Design of music training assistant system based on artificial intelligence

Abstract

In order to improve the input accuracy and response speed of music training, this paper designs an intelligent assistant system. The architecture is divided into infrastructure layer, data layer, application layer and presentation layer. In the hardware design, the combination of ARM and digital signal processor (DSP) is used to realize the interaction between data analysis and human and interface. In the software design, cepstrum algorithm is used to extract cepstrum features of music signals, linear smoothing algorithm is used to filter, dynamic time warping method is used to match patterns, and radial basis function algorithm is used to output the results. Thus, the overall design of the music-assisted training system is completed. Experimental results show that the signal-to-noise ratio of music signal transmission is more than 14dB, the accuracy is higher than 99.5%, and the response speed of serving 240 users is only 1s. The system has strong operability and good performance of music assistant training.