文献类型：期刊文献

英文题名：Construction and Application of Music Style Intelligent Learning System Based on Situational Awareness

作者：Chen, Yunfei[1]

第一作者：陈云飞

通讯作者：Chen, YF[1]

机构：[1]Xinxiang Univ, Xinxiang 453003, Henan, Peoples R China

第一机构：新乡学院

通讯机构：[1]corresponding author), Xinxiang Univ, Xinxiang 453003, Henan, Peoples R China.|[11071]新乡学院;

年份：2022

卷号：2022

外文期刊名：MATHEMATICAL PROBLEMS IN ENGINEERING

收录：;EI(收录号：20224112858791);Scopus(收录号：2-s2.0-85139277515);WOS:【SCI-EXPANDED(收录号:WOS:000864508100003)】；

语种：英文

摘要：Contextual representation recommendation directly uses contextual prefiltering technology when processing user contextual data, which is not the integration of context and model in the true sense. To this end, this paper proposes a context-aware recommendation model based on probability matrix factorization. We design a music genre style recognition and generation network. In this network, all the sub-networks of music genres share the explanation layer, which can greatly reduce the learning of model parameters and improve the learning efficiency. Each music genre sub-network analyzes music of different genres, realizing the effect of multitasking simultaneous processing. In this paper, a music style recognition method using a combination of independent recurrent neural network and scattering transform is proposed. The relevant characteristics of traditional audio processing methods are analyzed, and their suitable application scenarios and inapplicability in this task scenario are expounded. Starting from the principle of scattering transform, the superiority and rationality of using scattering transform in this task are explained. This paper proposes a music style recognition method combining two strategies of scattering transform and independent recurrent neural network. In the case that the incremental data set is all labeled, this paper introduces the solution of the convex hull vector, which reduces the training time of the initial sample. According to the error push strategy, an incremental learning algorithm based on convex hull vector and error push strategy is proposed, which can effectively filter historical useful information and at the same time eliminate useless information in new samples. Experiments show that this method improves the accuracy of music style recognition to a certain extent. Music style recognition based on independent recurrent neural network can achieve better performance.