# Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining

Authors: Ying Zhu
Journal: Security and Communication Networks
 (2022)

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## Abstract

Aiming at the problems of low error recognition accuracy and low correlation of data matching in the existing dance action and music beat matching error recognition methods, a dance action and music beat matching error recognition method based on data mining is designed. The quaternion array is used to represent the joint coordinate points of the dance movement, and the coordinates are rotated to obtain the curve change of the dance movement. The dance movement characteristics are regarded as a set of positive and negative sample data, the initial weights of different sample data are calculated, the absolute value is processed for the music beat signal, the interference factors are filtered with the help of Gauss filter, and according to the rhythm law of the music beat signal, the characteristics of music beat signal are extracted and the feature extraction of dance action matching is completed with music beat. The corresponding relationship between dance action and music beat is regarded as the corresponding model, the pairwise occurrence probability between music beat and action is determined, the matching model between dance action and music beat is discretized, and the peak point of correlation data is introduced to complete the matching between dance action and music beat. The features of matching data are extracted and segmented by short-time Fourier transform, the segmented matching data is transformed into matching data, the matching error identification model is established with the help of support vector mechanism, and the constraint conditions of error detection are set to complete the matching error identification. The experimental results show that the proposed method has high recognition accuracy and high data matching correlation.


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## Body

## 1. Introduction

In dance training and performance, the accurate matching of music beat is the key factor affecting the success of dance performance. Therefore, in dance training, the effective matching of music beat is very necessary [1]. In dance instruction and performance, the proper mixing of music rhythm is critical. With the changing of music rhythm, dancing motions reveal dance's emotion and expressive subject. Song may also be transmitted via dancing movements, which conveys the mood of the music. Dance moves and music achieve each other [2]. With the continuous progress of society, in the arrangement of dance movements and music, the effective combination and synchronization of movements and music beats are regarded as the key to good expression of movements. With the continuous development of modern electronic technology, many monitoring systems for matching dance movements with music beat have been studied [3]. Such artificial intelligence systems are helpful to dance training. However, due to the diversity of music beat and the continuous change of signal, the effect of music beat matching is poor [4]. Therefore, how to improve the effect of dance action and music beat matching and identify the error in its matching has become a hot issue in this field [5]. Therefore, researchers in this field have designed a correct method for identifying the matching error between dance action and music beat and achieved some results.A dance motion error detection approach based on 3D CNN was presented in [6]. This method extracts the actual video frames in the two-dimensional human dance action, encodes the extracted action video frames, and hierarchically processes the extracted action data with the help of CNN algorithm, addressing the shortcomings of existing dance moving mouth recognition and classification, primarily the poor recognition effect caused by Beijing, blurred rear edge, and other reasons. To enhance the dancing movement, find the hidden nonstandard movement data. This method has small error in identifying dance movement, and the identification method is relatively simple, but there are many interference data in the preprocessing of extracted data, which affects the identification of movement error and does not consider the matching of music beat, so it has some limitations. Literature [7] proposed a dance video action recognition technology based on computer vision, which can identify the matching error between dance action and music beat. This method first extracts the dance action video image and preprocesses the image, then determines the feature points of the dance video image according to the determined image, fuses the same feature data with the help of SVM, and then trains the different data for component recognition to realize the research of dance action recognition. The training data results in this method are more effective, but the compactness of music beat is not considered too much, resulting in large recognition error. The study on dance posture error identification technique based on motion capture technology was proposed in [8]. Dance motions are successfully recorded using this approach, which includes the collecting of dance movement data, the creation of a posture acquisition model database, and the extraction of important points from plane vector characteristics. Then the extracted data are grouped, and the recognition of dance movements is effectively analyzed to effectively identify dance movement errors. This method extracts data for different attitudes, but there are large errors in the extraction.Aiming at the problems in the above methods, this paper designs a matching error recognition method between dance action and music beat based on data mining. The main technical route of this paper is as follows.Step 1.
Use a quaternion array to represent the joint coordinate points of the dance movement, rotate the coordinates, obtain the curve change of the dance movement, regard the dance movement characteristics as a set of positive and negative sample data, calculate the initial weight of different sample data, process the music beat signal in absolute value, filter out the interference factors with the help of Gauss filter, and according to the rhythm law of the music beat signal, extract the characteristics of music beat signal and complete the feature extraction of dance action matching with music beat.Step 2.
Regard the corresponding relationship between dance action and music beat as the corresponding model, determine the pairwise occurrence probability between music beat and action, discretize the matching model between dance action and music beat, and introduce the peak point of correlation data to complete the matching between dance action and music beat.Step 3.
Extract the features of matching data through short-time Fourier transform, segment it, transform the segmented matching data into matching data, build the matching error identification model with the help of support vector mechanism, and set the constraints of error detection to complete the matching error identification.Step 4.
Experimental analysis is carried out.Step 5.
Conclusion is given.


