Web music emotion recognition based on higher effective gene expression programming

In the study, we present a higher effective algorithm, called revised gene expression programming (RGEP), to construct the model for music emotion recognition. Our main contributions are as follows: firstly, we describe the basic mechanisms of music emotion recognition and introduce gene expression programming (GEP) to deal with the model construction for music emotion recognition. Secondly, we present RGEP based on backward-chaining evolutionary algorithm and use GEP, RGEP, and SVM to construct the models for music emotion recognition separately, the results show that the models obtained by SVM, GEP, and RGEP are satisfactory and well confirm the experimental values. Finally, we report the comparison of these models, and we find that the model obtained by RGEP outperforms classification accuracy of the model by GEP and takes almost 15% less processing time of GEP and even half processing time of SVM, which offers a new efficient way for solving music emotion recognition problems; moreover, because processing time is essential for the problem of large scale music information retrieval, therefore, RGEP might prompt the development of the music information retrieval technology.