficiency problems of traditional music audio database. Based on this, this study uses Big Data and collaborative filtering algorithm technology to establish a new multilevel music and audio database with discrete dynamic modeling characteristics of complex systems, which can greatly improve the data structure of each level of the database and then improve the efficiency and accuracy of retrieving various types of music and audio files in the database.


## 2. Related Work

After years of development of database technology, there are still some deficiencies in the establishment, operation, maintenance, and upgrading of intelligent music audio database [6]. Yin et al. found that most university music sound databases ignore the recycling method of the database in the establishment process, so the data retrieval efficiency of music sound database cannot reach the optimal state [7]. Laishram and Padmanabhan proposed an automatic retrieval database model based on chaotic collaborative filtering algorithm. By analyzing the logical bottom layer and sentence characteristics of various audio systems, different music data are logically classified in different ways, so as to realize efficient retrieval in the database system [8]. According to the traditional establishment mode and practical experience of music audio database, Alhijawi et al. found that the current database has the problem of complex retrieval path in the retrieval process, so they developed a new retrieval path optimization algorithm based on Big Data technology [9]. Kuo et al. have proved through experiments that the classified retrieval method can play a good role in differential retrieval, effectively improve the accuracy of retrieval, and use a number of indicators to scientifically evaluate the modeling ability of the database [10]. Chen et al. proposed a collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering. The diversity experiment results show that this method has excellent performance in prediction accuracy and recommendation accuracy [11]. By analyzing and adjusting the logical path of different database systems, Kim et al. made the database reach a state of deep query in the construction process. Experiments show that the database established by this method can well improve the error correction and correction ability of the database in the retrieval process [12]. In order to improve the retrieval efficiency and overall stability of music audio database, Agnihotry et al. put forward a hierarchical retrieval method based on retrieval theory through various research and analysis of different text systems [13]. Zhao et al. presented an effective and scientific construction method of typical driving conditions of electric vehicles and studied the energy consumption and equivalent emission of electric vehicles on this basis [14]. High-frequency electromagnetic field monitoring is very important to the environment. The recording, continuous observation, and evaluation of harmful factor levels shall be checked by systematic and reliable measurement means. The purpose of Michałowska et al. is to verify and analyze whether modern mobile phones are safe to users during calls [15]. Huang et al. verified through repeated practice. The final results show that the establishment method of multiple structures can improve the retrieval efficiency of the database and the effectiveness of correcting data. It is suitable for continuous R&D planning of music and audio database and finding the optimal data structure scheme [16]. Yang et al. put forward a new method to establish a group music and sound database based on multifactors, which uses the multidimensional space frame sequence to misplace the data packets of each layer of the original music and sound database and realizes the optimal determination in the retrieval process of music and sound files [17]. Yang et al. have proved through practice for many times that the establishment scheme of clustered database has good data retrieval effect and is suitable for online retrieval of various types of music and audio data [18].To sum up, the current research on database construction focuses on the construction of stacked database method and database retrieval efficiency, and there is little research on the application of database construction combined with Big Data technology based on intelligent algorithm [19, 20]. On the contrary, the research on the hierarchical management and diversified construction of music and audio data mostly focuses on the theoretical analysis and research results, rarely constructs the database with complex system characteristics, does not pay attention to the practical application relying on discrete dynamic modeling technology, and does not establish the model of intelligent retrieval and correction for various types of text systems [21–23]. Therefore, it is of great significance to study the construction and application of music audio database based on collaborative filtering algorithm.


## 3. Methodology

### 3.1. Application of Big Data Technology Based on Collaborative Filtering Algorithm in the Construction of the Music Audio Database

