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

Niu, Nana

doi:10.1155/2022/5732687

Cited by 5 publications

(3 citation statements)

References 21 publications

(21 reference statements)

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“…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].…”

Section: B Related Workmentioning

confidence: 92%

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Attention-based CNN-BiGRU for Bengali Music Emotion Classification

Ghosh

Riad

2022

ijcs

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For Bengali music emotion classification, deep learning models, particularly CNN and RNN are frequently used. To extract meaningful knowledge, however, past studies' shortcomings of low accuracy and overfitting have to be addressed. We have proposed a model combining Conv1D, Bi-GRU and the Bahdanau attention mechanism for music emotion classification of our Bengali music dataset. The model integrates distinct MFCCs wav preprocessing methods with deep learning methods and attention-based methods. The attention mechanism has increased the accuracy of the proposed classification model. The music is finally classified into one of the four emotion classes: Angry, Happy, Relax, Sad. The proposed Conv1D+BiGRU+Attention model is validated as more effective and efficient at classifying emotions in the Bengali music dataset than baseline methods, according to comparisons with baseline models. For our Bengali music dataset, the performance of our proposed model is 95%.

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Section: B Related Workmentioning

confidence: 92%

“…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.…”

Section: A Introductionmentioning

confidence: 99%

Attention-based CNN-BiGRU for Bengali Music Emotion Classification

Ghosh

Riad

2022

ijcs

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“…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.…”

Section: Model Descriptionmentioning

confidence: 99%

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

Talaghat,

Parvinnia,

Boostani

et al. 2023

Preprint

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In the domain of music information retrieval, music emotion recognition (MER) is an active area of research. The chorus part of the music is the most critical and emotional section. In this paper, we proposed an MER approach that combines Convolutional Neural Networks with Long Short-Term Memory to extract emotional features from the log-mel spectrogram of the music chorus part. Then, the features feed into an attention mechanism block to enhance helpful information. Also, we introduce a new dataset for the MER task that consists of 10,438 samples with valence/arousal labels. The results show that using chorus parts of music can improve the deep models' performance. Also, experiments show our proposed method achieves 15% and 40% relative improvement of valence and arousal, respectively, on the R2 score compared to the state-of-the-art and baseline models.

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