## 2. Design of the Matching Error Identification Method between Dance Action and Music Beat

### 2.1. Feature Extraction of Dance Action and Music Beat Matching

In order to effectively identify the matching error between dance action and music beat, it is necessary to extract the matching feature between dance action and music beat before recognition. In this feature extraction, we need to uniformly extract the dance action features and music signal features and take the extracted unified features as the key data of this paper.


#### 2.1.1. Dance Movement Feature Extraction

In the dance movement recognition, the joint of the dance trainer is expressed in a 3D position of a certain performance position, and the 3D position coordinates of the dance movement performer are expressed asa,b,c and rotate around the position coordinates of the human joints in the dance movement, and the angle after the rotation is set to γ,β,α. The quaternionic array of its action is a,b,c,e. In the change of human joints in dance, it is assumed that coordinates represent the running position. The actions of different dance performers need to be normalized to ensure that the extracted action features are consistent [9]. Rotate the set dance action quaternion array to a certain point. At this time, the coordinate points of the rotated dance action are expressed as(1)a2+b2+c2+e2=1,where e represents the cosine value of the rotation angle.According to the determined angle value after the rotation of the dance action, calculate the curve change of the dance action [10] and obtain(2)Wi=Wj−1×Wk.Among them,W represents the action component and k represents the time to complete the dance movement.Based on the above analysis, the dance movement characteristics are regarded as a set containing positive and negative sample data [11], which is set as(3)A1,B1,A2,B2,...AN,BN.Among them,N represents the amount of sample dance movement characteristics data, A1,B1 represent the initial sample dance movement characteristics, and AN,BN represent the output dance movement characteristics data.Calculate the initial weight value of each action in the above dance action feature set, where the positive sample data feature is obtained:(4)Ri=1m.Of these,m represents the number of positive samples.The initial weight value [12] of negative sample action characteristic data is(5)ui=w1p,where w represents the initial weight outcome value for the negative sample data, and p represents the number of negative samples.Then, the determined different action feature numbers are normalized to complete the extraction of dance action feature data [13], and the following results are obtained:(6)Ei=ρui∑i=1nRi+μ∑i=1mui.Among them,μ represents the action normalization processing factor, ρ represents the normalization coefficient, and Ei represents the final dance action data characteristics.In the dance movement feature extraction, the quaternion array is used to represent the dance movement joint coordinate points, and the coordinates are rotated to obtain the curve change of the dance movement. Then, the dance movement feature is regarded as a set of positive and negative sample data; the initial weight of different sample data feature data is calculated and normalized to complete the dance movement feature extraction.


#### 2.1.2. Feature Extraction of Music Beat Signal in Dance Movement

According to the above extracted dance action features, it is also very important to extract the characteristics of music beat in the dance action performance. Because the music beat performance is more abstract [14], it is difficult to extract it directly. Therefore, in this paper, the music beat in dance action is regarded as music signal, and the music beat signal features are extracted in the form of signal features.The change in the music beat signal's waveform energy mirrors the change in the music beat, but there is additional interference information. It generally consists of spike pulse and ripple voltage interference. The electrical equipment used to play music is mostly responsible for these interference problems. To eliminate interference elements in the signal characteristics, it is important to filter the music beat signal before feature extraction [15]. The music beat signal undergoes several alterations. To begin, absolute value processing is used to verify that the properties of the music signal recovered later in the process are consistent, and the following findings are achieved:(7)Vi=∑i−441i+441vm.Among these,vm represents the frequency of the music beat signal change, i+441 represents the amplitude change of the signal in the sampling time, and i−441 represents the absolute value of the amplitude value of the music signal at the sampling point. The change law of music signal after absolute value is shown in Figure 1.Figure 1
Variation law of music signal after absolute value.According to the determined unified music signal, the filter is used to filter the interference factors in the music signal [16]. In this paper, with the help of Gauss filter, its time domain expression formula is(8)sit=1Gt≤G20t>G2.Among them,G represents the filter, si represents the noise value in the music signal, and t represents the filter time domain width value.According to the time domain calculation of the above filter, set the frequency response of the filter and complete the filtering of interference factors in the music signal to obtain(9)hti=YxnGi2i,where Y represents the threshold of the filter change and xn represents the limit value of the magnitude of the signal change.The music beat signal data processed by the filter not only eliminates the signal interference, but also reduces the number of changes in the data and reduces the workload of feature extraction of music beat signal data.The change of music beat signal presents strength change filtering. This change is not a random and unorganized change but is set according to a certain strength law and changes the signal change according to a specific law [17]. Therefore, after the above filtering processing, the feature vector of the music beat signal is extracted with the help of the variation law of the music beat strength, and the signal features obtained are(10)fa=1n∑i=1mhti∑℉στ.Among them,τ indicates the law of the music beat signal change and ℉ represents the length of the music signal and the σ strength of the music signal.In the feature extraction of music beat signal in dance action, firstly, the music beat signal is processed by absolute value to ensure the consistency of the later extracted music signal features. The interference factors are filtered by Gauss filter, and the music beat signal features are extracted according to the rhythm law of music beat signal.