Collaborative filtering recommendation algorithm is one of the earliest and well-known recommendation algorithms. Its main function is to realize prediction and recommendation according to specific requirements after in-depth learning of data. For example, in the commercial application of accurate push of goods to users, the algorithm finds users’ preferences through mining users’ historical behavior data, Group users based on different preferences and recommend products with similar tastes [24]. Collaborative filtering recommendation algorithms are divided into two categories: user-based collaborative filtering algorithms and item-based collaborative filtering algorithms [25]. Collaborative filtering algorithm is a complex Big Data analysis and deep learning strategy, which has achieved far more results in speech and data information extraction than previous related technologies [26]. Modern database, whether storage system or retrieval system, is becoming more and more complex. Objectively, it puts forward higher requirements for the database from the initial design to the state monitoring, control, maintenance, and management methods in the process of use. There are a lot of complex and nonlinear engineering optimization problems in the process of database design, manufacturing and use, and these problems are difficult to be solved by common linear equations and determined mathematical models. Therefore, using new optimization methods, such as deep learning and data analysis strategy to solve these problems has become the research goal of building intelligent database. Using collaborative filtering algorithm to optimize the construction of music audio database is mainly divided into the determination of database structure, the optimization of database collaborative filtering algorithm, and the result prediction of function.Based on this, this paper uses Big Data analysis technology and complex system discrete dynamic modeling technology to optimize the database construction method and puts forward a music audio database construction method based on Big Data technology and collaborative filtering algorithm.


### 3.2. Data Analysis Process of the Discrete Dynamic Modeling Method Based on the Collaborative Filtering Algorithm in the Database

The data analysis process of discrete dynamic modeling method based on collaborative filtering algorithm in the database. The optimization of each level in the music and audio database structure using collaborative filtering algorithm needs to redefine the weight and critical value of the retrieval algorithm. Its data analysis process in practical application is shown in Figure1.Figure 1
Data analysis process of the discrete dynamic modeling method based on collaborative filtering algorithm in the database.After completing the aforementioned steps, use the data sample set to train the database and predict the output of the music and audio database. The predicted simulation analysis results under different degrees of cooperation are shown in Figure2.Figure 2
Simulation analysis results of the collaborative filtering algorithm under different factors for the output of the music sound database.As can be seen from Figure2, with the increase of calculation times, the prediction results of four collaborative filtering algorithms with different degrees (0.25–1.0, of which 1.5 is the reference value of over synergy degree) on the simulation data are significantly different. It shows the law that the accurate coincidence rate increases with the increase of calculation times, and the prediction accuracy of collaborative filtering algorithm is the highest when the synergy degree is 1.0 and 1.5. However, when collaboration is too high, its computational complexity will increase. Taking the absolute value of the error between the predicted output and the expected output as the discretization function Qx, the complex analysis function Wx, and the influence weight function Rx, the calculation formula in the prediction stage is(1)Qx=x+ex3+1x2+e2,Wx=xx+33−8x+12x+eex−1,Rx=Wxx+33−Qxx+12ex−1.The function expressions of the discretization functionQx, the complex analysis function Wx, and the influence weight function Rx for the expected output process are(2)Q′x=x+1x3+ex2+ex,W′x=xe−1x+33−8x+12ex−1,R′x=W′xxe−1+33−Q′xxe−1+12ex−1,where x is the discretized data of the complex system, and e is the central index factor of the core layer of the database; after discretizing each data, the three functions of its dynamic representation are the initial dispersion function Ax, the state solidification function Sx, and the tail dispersion function Dx, and the corresponding expression is(3)Ax=x−e2+xexx+e,Sx=xe−1x+33x+e,Dx=Axkxe−1+33−Sxkxe−1+12,where x is the bottom data of the database, e is the central index factor of the core layer of the database, and k is the index coefficient. The real-time observation results are obtained by dynamically tracking the data to be processed and then the disturbance factor is added according to the observation results. At this time, the corresponding expressions of the initial dispersion function Ax, the state solidification function Sx, and the tail dispersion function Dx are(4)A′x=rx−12+xer+xx+er,S′x=xre−1rx+33x+rer,D′x=Axkxre−1+33−Sxkxre−1+12rkxre−1,where x is the bottom data of the database, e is the central index factor of the core layer of the database, k is the index coefficient, and r is the dynamic correction factor. In this process, as the number of Big Data analysis and optimization times of collaborative filtering algorithm increase (corresponding to different number of nodes), the simulation analysis results of the change law in the process of discrete dynamic modeling are shown in Figure 3.Figure 3
Simulation results of changes in the process of discrete dynamic modeling under different optimization times of collaborative filtering algorithm.As can be seen from Figure3, as the optimization times of collaborative filtering algorithm increase (corresponding to 3–8 different nodes in the figure, of which 3 is the lowest node number as the reference group), the database discrete dynamic modeling efficiency (evaluation value is 1–5) in the six groups of collaborative filtering algorithm methods also gradually increases and under the same evolution times. The threshold setting collaborative filtering algorithm has the most Big Data analysis centers, which is because it can effectively combine the advantages of Big Data analysis strategy and collaborative filtering algorithm in the process of discrete optimization of music and audio database and overcome the problem of slow speed of dynamic modeling to a certain extent. This algorithm is introduced into the characteristic data diagnosis of music audio database, which can optimize the efficiency of characteristic data analysis of music audio database.


