Music interaction method and system

Abstract

The invention provides a music interactive method and system. The method includes the steps that a sensitive signal of a user to current playing music is acquired; a music signal corresponding to the music is acquired; the sensitive signal and the music signal are analyzed, and the correlation coefficient of the sensitive signal and the correlation coefficient of the music signal are acquired; the correlation coefficients are compared with a preset first threshold value, and if the correlation coefficients are larger than or equal to the first threshold value, it can be confirmed that the user likes the current playing music. By the adoption of the method, the process for judging whether the user likes the music or not in the prior art is simplified, and the recognition precision rate of the likeability of the user to the music is improved.

Description

technical field [0001] The invention relates to music interaction technology, in particular to a music interaction method and system. Background technique [0002] With the development of modern society, music has become an indispensable element in people's daily life. However, there are more and more types and quantities of music. When listening to music, the player cannot automatically find tracks that meet the user's needs, thereby reducing the user experience. For example, if the player is in the pocket when the user is driving, the user cannot change the music tracks he or she likes in the player in time. [0003] For this reason, a method for automatically switching music tracks in the player is proposed in the prior art. For example, the user's mood information is obtained from the user's facial expressions and physiological indicators through emotion analysis technology, and judged according to the user's mood information. Whether the user likes the music track cur...

AI Technical Summary

Problems solved by technology

[0004] However, when the user is exercising vigorously, the physiological parameters will change significantly, resulting in inaccurate identification of the user's mood information

In addition, the above-mentioned process of identifying the user's mood information is very complicated, and the accuracy of determining the user's preference for music tracks based on the user's mood information is low.

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