### 2.2. Dance Action and Music Beat Data Match

The extracted two data pieces are matched according to the above collected dance action and music beat characteristic data. Since there is a corresponding relationship between dance action and music beat before training [18], the corresponding relationship between them is regarded as a corresponding model before matching, which is set as(11)Q=q1,q2,...qn.The attribute values of characteristic data are included in the dance action and music beat matching model. It mainly includes the identifier of action classification, the time length of standard action, and the rhythm matching of action.Therefore, the above corresponding action data and music signal data are divided with the help of a strong classifier to determine the probability value of paired occurrence between music beat and action and obtain(12)Kx,y=o∑i=1mδi×U℉i,σi,τi.Among them,δi is the classifier. This paper has 20 classifiers. U represents the feature value after unifying the dance action and the music signal, and o represents the weight value of the classifier.According to the determined probability, each classifier is scored in the standard matching to determine the dispersion degree of dance action and music beat matching, and the following results are obtained:(13)Li=1DISli>K0DISli<K,where K represents the threshold after matching between action and musical beat, and DISli represents the distance between ideal action eigenvalues.In order to further realize the matching between dance action and music beat [19], calculate the correlation between dance action and music beat and obtain(14)ℵε=∑i=1NDx×Mθ−ϕ,where θ represents the musical beat signal value, M represents the correlation coefficient, and ϕ represents the length value of the data characteristics.Therefore, the peak point of correlation data is introduced to complete the matching between dance action and music beat, and the following results are obtained:(15)gx,y=ℵεωHn21/m∑i=1NDx,where Hn2 represents the peak mass and Dx represents the relative local energy and the ω action feature amplitude influence.The matching process between dance action and music beat is shown in Figure2.Figure 2
Matching process between dance action and music beat.The corresponding relationship between dance action and music beat is regarded as the corresponding model during the process of matching dance action and music beat data, the pairwise occurrence probability between music beat and action is determined, the dance action and music beat matching model is discretized, and the peak point of relevant data is introduced to complete the matching between dance action and music beat.