### 3.3. Analysis Process of Discrete Dynamic Modeling Technology Based on the Collaborative Filtering Algorithm in the Music Audio Database

In this study, the discrete dynamic modeling technology based on collaborative filtering algorithm is used to quickly obtain the specified data in the music and audio database. The first step of file retrieval is to collect, describe it into some point sets, find the starting point, and then calculate it with collaborative filtering algorithms of different dimensions. The simulation analysis results of the change of the number of data analysis groups in the music and audio database are shown in Figure4.Figure 4
Number of data analysis groups of the music sound database under different dimensional optimization strategies.As can be seen from Figure4, with the increase of collaborative filtering algorithm in different data analysis times, the number of data analysis groups of music sound database under different dimensional optimization strategies (one-dimensional, two-dimensional, and three-dimensional) shows a trend of first increasing and then decreasing, and under the same conditions, the two-dimensional data group reaches the maximum first than the analysis group of three-dimensional data group. However, the maximum value of three-dimensional optimization strategy is the largest among the three groups of simulation. The key point of the dynamic modeling analysis method is to judge the distance from the point to the straight line. This method first directly compares the difference. If it is less than a fixed parameter, there will be a linear similarity study. If the difference value exceeds a certain standard value, it will be calculated according to the requirements. The data operation and information extraction process are shown in Figure 5.Figure 5
Data operation and information extraction process of the music sound database based on collaborative filtering algorithm.When the collaborative filtering algorithm processes different types of data groups, the corresponding dynamic modeling process will deviate to varying degrees with the construction speed of the algorithm. At this time, the data types need to be corrected in combination with Big Data analysis technology. The functions used in the Big Data analysis process are dynamic correction functionsZx. The corresponding expressions of threshold analysis function Kx and complex system stability quantization function Lx are(5)Zx=rxer+x/rx+erxerx,Kx=rxxre−1+m3/x+rerxrx+erx,Lx=Zx3+Kmx2mkx+rxre−13,where x is the bottom data of the database, e is the central index factor of the core layer of the database, k is the index coefficient, r is the dynamic correction factor, and m is the dynamic capture factor. In the process of data analysis at this stage, combined with the Big Data analysis strategy and the collaborative filtering algorithm based on discrete cosine transform, the simulation analysis results are shown in Figure 6.Figure 6
Database information extraction rate under different number of dynamic correction factors.As can be seen from Figure6, under the processing of the collaborative filtering algorithm, with the increase of the number of correction factors, the audio data sets of pop music, rock music, and classical music were analyzed, of which the first two groups show a law of gradual increase in the database information extraction rate, and the other group shows a change law of first increase, then decrease, then increase, and then decrease. When the neuron nodes are determined, the information extraction rate of the highest-order Big Data analysis data group is the highest. This is because in the actual calculation, the stability quantization function of complex system will determine the approximate range of the number of correction factors according to different data group types. Then, the optimal number of correction factors is determined. For the third group of data, the output result is less affected by the number of hidden layer correction factors, so the classification error of data analysis strategy will also change in different types after simulation.


### 3.4. Construction of the EFK Verification Model in the Complex System Based on Big Data

The establishment of music audio database is based on Big Data analysis technology, combined with discrete dynamic modeling technology, using traveling wave correction method as the learning algorithm of weight and threshold, but the learning process converges slowly. To solve this problem, discrete dynamic modeling strategy and greedy iterative analysis strategy need to be used. In this link, the simulation analysis results of the coincidence between the data transmission rate of different Big Data analysis strategies under the collaborative filtering algorithm and the accuracy of analysis information are shown in Figure7.Figure 7
Accuracy changes corresponding to different levels of data transmission rates in the EFK model.It can be seen from the simulation results in Figure7 that under the collaborative filtering algorithm, among the three different Big Data analysis strategy data groups, with the increase of data transmission rate, the accuracy of analysis information of single-layer analysis strategy data group has a great jump and quickly enters the attenuation stage. The two-tier analysis strategy data group and the three-tier analysis strategy data group have the change law of increasing the accuracy of analysis information because the greater the data transmission rate, the higher the accuracy of data analysis.


