Emotional representation of music in multi-source data by the Internet of Things and deep learning

Wang, Chunqiu; Ko, Young Chun

doi:10.1007/s11227-022-04665-3

Cited by 9 publications

(5 citation statements)

References 34 publications

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“…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]. The upgraded DBN network combined with the SVM classification algorithm can classify music emotions with an accuracy of 88.31% [28].…”

Section: B Related Workmentioning

confidence: 95%

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: 95%

Attention-based CNN-BiGRU for Bengali Music Emotion Classification

Ghosh

Riad

2022

ijcs

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“…In recent years, some studies have begun to explore how to use knowledge graphs for sentiment analysis. For example, reference [17] proposed a sentiment analysis model based on knowledge graphs, which can utilize the information in the knowledge graph to improve the performance of sentiment analysis. However, the above artificial intelligence-based sentiment analysis models also have some challenges and limitations.…”

Section: A Emotional Analysis Of Vocal Performancementioning

confidence: 99%

Design of Emotion Analysis Model IABC-Deep Learning-based for Vocal Performance

Zhu,

2024

IJACSA

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With the development of deep learning technology, and due to its potential in solving optimization problems with deep structures, deep learning technology is gradually being applied to sentiment analysis models. However, most existing deep learning-based sentiment analysis models have low accuracy issues. Therefore, this study focuses on the issue of emotional analysis in vocal performance. Firstly, based on vocal performance experts and user experience, classify the emotions expressed in vocal performance works to identify the emotional representations of music. On this basis, in order to improve the accuracy of emotion analysis models for deep learning based vocal performance, an improved artificial bee colony algorithm (IABC) was developed to optimize deep neural networks (DNN). Finally, the effectiveness of the proposed deep neural network based on improved artificial bee colony (IABC-DNN) was verified through a training set consisting of 150 vocal performance works and a testing set consisting of 30 vocal performance works. The results indicate that the accuracy of the sentiment analysis model for vocal performance based on IABC-DNN can reach 98%.

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“…Wang and Ko [26] presented the emotional depiction of music during the music experience. Initially, they employed Internet of Things sensors based on the principles of multi-modal technology to acknowledge the emotional impact of music.…”

Section: Literature Surveymentioning

confidence: 99%

Augmented Physics-Informed Neural Networks and Seahorse Optimization Algorithm for Music Scene Simulation and Emotion Regulation in Virtual Reality Technology

Xinye Wang

2024

jes

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Virtual Reality (VR) technology has shown promise in simulating music scenes for emotion regulation. The challenge is to enhance the realism and interactivity of VR-based music experiences to create a more immersive and emotionally impactful environment for users. This manuscript proposes an augmented physics-informed neural network and seahorse optimization algorithm for music scene simulation and emotion regulation in virtual reality technology (APINN-SHO-MSS-ERVRT). Initially, the data is collected via head-mounted display (HMD) in real time basis. Afterward, the data is fed to data-adaptive Gaussian average filtering (DAGAF) based pre-processing process. In the pre-processing segment, it improves virtual presence. The pre-processing output is given to augmented physics-informed neural network (APINN)and seahorse optimization algorithm (SHO) for effectively classifying the facial expression for fear and null. Therefore, SHO is proposed to enhance weight parameter of augmented physics-informed neural network classifier, which precisely classifies the facial emotion classifier. The proposed method is implemented in MATLAB and evaluated their performance with existing methods. The performance metrics, like accuracy, precision, sensitivity, specificity, computational time and ROC is analysed to the performance of the proposed method. The proposed APINN-SHO-MSS-ERVRT methods of accuracy are provide 90% higher accuracy for fear and 97% higher accuracy for null emotion is analysed when compared with existing methods GERR-NN-AMSVR, ER-VR-EEG and MTS-VRS-CDI. Proposed APINN-SHO-MSS-ERVRT method attains 0.87%, 0.88% and 0.89%higher ROC analysed to the existing methods respectively.

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Emotional representation of music in multi-source data by the Internet of Things and deep learning

Cited by 9 publications

References 34 publications

Attention-based CNN-BiGRU for Bengali Music Emotion Classification

Attention-based CNN-BiGRU for Bengali Music Emotion Classification

Design of Emotion Analysis Model IABC-Deep Learning-based for Vocal Performance

Augmented Physics-Informed Neural Networks and Seahorse Optimization Algorithm for Music Scene Simulation and Emotion Regulation in Virtual Reality Technology

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