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Music Exploration Based on XGBoost Algorithm and Node2vec-BP Model

Published: 06 September 2021 Publication History

Abstract

The development trend of music genres tends to be homogenous, due to the reduction of music production costs, changes in market demand and the consolidation of composers’ thinking. In this article, the significance of each feature of music is inferred through the XGBoost algorithm, and further evidence of music homogeneity is obtained from the time series analysis of feature decomposition. In addition, we propose a high-performance music prediction system that combines Node2vec and BP algorithm to consider the influence of composers on the music revolution. It is concluded that the Node2vec-BP algorithm has higher generalization ability and accuracy than the traditional BP algorithm.

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ICMLT '21: Proceedings of the 2021 6th International Conference on Machine Learning Technologies
April 2021
183 pages
ISBN:9781450389402
DOI:10.1145/3468891
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 September 2021

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Author Tags

  1. BP algorithm
  2. Feature extraction
  3. Node2vec
  4. XGBoost

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