sis 18: e33

Research Article

Music Emotion Recognition Based on Long Short-Term Memory and Forward Neural Network

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  • @ARTICLE{10.4108/eai.27-1-2022.173162,
        author={Aizhen Liu},
        title={Music Emotion Recognition Based on Long Short-Term Memory and Forward Neural Network},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2022},
        month={1},
        keywords={music emotion recognition, long short-term memory, forward neural network, MFCC, RP},
        doi={10.4108/eai.27-1-2022.173162}
    }
    
  • Aizhen Liu
    Year: 2022
    Music Emotion Recognition Based on Long Short-Term Memory and Forward Neural Network
    SIS
    EAI
    DOI: 10.4108/eai.27-1-2022.173162
Aizhen Liu1,*
  • 1: College of Art and Design, Luoyang Vocational College of Science and Technology, Luoyang 471000 China
*Contact email: byoungholee@qq.com

Abstract

This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173793. In this paper, we propose a new music emotion recognition method based on long short-term memory and forward neural network. First, Mel Frequency Cepstral Coefficient (MFCC) and Residual Phase (RP) are weighted to extract music emotion features, which improves the recognition efficiency of music emotion features. Meanwhile, in order to improve the classification accuracy of music emotion and shorten the training time of the new model, Long short-term Memory network (LSTM) and forward neural network (FNN) are combined. Using LSTM as the feature mapping node of FNN, a new deep learning network (LSTM-FNN) is proposed for music emotion recognition and classification training. Finally, we conduct the experiments on the emotion data set. The results show that the proposed algorithm achieves higher recognition accuracy than other state-of-the-art complex networks.