### 2.3. Realization of Matching Error Recognition Based on Data Mining

According to the above dance action and music beat data matching, the data mining algorithm is used to identify the matching error. Data mining algorithm is an artificial intelligence algorithm, which includes many artificial intelligence algorithms and has the advantages of fast data processing speed and sensitive reflection [20]. Therefore, this paper realizes the error detection of dance action and music beat data matching with the help of the data mining algorithm in this algorithm. Through the data mining algorithm, the data characteristics after the two matching are analyzed, and the data with weak correlation is regarded as error data, which can more simply complete the matching error identification.The matched data are extracted by short-time Fourier transform [21]. The calculation [22] process of short-time Fourier transform is regarded as a long-time signal segmentation and signal segmentation in the same time period, and its calculation formula is(16)Fx,y=∑i=1b/2−1ϕ1xl+nχ×∅w/n,where Fx,y represents the input signal of the data after the N th match, ϕ1 represents the Hamming window function, and nχ represents the index of the matching point data ∅ representing the number of jumps in the adjacent frame action.After segmenting the matched data features, since the matched data is a kind of video image, it is necessary to convert the spectrum diagram into simple and easy to analyze matching feature data [23], so as to obtain(17)Fx,y′=℃ϕlxl+nχ×∅w/n2,Fx,y′ represents the simplified spectral image feature, and ℃ represents the spectral map power.The support vector machine algorithm of data mining algorithm is used to identify the matching error of the segmented matching data. Assuming that the set matching data is in a certain feature space [24], the linearly separable training linear data set is(18)ς=a1,a1,a2,a2,...aN,aN,where ς represents the linear separable property data.The functional distance [25] from the point in the matching data set in the training set to the hyperplane is expressed as(19)distance=yiϖ∗xi+b.Among these, distance represents the distance between the hyperplanes, andb represents the bias coefficient.According to the distance between the determined matching data, the recognition model based on support vector machine is set to obtain(20)Ξi=yiωϖϖ+bω.According to the set model, the constraint conditions for error identification are limited, the matched data are limited according to the set identification constraint conditions, and the data that do not meet the limit conditions are regarded as matched error data. The set constraint conditions are(21)s.tΞiLωϖϖ+bω>υ,where L represents the Lagrangian function and υ represents the proportion of the proportional-scaled hyperplane.Input the determined matching data set into the set error detection model, output the final error identification result, and obtain(22)ξa,b=Γ12w2+1v∑bωΘ,where v represents the relaxation variable, Γ represents the final output of the error detection result data, and Θ represents the minimum taxonomic plane distance value of the data in the error detection.To complete the matching error recognition based on data mining, the matching data features are extracted and segmented using a short-time Fourier transform, the segmented matching data is transformed into matching data, the matching error recognition model is established using a support vector mechanism, and the error detection constraint conditions are set.


## 2.1. Feature Extraction of Dance Action and Music Beat Matching

In order to effectively identify the matching error between dance action and music beat, it is necessary to extract the matching feature between dance action and music beat before recognition. In this feature extraction, we need to uniformly extract the dance action features and music signal features and take the extracted unified features as the key data of this paper.


### 2.1.1. Dance Movement Feature Extraction

In the dance movement recognition, the joint of the dance trainer is expressed in a 3D position of a certain performance position, and the 3D position coordinates of the dance movement performer are expressed asa,b,c and rotate around the position coordinates of the human joints in the dance movement, and the angle after the rotation is set to γ,β,α. The quaternionic array of its action is a,b,c,e. In the change of human joints in dance, it is assumed that coordinates represent the running position. The actions of different dance performers need to be normalized to ensure that the extracted action features are consistent [9]. Rotate the set dance action quaternion array to a certain point. At this time, the coordinate points of the rotated dance action are expressed as(1)a2+b2+c2+e2=1,where e represents the cosine value of the rotation angle.According to the determined angle value after the rotation of the dance action, calculate the curve change of the dance action [10] and obtain(2)Wi=Wj−1×Wk.Among them,W represents the action component and k represents the time to complete the dance movement.Based on the above analysis, the dance movement characteristics are regarded as a set containing positive and negative sample data [11], which is set as(3)A1,B1,A2,B2,...AN,BN.Among them,N represents the amount of sample dance movement characteristics data, A1,B1 represent the initial sample dance movement characteristics, and AN,BN represent the output dance movement characteristics data.Calculate the initial weight value of each action in the above dance action feature set, where the positive sample data feature is obtained:(4)Ri=1m.Of these,m represents the number of positive samples.The initial weight value [12] of negative sample action characteristic data is(5)ui=w1p,where w represents the initial weight outcome value for the negative sample data, and p represents the number of negative samples.Then, the determined different action feature numbers are normalized to complete the extraction of dance action feature data [13], and the following results are obtained:(6)Ei=ρui∑i=1nRi+μ∑i=1mui.Among them,μ represents the action normalization processing factor, ρ represents the normalization coefficient, and Ei represents the final dance action data characteristics.In the dance movement feature extraction, the quaternion array is used to represent the dance movement joint coordinate points, and the coordinates are rotated to obtain the curve change of the dance movement. Then, the dance movement feature is regarded as a set of positive and negative sample data; the initial weight of different sample data feature data is calculated and normalized to complete the dance movement feature extraction.


