A Study of Instrument Playing Action Recommendation Scheme with AI Vision Technology

To address the problem of low accuracy rate of musical instrument playing movements with AI vision technology at this stage, we propose to build a logical statistical model of musical instrument playing movements using improved particle swarm algorithm to collect data and pinpoint simple human movements. The model is implemented based on a certain sports bracelet to recognize the basic playing movements. Firstly, the support vector machine (SVM) model is optimized by improved particle swarm optimization (IPSO) algorithm and structured local dictionary learning, and then the optimized SVM is used to classify the human activity detection data. And the error information is corrected by using scenario analysis method. The results demonstrate that the accuracy rate of performance action information detection can reach 94.3% by applying the model on a sports bracelet, which shows that AI vision technology can achieve accurate pushing of target human performance action solutions by combining with this user portrait model. The creation of complex musical compositions requires the display of true creativity, which depends on various factors related to the hierarchy of musical language. Music generation faces algorithmic problems, and in this paper we want to apply deep learning models based on AI vision techniques to the human music composition and creative process.