Music Emotion Recognition Model Using Gated Recurrent Unit Networks and Multi-Feature Extraction

Abstract

A large number of music platforms have appeared on the Internet recently. The deep learning framework for music recommendation is still very limited when it comes to accurately identifying the emotional type of music and recommending it to users. Languages, musical styles, thematic scenes, and the ages to which they belong are all common classifications. And this is far from sufficient, posing difficulties in music classification and identification. As a result, this paper uses the methods of music emotion multi-feature extraction, BiGRU model design, and music theme scene classification model design to improve the accuracy of music emotion recognition. It develops the BiGRU emotion recognition model and compares it to other models. BiGRU can correctly identify happy and sad emotion music up to 79 percent and 81.01 percent of the time, respectively. It goes far beyond Rnet-LSTM, and the greater the difference in emotion categories, the easier it is to analyze the feature sequence containing emotional features and the higher the recognition accuracy. This is especially evident in the accuracy with which happiness and sadness are recognized. It can meet users' needs for music recognition in a variety of settings.

Attention-based CNN-BiGRU for Bengali Music Emotion Classification

“…The BiGRU emotion recognition model is developed in another research, and it is compared to other models. Up to 79 % and 81.01 % of the time, respectively, BiGRU can properly detect music with joyful and sad emotions [26]. According to the findings, Naive Bayes had the highest classification accuracy for musical mood, at 86.64% [27].…”

“…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

“…Pooling layers are typically used after convolution layers to perform nonlinear down sampling by taking the maximum or average value for each small subsection of the matrix. Our model comprises two consecutive onedimensional convolution layers that use 32 and 16 lters with ReLU [30] as an activation function, a kernel size of 7, stride 1, and max pooling of size 4 and stride 4. We applied batch normalization [31] after each convolutional layer to stabilize the network.…”