### 2.1.2. Feature Extraction of Music Beat Signal in Dance Movement

According to the above extracted dance action features, it is also very important to extract the characteristics of music beat in the dance action performance. Because the music beat performance is more abstract [14], it is difficult to extract it directly. Therefore, in this paper, the music beat in dance action is regarded as music signal, and the music beat signal features are extracted in the form of signal features.The change in the music beat signal's waveform energy mirrors the change in the music beat, but there is additional interference information. It generally consists of spike pulse and ripple voltage interference. The electrical equipment used to play music is mostly responsible for these interference problems. To eliminate interference elements in the signal characteristics, it is important to filter the music beat signal before feature extraction [15]. The music beat signal undergoes several alterations. To begin, absolute value processing is used to verify that the properties of the music signal recovered later in the process are consistent, and the following findings are achieved:(7)Vi=∑i−441i+441vm.Among these,vm represents the frequency of the music beat signal change, i+441 represents the amplitude change of the signal in the sampling time, and i−441 represents the absolute value of the amplitude value of the music signal at the sampling point. The change law of music signal after absolute value is shown in Figure 1.Figure 1
Variation law of music signal after absolute value.According to the determined unified music signal, the filter is used to filter the interference factors in the music signal [16]. In this paper, with the help of Gauss filter, its time domain expression formula is(8)sit=1Gt≤G20t>G2.Among them,G represents the filter, si represents the noise value in the music signal, and t represents the filter time domain width value.According to the time domain calculation of the above filter, set the frequency response of the filter and complete the filtering of interference factors in the music signal to obtain(9)hti=YxnGi2i,where Y represents the threshold of the filter change and xn represents the limit value of the magnitude of the signal change.The music beat signal data processed by the filter not only eliminates the signal interference, but also reduces the number of changes in the data and reduces the workload of feature extraction of music beat signal data.The change of music beat signal presents strength change filtering. This change is not a random and unorganized change but is set according to a certain strength law and changes the signal change according to a specific law [17]. Therefore, after the above filtering processing, the feature vector of the music beat signal is extracted with the help of the variation law of the music beat strength, and the signal features obtained are(10)fa=1n∑i=1mhti∑℉στ.Among them,τ indicates the law of the music beat signal change and ℉ represents the length of the music signal and the σ strength of the music signal.In the feature extraction of music beat signal in dance action, firstly, the music beat signal is processed by absolute value to ensure the consistency of the later extracted music signal features. The interference factors are filtered by Gauss filter, and the music beat signal features are extracted according to the rhythm law of music beat signal.


## 2.1.1. Dance Movement Feature Extraction

In the dance movement recognition, the joint of the dance trainer is expressed in a 3D position of a certain performance position, and the 3D position coordinates of the dance movement performer are expressed asa,b,c and rotate around the position coordinates of the human joints in the dance movement, and the angle after the rotation is set to γ,β,α. The quaternionic array of its action is a,b,c,e. In the change of human joints in dance, it is assumed that coordinates represent the running position. The actions of different dance performers need to be normalized to ensure that the extracted action features are consistent [9]. Rotate the set dance action quaternion array to a certain point. At this time, the coordinate points of the rotated dance action are expressed as(1)a2+b2+c2+e2=1,where e represents the cosine value of the rotation angle.According to the determined angle value after the rotation of the dance action, calculate the curve change of the dance action [10] and obtain(2)Wi=Wj−1×Wk.Among them,W represents the action component and k represents the time to complete the dance movement.Based on the above analysis, the dance movement characteristics are regarded as a set containing positive and negative sample data [11], which is set as(3)A1,B1,A2,B2,...AN,BN.Among them,N represents the amount of sample dance movement characteristics data, A1,B1 represent the initial sample dance movement characteristics, and AN,BN represent the output dance movement characteristics data.Calculate the initial weight value of each action in the above dance action feature set, where the positive sample data feature is obtained:(4)Ri=1m.Of these,m represents the number of positive samples.The initial weight value [12] of negative sample action characteristic data is(5)ui=w1p,where w represents the initial weight outcome value for the negative sample data, and p represents the number of negative samples.Then, the determined different action feature numbers are normalized to complete the extraction of dance action feature data [13], and the following results are obtained:(6)Ei=ρui∑i=1nRi+μ∑i=1mui.Among them,μ represents the action normalization processing factor, ρ represents the normalization coefficient, and Ei represents the final dance action data characteristics.In the dance movement feature extraction, the quaternion array is used to represent the dance movement joint coordinate points, and the coordinates are rotated to obtain the curve change of the dance movement. Then, the dance movement feature is regarded as a set of positive and negative sample data; the initial weight of different sample data feature data is calculated and normalized to complete the dance movement feature extraction.