## 3.1. Application of Big Data Technology Based on Collaborative Filtering Algorithm in the Construction of the Music Audio Database

Collaborative filtering recommendation algorithm is one of the earliest and well-known recommendation algorithms. Its main function is to realize prediction and recommendation according to specific requirements after in-depth learning of data. For example, in the commercial application of accurate push of goods to users, the algorithm finds users’ preferences through mining users’ historical behavior data, Group users based on different preferences and recommend products with similar tastes [24]. Collaborative filtering recommendation algorithms are divided into two categories: user-based collaborative filtering algorithms and item-based collaborative filtering algorithms [25]. Collaborative filtering algorithm is a complex Big Data analysis and deep learning strategy, which has achieved far more results in speech and data information extraction than previous related technologies [26]. Modern database, whether storage system or retrieval system, is becoming more and more complex. Objectively, it puts forward higher requirements for the database from the initial design to the state monitoring, control, maintenance, and management methods in the process of use. There are a lot of complex and nonlinear engineering optimization problems in the process of database design, manufacturing and use, and these problems are difficult to be solved by common linear equations and determined mathematical models. Therefore, using new optimization methods, such as deep learning and data analysis strategy to solve these problems has become the research goal of building intelligent database. Using collaborative filtering algorithm to optimize the construction of music audio database is mainly divided into the determination of database structure, the optimization of database collaborative filtering algorithm, and the result prediction of function.Based on this, this paper uses Big Data analysis technology and complex system discrete dynamic modeling technology to optimize the database construction method and puts forward a music audio database construction method based on Big Data technology and collaborative filtering algorithm.


## 3.2. Data Analysis Process of the Discrete Dynamic Modeling Method Based on the Collaborative Filtering Algorithm in the Database

The data analysis process of discrete dynamic modeling method based on collaborative filtering algorithm in the database. The optimization of each level in the music and audio database structure using collaborative filtering algorithm needs to redefine the weight and critical value of the retrieval algorithm. Its data analysis process in practical application is shown in Figure1.Figure 1
Data analysis process of the discrete dynamic modeling method based on collaborative filtering algorithm in the database.After completing the aforementioned steps, use the data sample set to train the database and predict the output of the music and audio database. The predicted simulation analysis results under different degrees of cooperation are shown in Figure2.Figure 2
Simulation analysis results of the collaborative filtering algorithm under different factors for the output of the music sound database.As can be seen from Figure2, with the increase of calculation times, the prediction results of four collaborative filtering algorithms with different degrees (0.25–1.0, of which 1.5 is the reference value of over synergy degree) on the simulation data are significantly different. It shows the law that the accurate coincidence rate increases with the increase of calculation times, and the prediction accuracy of collaborative filtering algorithm is the highest when the synergy degree is 1.0 and 1.5. However, when collaboration is too high, its computational complexity will increase. Taking the absolute value of the error between the predicted output and the expected output as the discretization function Qx, the complex analysis function Wx, and the influence weight function Rx, the calculation formula in the prediction stage is(1)Qx=x+ex3+1x2+e2,Wx=xx+33−8x+12x+eex−1,Rx=Wxx+33−Qxx+12ex−1.The function expressions of the discretization functionQx, the complex analysis function Wx, and the influence weight function Rx for the expected output process are(2)Q′x=x+1x3+ex2+ex,W′x=xe−1x+33−8x+12ex−1,R′x=W′xxe−1+33−Q′xxe−1+12ex−1,where x is the discretized data of the complex system, and e is the central index factor of the core layer of the database; after discretizing each data, the three functions of its dynamic representation are the initial dispersion function Ax, the state solidification function Sx, and the tail dispersion function Dx, and the corresponding expression is(3)Ax=x−e2+xexx+e,Sx=xe−1x+33x+e,Dx=Axkxe−1+33−Sxkxe−1+12,where x is the bottom data of the database, e is the central index factor of the core layer of the database, and k is the index coefficient. The real-time observation results are obtained by dynamically tracking the data to be processed and then the disturbance factor is added according to the observation results. At this time, the corresponding expressions of the initial dispersion function Ax, the state solidification function Sx, and the tail dispersion function Dx are(4)A′x=rx−12+xer+xx+er,S′x=xre−1rx+33x+rer,D′x=Axkxre−1+33−Sxkxre−1+12rkxre−1,where x is the bottom data of the database, e is the central index factor of the core layer of the database, k is the index coefficient, and r is the dynamic correction factor. In this process, as the number of Big Data analysis and optimization times of collaborative filtering algorithm increase (corresponding to different number of nodes), the simulation analysis results of the change law in the process of discrete dynamic modeling are shown in Figure 3.Figure 3
Simulation results of changes in the process of discrete dynamic modeling under different optimization times of collaborative filtering algorithm.As can be seen from Figure3, as the optimization times of collaborative filtering algorithm increase (corresponding to 3–8 different nodes in the figure, of which 3 is the lowest node number as the reference group), the database discrete dynamic modeling efficiency (evaluation value is 1–5) in the six groups of collaborative filtering algorithm methods also gradually increases and under the same evolution times. The threshold setting collaborative filtering algorithm has the most Big Data analysis centers, which is because it can effectively combine the advantages of Big Data analysis strategy and collaborative filtering algorithm in the process of discrete optimization of music and audio database and overcome the problem of slow speed of dynamic modeling to a certain extent. This algorithm is introduced into the characteristic data diagnosis of music audio database, which can optimize the efficiency of characteristic data analysis of music audio database.


