Music Emotion Classification Method Based on Deep Learning and Explicit Sparse Attention Network

Abstract

In order to improve the accuracy of music emotion recognition and classification, this study combines an explicit sparse attention network with deep learning and proposes an effective emotion recognition and classification method for complex music data sets. First, the method uses fine-grained segmentation and other methods to preprocess the sample data set, so as to provide a high-quality input data sample set for the classification model. The explicit sparse attention network is introduced into the deep learning network to reduce the influence of irrelevant information on the recognition results and improve the emotion classification and recognition ability of music sample data set. The simulation experiment is based on the actual data set of the network. The experimental results show that the recognition accuracy of the proposed method is 0.71 for happy emotions and 0.688 for sad emotions. It has a good ability of music emotion recognition and classification.

Attention-based CNN-BiGRU for Bengali Music Emotion Classification

“…On other hand, MFCC with SVM produced an 85.7% recognition rate [37]. According to the experimental findings, the proposed approach has a recognition accuracy of 71% for joyful emotions and 68.8% for sad emotions [38]. For three experiments, MECS 1 obtains accuracy of 91 %, 88 %, and 86 %, MECS 2 obtains accuracy of 87 %, 82 %, and 79 %, MECS 3 obtains 82.3 %, 82 percent, and 81.6 % accuracy [39] [40].…”

“…BiGRU model, convolutional long short-term memory deep neural network (CLDNN) model, CNN-LSTM model, etc. are recently proposed deep learning algorithms to classify musical emotions [26] [38] [39]. But previous researches had the flaws of low accuracy and overfitting problem.…”

A Deep Attentive Network for Music Emotion Recognition using the chorus part of the music

“…Also, they optimize the emotional representation to make the training process faster. Jia [21] presented a DNN that uses a spectrogram and Low-level descriptors (LLD) as input. The model mainly comprises spectrogram + CNN-LSTM and LLDs + DNN.…”

“…LSTM can effectively capture the context information of the input sequence and solve the problem of saving and transmitting sequence information [16]. AM gives different weights to the features, highlights the in uence of the more critical factor, and helps the model make a more accurate classi er [21,29]. Figure 3 shows the block diagram of the proposed network.…”

Retracted: Music Emotion Classification Method Based on Deep Learning and Explicit Sparse Attention Network

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”