## 2.1.2. Feature Extraction of Music Beat Signal in Dance Movement

According to the above extracted dance action features, it is also very important to extract the characteristics of music beat in the dance action performance. Because the music beat performance is more abstract [14], it is difficult to extract it directly. Therefore, in this paper, the music beat in dance action is regarded as music signal, and the music beat signal features are extracted in the form of signal features.The change in the music beat signal's waveform energy mirrors the change in the music beat, but there is additional interference information. It generally consists of spike pulse and ripple voltage interference. The electrical equipment used to play music is mostly responsible for these interference problems. To eliminate interference elements in the signal characteristics, it is important to filter the music beat signal before feature extraction [15]. The music beat signal undergoes several alterations. To begin, absolute value processing is used to verify that the properties of the music signal recovered later in the process are consistent, and the following findings are achieved:(7)Vi=∑i−441i+441vm.Among these,vm represents the frequency of the music beat signal change, i+441 represents the amplitude change of the signal in the sampling time, and i−441 represents the absolute value of the amplitude value of the music signal at the sampling point. The change law of music signal after absolute value is shown in Figure 1.Figure 1
Variation law of music signal after absolute value.According to the determined unified music signal, the filter is used to filter the interference factors in the music signal [16]. In this paper, with the help of Gauss filter, its time domain expression formula is(8)sit=1Gt≤G20t>G2.Among them,G represents the filter, si represents the noise value in the music signal, and t represents the filter time domain width value.According to the time domain calculation of the above filter, set the frequency response of the filter and complete the filtering of interference factors in the music signal to obtain(9)hti=YxnGi2i,where Y represents the threshold of the filter change and xn represents the limit value of the magnitude of the signal change.The music beat signal data processed by the filter not only eliminates the signal interference, but also reduces the number of changes in the data and reduces the workload of feature extraction of music beat signal data.The change of music beat signal presents strength change filtering. This change is not a random and unorganized change but is set according to a certain strength law and changes the signal change according to a specific law [17]. Therefore, after the above filtering processing, the feature vector of the music beat signal is extracted with the help of the variation law of the music beat strength, and the signal features obtained are(10)fa=1n∑i=1mhti∑℉στ.Among them,τ indicates the law of the music beat signal change and ℉ represents the length of the music signal and the σ strength of the music signal.In the feature extraction of music beat signal in dance action, firstly, the music beat signal is processed by absolute value to ensure the consistency of the later extracted music signal features. The interference factors are filtered by Gauss filter, and the music beat signal features are extracted according to the rhythm law of music beat signal.


## 2.2. Dance Action and Music Beat Data Match

The extracted two data pieces are matched according to the above collected dance action and music beat characteristic data. Since there is a corresponding relationship between dance action and music beat before training [18], the corresponding relationship between them is regarded as a corresponding model before matching, which is set as(11)Q=q1,q2,...qn.The attribute values of characteristic data are included in the dance action and music beat matching model. It mainly includes the identifier of action classification, the time length of standard action, and the rhythm matching of action.Therefore, the above corresponding action data and music signal data are divided with the help of a strong classifier to determine the probability value of paired occurrence between music beat and action and obtain(12)Kx,y=o∑i=1mδi×U℉i,σi,τi.Among them,δi is the classifier. This paper has 20 classifiers. U represents the feature value after unifying the dance action and the music signal, and o represents the weight value of the classifier.According to the determined probability, each classifier is scored in the standard matching to determine the dispersion degree of dance action and music beat matching, and the following results are obtained:(13)Li=1DISli>K0DISli<K,where K represents the threshold after matching between action and musical beat, and DISli represents the distance between ideal action eigenvalues.In order to further realize the matching between dance action and music beat [19], calculate the correlation between dance action and music beat and obtain(14)ℵε=∑i=1NDx×Mθ−ϕ,where θ represents the musical beat signal value, M represents the correlation coefficient, and ϕ represents the length value of the data characteristics.Therefore, the peak point of correlation data is introduced to complete the matching between dance action and music beat, and the following results are obtained:(15)gx,y=ℵεωHn21/m∑i=1NDx,where Hn2 represents the peak mass and Dx represents the relative local energy and the ω action feature amplitude influence.The matching process between dance action and music beat is shown in Figure2.Figure 2
Matching process between dance action and music beat.The corresponding relationship between dance action and music beat is regarded as the corresponding model during the process of matching dance action and music beat data, the pairwise occurrence probability between music beat and action is determined, the dance action and music beat matching model is discretized, and the peak point of relevant data is introduced to complete the matching between dance action and music beat.