## 3.3. Analysis Process of Discrete Dynamic Modeling Technology Based on the Collaborative Filtering Algorithm in the Music Audio Database

In this study, the discrete dynamic modeling technology based on collaborative filtering algorithm is used to quickly obtain the specified data in the music and audio database. The first step of file retrieval is to collect, describe it into some point sets, find the starting point, and then calculate it with collaborative filtering algorithms of different dimensions. The simulation analysis results of the change of the number of data analysis groups in the music and audio database are shown in Figure4.Figure 4
Number of data analysis groups of the music sound database under different dimensional optimization strategies.As can be seen from Figure4, with the increase of collaborative filtering algorithm in different data analysis times, the number of data analysis groups of music sound database under different dimensional optimization strategies (one-dimensional, two-dimensional, and three-dimensional) shows a trend of first increasing and then decreasing, and under the same conditions, the two-dimensional data group reaches the maximum first than the analysis group of three-dimensional data group. However, the maximum value of three-dimensional optimization strategy is the largest among the three groups of simulation. The key point of the dynamic modeling analysis method is to judge the distance from the point to the straight line. This method first directly compares the difference. If it is less than a fixed parameter, there will be a linear similarity study. If the difference value exceeds a certain standard value, it will be calculated according to the requirements. The data operation and information extraction process are shown in Figure 5.Figure 5
Data operation and information extraction process of the music sound database based on collaborative filtering algorithm.When the collaborative filtering algorithm processes different types of data groups, the corresponding dynamic modeling process will deviate to varying degrees with the construction speed of the algorithm. At this time, the data types need to be corrected in combination with Big Data analysis technology. The functions used in the Big Data analysis process are dynamic correction functionsZx. The corresponding expressions of threshold analysis function Kx and complex system stability quantization function Lx are(5)Zx=rxer+x/rx+erxerx,Kx=rxxre−1+m3/x+rerxrx+erx,Lx=Zx3+Kmx2mkx+rxre−13,where x is the bottom data of the database, e is the central index factor of the core layer of the database, k is the index coefficient, r is the dynamic correction factor, and m is the dynamic capture factor. In the process of data analysis at this stage, combined with the Big Data analysis strategy and the collaborative filtering algorithm based on discrete cosine transform, the simulation analysis results are shown in Figure 6.Figure 6
Database information extraction rate under different number of dynamic correction factors.As can be seen from Figure6, under the processing of the collaborative filtering algorithm, with the increase of the number of correction factors, the audio data sets of pop music, rock music, and classical music were analyzed, of which the first two groups show a law of gradual increase in the database information extraction rate, and the other group shows a change law of first increase, then decrease, then increase, and then decrease. When the neuron nodes are determined, the information extraction rate of the highest-order Big Data analysis data group is the highest. This is because in the actual calculation, the stability quantization function of complex system will determine the approximate range of the number of correction factors according to different data group types. Then, the optimal number of correction factors is determined. For the third group of data, the output result is less affected by the number of hidden layer correction factors, so the classification error of data analysis strategy will also change in different types after simulation.


