Auditory Tension Classification Algorithm Based on Interval Statistics Approach

To process the information contained in music and dance art, a direct study science based on interval statistics in the process of human emotions is put forward. This paper aims to establish the basis of emotional modeling which need to explore the correspondence between music data and auditory tension. It is the basic starting point and it contains the knowledge model and data model of emotion recognition modeling method and it mainly analyzes the music tone said, transfer and recognition in the process of psychological mode. Through data collection, data preprocessing, classification rule mining, we evaluate the output four steps for auditory tone recognition model research. Then it is based on fisher discriminant method which adopts SPSS for data analysis. In actual estimation of the unknown music auditory stress levels, the result of the experiment of rate down through generation of data shows more scientific rules.