## 2.3. Realization of Matching Error Recognition Based on Data Mining

According to the above dance action and music beat data matching, the data mining algorithm is used to identify the matching error. Data mining algorithm is an artificial intelligence algorithm, which includes many artificial intelligence algorithms and has the advantages of fast data processing speed and sensitive reflection [20]. Therefore, this paper realizes the error detection of dance action and music beat data matching with the help of the data mining algorithm in this algorithm. Through the data mining algorithm, the data characteristics after the two matching are analyzed, and the data with weak correlation is regarded as error data, which can more simply complete the matching error identification.The matched data are extracted by short-time Fourier transform [21]. The calculation [22] process of short-time Fourier transform is regarded as a long-time signal segmentation and signal segmentation in the same time period, and its calculation formula is(16)Fx,y=∑i=1b/2−1ϕ1xl+nχ×∅w/n,where Fx,y represents the input signal of the data after the N th match, ϕ1 represents the Hamming window function, and nχ represents the index of the matching point data ∅ representing the number of jumps in the adjacent frame action.After segmenting the matched data features, since the matched data is a kind of video image, it is necessary to convert the spectrum diagram into simple and easy to analyze matching feature data [23], so as to obtain(17)Fx,y′=℃ϕlxl+nχ×∅w/n2,Fx,y′ represents the simplified spectral image feature, and ℃ represents the spectral map power.The support vector machine algorithm of data mining algorithm is used to identify the matching error of the segmented matching data. Assuming that the set matching data is in a certain feature space [24], the linearly separable training linear data set is(18)ς=a1,a1,a2,a2,...aN,aN,where ς represents the linear separable property data.The functional distance [25] from the point in the matching data set in the training set to the hyperplane is expressed as(19)distance=yiϖ∗xi+b.Among these, distance represents the distance between the hyperplanes, andb represents the bias coefficient.According to the distance between the determined matching data, the recognition model based on support vector machine is set to obtain(20)Ξi=yiωϖϖ+bω.According to the set model, the constraint conditions for error identification are limited, the matched data are limited according to the set identification constraint conditions, and the data that do not meet the limit conditions are regarded as matched error data. The set constraint conditions are(21)s.tΞiLωϖϖ+bω>υ,where L represents the Lagrangian function and υ represents the proportion of the proportional-scaled hyperplane.Input the determined matching data set into the set error detection model, output the final error identification result, and obtain(22)ξa,b=Γ12w2+1v∑bωΘ,where v represents the relaxation variable, Γ represents the final output of the error detection result data, and Θ represents the minimum taxonomic plane distance value of the data in the error detection.To complete the matching error recognition based on data mining, the matching data features are extracted and segmented using a short-time Fourier transform, the segmented matching data is transformed into matching data, the matching error recognition model is established using a support vector mechanism, and the error detection constraint conditions are set.


## 3. Experimental Analysis

### 3.1. Experimental Scheme Design

In order to verify the effectiveness of the proposed method, experimental analysis is carried out. The experiment selects a video of the world professional Latin dance cup competition in MySQL database as the research object. The length of the video is about 10 minutes. The video in which the dance movements of a group of professional players match their music beat is the recognition object. The music selected in the Latin dance performance is the episode of the movie “dirty dancing”-representative. The beat rhythm in the music segment is very obvious, and its audio signal characteristics are shown in Figure3.Figure 3
Characteristic diagram of audio signal.In order to reduce the error in recognition, the Latin dance music and dance video are denoised. Using professional audio processor to collect the characteristic data of sample Latin dance video clips, this data is taken as the research object to complete the experimental analysis. In the experiment, Windows XP system is used for experimental research, and professional data processing software is used to process the sample data. The Latin dance segments selected in the experiment are shown in Figure4.Figure 4
Experimental sample image.


