Migratory Choreography and Spaces of Resistance

Choreography is an art form in and of itself. Because music and dance have always appeared at the same time throughout human history, music has had a significant influence on dance arrangement. It is important to arrange appropriate dance movements based on the music pieces chosen by users when creating choreography. This paper proposes a mixed density network-based music choreography algorithm in response to the current state of music choreography. The algorithm should be able to convert motion and music signals into a high-level semantic meaning that is compatible with human cognition, compare the degree of matching, and arrange the dance based on the music and motion segments that match. Furthermore, the consistency and authenticity of the movements in the dance created in this paper have been improved. Users’ subjective feedback indicates that the choreography results in this paper are more closely aligned with the music. In the field of music choreography, it has some practical utility.

Section: Related Workmentioning

confidence: 99%

[Retracted] Music Choreography Algorithm Based on Feature Matching and Fragment Segmentation

Wang

2022

“…The first calculation to study the application of MCP in the field of mechanical learning was in the perceptron calculation in 1958 [ 12 ]. The algorithm uses the MCP model, conducts a two-analysis of the orthogonality protection data provided by the training, and uses the gradient descent method to automatically learn information from the training samples and modify the connection weights [ 13 ]. In 1962, the method was proved to be theoretically convergent and thus promoted the first stage of scientific research on neural layer networks [ 14 ].…”

Section: Methods Of Automatic Arrangement Of Sports Dance Based On De...mentioning

confidence: 99%

Automatic Arrangement of Sports Dance Movement Based on Deep Learning

Feng

Zhao

Zhang

2022

Sports dance is a new form of sports that integrates sports, dance, music, and other elements. The core content of “dance” is an important carrier for athletes to display their body art. This article aims to study the automatic arrangement of sports dance based on deep learning. This article first introduces the development process of deep learning. As the latest research direction developed from artificial neural network technology in machine learning, deep learning has attracted widespread attention from the society. And then proposing a shallow regression model based on deep learning, a convolutional neural network based on deep learning, and an offline sorting regression model, given the general process of deep learning, then, based on the clustering algorithm, the deep learning was researched, and the sport dance movement arrangement was analyzed based on the deep learning. The experimental results of this article show that deep learning can effectively enhance the artistic ability of automatic choreography in sports dance and increase the accuracy of dance movements by 80%. At the same time, on the basis of deep learning, the practical ability is strengthened on the basis of consolidating theory, to further improve one’s own business ability and educational technology level, actively absorb advanced teaching methods, and earnestly delve into reasonable teaching methods. It is also used in curriculum training practice to actively gain insight into new development trends in educational methods and skills, to enhance the artistic creativity of students’ arrangements.

“…Radial basis function neural network is a feedforward neural network based on interpolation algorithm, which can approximate any nonlinear function with any accuracy [ 17 ]. Compared with BP network, the excitation function of RBF neural network is radial symmetric function, which only responds to the input signal locally, so it avoids the problem of long training time and easy to fall into local minimum of BP network.…”

Section: Radial Basis Function Neural Network Modelmentioning

confidence: 99%

Aesthetic Characteristics of Dance Based on Few-Shot Learning and Neural Networks

2022

Dance is constantly discovering truth, goodness, and beauty in human social life, spreading truth, goodness, and beauty, and fully expressing the artistic pursuit of dance beauty. It shapes different dance images, expresses the aesthetic consciousness and feelings of dance, and resonates with the audience to meet their aesthetic needs through various forms of movement. Because the RBF neural network model is good at approximating functions, many researchers have begun to use the RBNN approximation model for engineering design. Due to the limited dance data available for research, this paper uses radial basis function neural network model to study the aesthetic characteristics of dance in the context of few-shot learning. When the time index reaches 50, the average ratio of the L-MBP algorithm is 33.4 percent, 32.5 percent for the RBNN algorithm, and 46.3 percent for this method. As can be seen, this method has the highest ratio of the three algorithms, giving it a distinct advantage in terms of dance aesthetics. As a result, this paper establishes a neural network model, trains and simulates the network model, studies and analyzes the influence of changes in influencing factors on the aesthetic characteristics of dance, and provides a new idea for the prediction of the aesthetic characteristics of dance and a reference for optimizing the design of the aesthetic system of dance using the prediction ability of radial basis function neural networks.