With the development of society and the progress of technology, the piano education industry has a large market. In view of the problem of high payment fees in the piano education industry, the scientific and automatic nature of piano performance evaluation has attracted people’s attention. However, since most of the piano performance evaluation schemes are based on rules, the continuity of the piano music and the accuracy of playing are ignored. Therefore, the purpose is to design a scientific piano performance evaluation scheme that can play a certain role in the sustainable development of the piano education industry. Firstly, long short-term memory in deep learning is explored. Secondly, the musical characteristics of piano performance are analyzed according to the musical instrument digital interface. The piano music features are extracted, and a long short-term memory-based musical instrument digital interface piano performance evaluation model is constructed. Finally, it analyzes the number of hidden layers implemented in the long short-term memory model for piano performance evaluation. The accuracy of piano performance evaluation under different models is analyzed. Under the bidirectional long short-term memory network model, different piano performance levels are evaluated to realize the study of piano performance evaluation strategies. Compared with the accuracy of the recurrent neural network and the long short-term memory model with different hidden layers, the bidirectional long short-term memory model has the highest test accuracy, with an average of 69.78%. When the hidden layer of the bidirectional long short-term memory model is 3, the loss function L value is the smallest, which is 0.11. Different levels of piano skills are evaluated, and the results of the systematic evaluation are consistent with the performance of different levels. This shows that the BLSM model is feasible for the piano performance evaluation strategy system. This study not only conducts an in-depth analysis of the deep learning long short-term memory model but also proposes a long short-term memory-based musical instrument digital interface piano performance evaluation model. Additionally, the flaws such as the incomplete consideration of musical continuity and expressiveness when evaluating piano performance pieces have been compensated. Finally, through different model validations, the bidirectional long short-term memory model is concluded with good accuracy in piano performance evaluation. These conclusions provide theoretical research and practical significance for the accuracy of piano performance evaluation.
Summary Aiming at exploring the opto‐electric target tracking, which is an important technology in the field of computer vision, the binocular stereo vision camera opto‐electric target tracking is studied and and a multi feature fusion characterization modeling method locally weighted is proposed. The target area is divided into multiple sub‐image areas by the modeling method, the feature histogram after the background weighting is extracted, and the sub‐image region is taken as a basic unit for adjusting the feature weight. The sub‐image area selected is regarded as the significant area, and the significant area is further extracted and fused in particle filter tracking algorithm. Then, the obtained significant are is conducted with color distribution processing. In the state prediction stage, the Mean Shift algorithm is applied to optimize each particle so that it converges to the optimal position. The experiment results showed that the multi feature fusion representation modeling method has better tracking accuracy and stability compared with the traditional fusion method and after the color distribution treatment; it has strong anti‐jamming for background effect. It is concluded that using Mean Shift algorithm for particle optimization can further strengthen the accurate tracking of the targets.
The purpose is to study the interactive teaching mode of human action recognition technology in music and dance teaching under computer vision. The human action detection and recognition system based on a three-dimensional (3D) convolutional neural network (CNN) is established. Then, a human action recognition model based on the dual channel is proposed on the basis of CNN, and the visual attention mechanism using the interframe differential channel is introduced into the model. Through experiments, the performance of the system in the process of human dance image recognition based on the Kungliga Tekniska Högskolan (KTH) dataset is verified. The results show that the dual-channel 3D CNN human action recognition system can achieve high accuracy in the first few rounds of training after the frame difference channel is added, the error can be reduced quickly, and the convergence can start quickly; the recognition accuracy of the system on KTH dataset is 96.6%, which is higher than that of other methods; for 3 × 3 × 3 basic convolution kernel, the best performance of the classification network can be obtained by pushing forward 0.0091 seconds in the calculation. Thereby, the dual-channel 3D CNN recognition system has good human action recognition accuracy in the dance interactive teaching mode of music teaching.
In the traditional recording system, recording any music includes a sizeable instrumental setup and allocates space for the music players. Lighter and fewer devices are replacing larger instruments due to technological advancement and epidemic environmental conditions. This research focuses on text, but audio and video types are also considered. Multiple signal classification with a 5G-based wireless communication network algorithm is implemented to perform the automatic recording and classification of the music data. In this research, a multi-modal gesture recognition dataset is considered for analysis. The dataset was obtained using sensor networks and an intelligent system to record the musical gestures and classify the recorded gestures. The development of machine learning algorithms is not limited to similar technological concepts. Still, it extends to almost all other technical resources such as the 5G network, signal processing, networking, and all other technical resources. This would lead to additional engineering challenges that are utilized in most cases, such as the development of gestures with multi-mode recording. This research has proposed MSA with WCN algorithm to perform intelligent analysis and classification of piano music gestures and is compared with the existing K-Means algorithm and achieved an accuracy of 99.12%.
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