Research Article
Music Emotion Recognition Based on Long Short-Term Memory and Forward Neural Network
@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}, volume={9}, number={4}, 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
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This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173793.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.
Copyright © 2022 Aizhen Liu et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