## 3.4. Construction of the EFK Verification Model in the Complex System Based on Big Data

The establishment of music audio database is based on Big Data analysis technology, combined with discrete dynamic modeling technology, using traveling wave correction method as the learning algorithm of weight and threshold, but the learning process converges slowly. To solve this problem, discrete dynamic modeling strategy and greedy iterative analysis strategy need to be used. In this link, the simulation analysis results of the coincidence between the data transmission rate of different Big Data analysis strategies under the collaborative filtering algorithm and the accuracy of analysis information are shown in Figure7.Figure 7
Accuracy changes corresponding to different levels of data transmission rates in the EFK model.It can be seen from the simulation results in Figure7 that under the collaborative filtering algorithm, among the three different Big Data analysis strategy data groups, with the increase of data transmission rate, the accuracy of analysis information of single-layer analysis strategy data group has a great jump and quickly enters the attenuation stage. The two-tier analysis strategy data group and the three-tier analysis strategy data group have the change law of increasing the accuracy of analysis information because the greater the data transmission rate, the higher the accuracy of data analysis.


## 4. Result Analysis and Discussion

### 4.1. Simulation Experiment and Data Analysis

Before the experiment, it is necessary to set various parameters in the algorithm in advance. For convenience, this study sets the initial number of data cluster centers of the manual collaborative filtering algorithm as 10 and the dispersion index of the manual collaborative filtering algorithm as 0.1. The maximum algebraic value is 50. The model is verified by EFK data analysis to practice a large number of sample data. In order to verify the practical application reliability of the music sound database based on the collaborative filtering algorithm, this study conducted an experimental study on a large number of music sound data. According to the size of the data, five groups are set in ascending order. The preliminary analysis results of the five groups of different music sound databases are shown in Figure8.Figure 8
Reliability of the corresponding retrieved data results under different experimental analysis times under the collaborative filtering algorithm.As can be seen from Figure8, the reliability of the collaborative filtering algorithm for the results of five groups of experimental data is different. Among them, the reliability of the data results of the first group of databases is the highest, and the reliability of the data results of the fifth group is the lowest. Moreover, the differences of the results are also obvious in databases with different data volumes. This is because the music audio database collection samples referred to in the experiment process include data analysis strategy training samples and simulation experiment test samples. The music audio training samples used in the Big Data analysis process are known, and the collaborative filtering algorithm discretizes the random data in the dynamic modeling process. In the music and sound database model constructed in the experimental link, the data of the sample set is simulated by the collaborative analysis software, and the music and sound data to be retrieved are analyzed in depth at different branch points. The response of the collaborative filtering algorithm in the corresponding feature data location in the database is judged by artificially manufacturing the feature data nodes in the music and audio data. In many sample databases, it is divided into no less than 100 complete data analysis strategy models, and the corresponding databases adopt the strategy based on multilayer data analysis mode.


### 4.2. Result Analysis

The experimental results show that on the premise of meeting the hierarchical optimization model of music and audio database, the existing data structure is optimized using collaborative filtering algorithm and Big Data analysis technology. The data of users accessing the corresponding music and audio files are obtained using interface logs, which can complete the adjustment and improvement of the database structure. In the experiment, the average experimental running time of five groups using different database construction strategies is analyzed one by one, and the analysis results are shown in Figure9.Figure 9
Analysis of experimental results of the deep learning neural network.In Figure9, Group 1 adopts the traditional stacking strategy, Groups 2 and 3 adopt two mainstream iterative strategies, and Groups 4 and 5 adopt collaborative filtering and Big Data analysis strategies. According to the analysis of the experimental results in Figure 9, compared with the traditional stacked database analysis strategy and the mainstream iterative database analysis strategy, the database analysis strategy using collaborative filtering algorithm and Big Data analysis technology also maintains a high level of accuracy in the final data analysis in the process of solving the target problem, and when the analysis times are 15 times. It is significantly higher than the other two methods in analysis accuracy and position judgment accuracy. This is because in the process of using collaborative filtering algorithm combined with a large number of music and audio data to build the database, it performs well in processing and judging the characteristic data in the branch points. The location of feature data is also accurately judged. More feature data branches can be judged to achieve accurate positioning with specific feature data. With the change of experimental time, the complex database system using collaborative filtering algorithm is constantly replaced and iterated, and the judgment speed of characteristic data is faster and faster.


