Music emotions recognition by cognitive classification methodologies

Bai, Junjie; Luo, Kan; Peng, Jun; Shi, Jingping; Wu, Ying; Feng, Lei; Li, Jianqing; Wang, Yingxu

doi:10.1109/icci-cc.2017.8109740

Cited by 13 publications

(3 citation statements)

References 20 publications

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“…Neural Network [31][32][33] 85.80% Support Vector Machine [34][35][36] 77.80% K-Nearest Neighbor [33,37,38] 88.94% Multi-layer Perceptron [38][39][40] 78.16% Bayes [41][42][43] 69.62% Extreme Learning Machine [41] 87.10% K-Means [43] 78.06% Linear Discriminant Analysis [42] 71.30% Gaussian Process [44] 71.30% To improve on the performance of the SOA methods, we used the features generated by M3GP in Figure 15. This kind of transfer learning was used in [45] with success and the best training transformation found in M3GP was used to transform the dataset into a new one (M3GP tree in Section 4.2), considering that these new features contain more information to simplify the learning process of the SOA methods.…”

Section: Classifier Average Performancementioning

confidence: 99%

EEG-Based Emotion Recognition Using Deep Learning and M3GP

et al. 2022

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This paper presents the proposal of a method to recognize emotional states through EEG analysis. The novelty of this work lies in its feature improvement strategy, based on multiclass genetic programming with multidimensional populations (M3GP), which builds features by implementing an evolutionary technique that selects, combines, deletes, and constructs the most suitable features to ease the classification process of the learning method. In this way, the problem data can be mapped into a more favorable search space that best defines each class. After implementing the M3GP, the results showed an increment of 14.76% in the recognition rate without changing any settings in the learning method. The tests were performed on a biometric EEG dataset (BED), designed to evoke emotions and record the cerebral cortex’s electrical response; this dataset implements a low cost device to collect the EEG signals, allowing greater viability for the application of the results. The proposed methodology achieves a mean classification rate of 92.1%, and simplifies the feature management process by increasing the separability of the spectral features.

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Section: Classifier Average Performancementioning

confidence: 99%

EEG-Based Emotion Recognition Using Deep Learning and M3GP

et al. 2022

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“…Gkaitatzis et al [17] analyzed the influence of different music on emotions using 32 EEG channels and found that listening to 120 BPM music was satisfactory and pleasant, while music that was faster than 170 BPM and slower than 70 BPM made people feel more stressed and unhappy. Bai et al [18] analyzed four types of music emotion: happy, angry, sad, and relaxed. They extracted 548-dimensional music features, and the accuracy rate was more than 80%.…”

Section: Introductionmentioning

confidence: 99%

EEG Emotion Recognition Applied to the Effect Analysis of Music on Emotion Changes in Psychological Healthcare

Zhou

Liang

Liu

et al. 2022

IJERPH

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Music therapy is increasingly being used to promote physical health. Emotion semantic recognition is more objective and provides direct awareness of the real emotional state based on electroencephalogram (EEG) signals. Therefore, we proposed a music therapy method to carry out emotion semantic matching between the EEG signal and music audio signal, which can improve the reliability of emotional judgments, and, furthermore, deeply mine the potential influence correlations between music and emotions. Our proposed EER model (EEG-based Emotion Recognition Model) could identify 20 types of emotions based on 32 EEG channels, and the average recognition accuracy was above 90% and 80%, respectively. Our proposed music-based emotion classification model (MEC model) could classify eight typical emotion types of music based on nine music feature combinations, and the average classification accuracy was above 90%. In addition, the semantic mapping was analyzed according to the influence of different music types on emotional changes from different perspectives based on the two models, and the results showed that the joy type of music video could improve fear, disgust, mania, and trust emotions into surprise or intimacy emotions, while the sad type of music video could reduce intimacy to the fear emotion.

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“…Para adentrarse en la investigación de las emociones en la música hay que estudiar los métodos de medición de las emociones generadas por la música que se han estado utilizando y desarrollando en las últimas décadas. Bai, Luo et al (2017) describen la disciplina y sus características: "MER (Music Emotion Recognition) [...] is a challenging field of studies" "how to evaluate the emotion of a song is a challenging problem [...], machine learning algorithm is the research hotspot" , y describen así la importancia que ha cobrado el "machine learning en 105 toda esta rama de la investigación. Pese a ser un mundo apasionante e interesantísimo, no es el objetivo de esta investigación adentrarse en las complejidades de las soluciones técnicas al problema de la medición de las emociones, sino sentar una base metodológica para poder realizar algunas experimentaciones con elementos de medición emocional.…”

Section: Mediciónunclassified

Personalización subjetiva en los sistemas de música interactiva en los videojuegos. En busca de una experiencia de juego a la medida del individuo

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Music emotions recognition by cognitive classification methodologies

Cited by 13 publications

References 20 publications

EEG-Based Emotion Recognition Using Deep Learning and M3GP

EEG-Based Emotion Recognition Using Deep Learning and M3GP

EEG Emotion Recognition Applied to the Effect Analysis of Music on Emotion Changes in Psychological Healthcare

Personalización subjetiva en los sistemas de música interactiva en los videojuegos. En busca de una experiencia de juego a la medida del individuo

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