### 3.2. Analysis of Experimental Results

In order to verify the effectiveness of the proposed method, the experiment is carried out by comparing the methods in this paper, in [6], and in [7]. The effectiveness of this method is verified by comparing the recognition accuracy of the three methods in identifying the matching error between sample dance segments and music beat. A total of 100 iterations are carried out in the experiment, and the recognition accuracy result is the mean value of multiple recognition. The experimental results are shown in Figure 5.Figure 5
Recognition accuracy analysis of matching error between sample dance segments and music beat identified by different methods.By analyzing the experimental data in Figure5, it can be seen that there are some differences in the recognition accuracy of the matching error between sample dance segments and music beats by comparing the three methods with the methods in this paper, in [6], and in [7]. Among them, the recognition accuracy of the proposed method is higher and always higher than 90%. In contrast, the recognition accuracy of the matching error between dance segments and music beats of the other two methods is lower than that of the method in this paper, and there is a large fluctuation. After comparing the three approaches, it was discovered that this method had the highest recognition accuracy, indicating its viability.The connection between the action and music beat matching in the sample dance video using the techniques of this study, in [6], and in [7] is empirically investigated to further validate the efficiency of the suggested approach. The efficiency of the strategy is tested in this experiment by calculating the correlation coefficient. The value range of the correlation coefficient among them is [0, 1]. The closer the value is to 1, the higher the correlation is. The correlation coefficient results after comparing the three methods are shown in Figure 6.Figure 6
Correlation analysis of action and music beat matching in dance videos with different methods.By analyzing the experimental result data in Figure6, it can be seen that the correlation coefficients of action and music beat matching in the sample dance video are different by using the methods of this paper, in [26], and in [27]. The correlation coefficient of this approach is the closest to 1 and the greatest is about 0.98, but the correlation coefficient of the other two ways is lower, demonstrating the usefulness of this method.


## 3.1. Experimental Scheme Design

In order to verify the effectiveness of the proposed method, experimental analysis is carried out. The experiment selects a video of the world professional Latin dance cup competition in MySQL database as the research object. The length of the video is about 10 minutes. The video in which the dance movements of a group of professional players match their music beat is the recognition object. The music selected in the Latin dance performance is the episode of the movie “dirty dancing”-representative. The beat rhythm in the music segment is very obvious, and its audio signal characteristics are shown in Figure3.Figure 3
Characteristic diagram of audio signal.In order to reduce the error in recognition, the Latin dance music and dance video are denoised. Using professional audio processor to collect the characteristic data of sample Latin dance video clips, this data is taken as the research object to complete the experimental analysis. In the experiment, Windows XP system is used for experimental research, and professional data processing software is used to process the sample data. The Latin dance segments selected in the experiment are shown in Figure4.Figure 4
Experimental sample image.


## 3.2. Analysis of Experimental Results

In order to verify the effectiveness of the proposed method, the experiment is carried out by comparing the methods in this paper, in [6], and in [7]. The effectiveness of this method is verified by comparing the recognition accuracy of the three methods in identifying the matching error between sample dance segments and music beat. A total of 100 iterations are carried out in the experiment, and the recognition accuracy result is the mean value of multiple recognition. The experimental results are shown in Figure 5.Figure 5
Recognition accuracy analysis of matching error between sample dance segments and music beat identified by different methods.By analyzing the experimental data in Figure5, it can be seen that there are some differences in the recognition accuracy of the matching error between sample dance segments and music beats by comparing the three methods with the methods in this paper, in [6], and in [7]. Among them, the recognition accuracy of the proposed method is higher and always higher than 90%. In contrast, the recognition accuracy of the matching error between dance segments and music beats of the other two methods is lower than that of the method in this paper, and there is a large fluctuation. After comparing the three approaches, it was discovered that this method had the highest recognition accuracy, indicating its viability.The connection between the action and music beat matching in the sample dance video using the techniques of this study, in [6], and in [7] is empirically investigated to further validate the efficiency of the suggested approach. The efficiency of the strategy is tested in this experiment by calculating the correlation coefficient. The value range of the correlation coefficient among them is [0, 1]. The closer the value is to 1, the higher the correlation is. The correlation coefficient results after comparing the three methods are shown in Figure 6.Figure 6
Correlation analysis of action and music beat matching in dance videos with different methods.By analyzing the experimental result data in Figure6, it can be seen that the correlation coefficients of action and music beat matching in the sample dance video are different by using the methods of this paper, in [26], and in [27]. The correlation coefficient of this approach is the closest to 1 and the greatest is about 0.98, but the correlation coefficient of the other two ways is lower, demonstrating the usefulness of this method.


## 4. Conclusion

In order to improve the matching effect between dance action and music beat, this paper designs a matching error recognition method between dance action and music beat based on data mining, by extracting the characteristic data of dance action and music beat, and effectively matching them, extracting the characteristics of matching data through short-time Fourier transform, segmenting it, transforming the segmented matching data into matching data, building the matching error identification model with the help of support vector mechanism, and setting the constraints of error detection to complete the matching error identification. The experimental results show that the proposed method has high recognition accuracy and high data matching correlation, which verifies the effectiveness of the proposed method.



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*Source: 8176863-2022-03-10.xml*