## 4.1. Simulation Experiment and Data Analysis

Before the experiment, it is necessary to set various parameters in the algorithm in advance. For convenience, this study sets the initial number of data cluster centers of the manual collaborative filtering algorithm as 10 and the dispersion index of the manual collaborative filtering algorithm as 0.1. The maximum algebraic value is 50. The model is verified by EFK data analysis to practice a large number of sample data. In order to verify the practical application reliability of the music sound database based on the collaborative filtering algorithm, this study conducted an experimental study on a large number of music sound data. According to the size of the data, five groups are set in ascending order. The preliminary analysis results of the five groups of different music sound databases are shown in Figure8.Figure 8
Reliability of the corresponding retrieved data results under different experimental analysis times under the collaborative filtering algorithm.As can be seen from Figure8, the reliability of the collaborative filtering algorithm for the results of five groups of experimental data is different. Among them, the reliability of the data results of the first group of databases is the highest, and the reliability of the data results of the fifth group is the lowest. Moreover, the differences of the results are also obvious in databases with different data volumes. This is because the music audio database collection samples referred to in the experiment process include data analysis strategy training samples and simulation experiment test samples. The music audio training samples used in the Big Data analysis process are known, and the collaborative filtering algorithm discretizes the random data in the dynamic modeling process. In the music and sound database model constructed in the experimental link, the data of the sample set is simulated by the collaborative analysis software, and the music and sound data to be retrieved are analyzed in depth at different branch points. The response of the collaborative filtering algorithm in the corresponding feature data location in the database is judged by artificially manufacturing the feature data nodes in the music and audio data. In many sample databases, it is divided into no less than 100 complete data analysis strategy models, and the corresponding databases adopt the strategy based on multilayer data analysis mode.


## 4.2. Result Analysis

The experimental results show that on the premise of meeting the hierarchical optimization model of music and audio database, the existing data structure is optimized using collaborative filtering algorithm and Big Data analysis technology. The data of users accessing the corresponding music and audio files are obtained using interface logs, which can complete the adjustment and improvement of the database structure. In the experiment, the average experimental running time of five groups using different database construction strategies is analyzed one by one, and the analysis results are shown in Figure9.Figure 9
Analysis of experimental results of the deep learning neural network.In Figure9, Group 1 adopts the traditional stacking strategy, Groups 2 and 3 adopt two mainstream iterative strategies, and Groups 4 and 5 adopt collaborative filtering and Big Data analysis strategies. According to the analysis of the experimental results in Figure 9, compared with the traditional stacked database analysis strategy and the mainstream iterative database analysis strategy, the database analysis strategy using collaborative filtering algorithm and Big Data analysis technology also maintains a high level of accuracy in the final data analysis in the process of solving the target problem, and when the analysis times are 15 times. It is significantly higher than the other two methods in analysis accuracy and position judgment accuracy. This is because in the process of using collaborative filtering algorithm combined with a large number of music and audio data to build the database, it performs well in processing and judging the characteristic data in the branch points. The location of feature data is also accurately judged. More feature data branches can be judged to achieve accurate positioning with specific feature data. With the change of experimental time, the complex database system using collaborative filtering algorithm is constantly replaced and iterated, and the judgment speed of characteristic data is faster and faster.


## 5. Conclusion

The research on the construction method of music sound database, the comprehensive analysis of music sound data and the intelligence of music sound database retrieval results are called the three mainstream research directions of contemporary music sound database theory. To better realize the efficient construction of music audio database, we need to pay attention to the analysis and research from the perspective of database structure level. Based on this, this paper establishes a multilevel music and sound database system based on Big Data and collaborative filtering algorithm. First, this paper introduces the development status and research progress of various types of music and sound databases and puts forward a music and sound database establishment method based on Big Data and collaborative filtering algorithm. Second, it introduces the Big Data technology, collaborative filtering algorithm, and the establishment of EFK model, which can effectively reduce the error rate in the process of database retrieval with the help of Big Data analysis. The experimental results show that the database based on Big Data and collaborative filtering algorithm can greatly improve the retrieval speed. However, this paper only pays attention to the construction of logical layer in each structure of music sound database and does not take the differences of different music sound systems into account. Therefore, the music sound database established by this method needs to be further studied.



---

*Source: 1756357-2022-02-12.xml*