Empirical Research on a Fuzzy Model of Music Emotion Classification Based on Pleasure-Arousal Model

Huang, Siqi; Zhou, Li; Liu, Zhentao; Ni, Shan; He, Jingxian

doi:10.23919/chicc.2018.8483910

Cited by 10 publications

(7 citation statements)

References 5 publications

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“…Further, musical emotion characteristics can be classified based on musical dimensions, such as pitch height, loudness, timbre, tempo, and intensity [64,65]. For instance, referring to the Plutchik 3-D Circumplex Model, a pleasure-arousal (PA) model of musical emotion classification based on register, tempo and dynamic was presented [66].…”

Section: Musical Emotionmentioning

confidence: 99%

Effects of the Musical Sound Environment on Communicating Emotion

Meng

Jiang

Liu

et al. 2020

IJERPH

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The acoustic environment is one of the factors influencing emotion, however, existing research has mainly focused on the effects of noise on emotion, and on music therapy, while the acoustic and psychological effects of music on interactive behaviour have been neglected. Therefore, this study aimed to investigate the effects of music on communicating emotion including evaluation of music, and d-values of pleasure, arousal, and dominance (PAD), in terms of sound pressure level (SPL), musical emotion, and tempo. Based on acoustic environment measurement and a questionnaire survey with 52 participants in a normal classroom in Harbin city, China, the following results were found. First, SPL was significantly correlated with musical evaluation of communication: average scores of musical evaluation decreased sharply from 1.31 to −2.13 when SPL rose from 50 dBA to 60 dBA, while they floated from 0.88 to 1.31 between 40 dBA and 50 dBA. Arousal increased with increases in musical SPL in the negative evaluation group. Second, musical emotions had significant effects on musical evaluation of communication, among which the effect of joyful-sounding music was the highest; and in general, joyful- and stirring-sounding music could enhance pleasure and arousal efficiently. Third, musical tempo had significant effect on musical evaluation and communicating emotion, faster music could enhance arousal and pleasure efficiently. Finally, in terms of social characteristics, familiarity, gender combination, and number of participants affected communicating emotion. For instance, in the positive evaluation group, dominance was much higher in the single-gender groups. This study shows that some music factors, such as SPL, musical emotion, and tempo, can be used to enhance communicating emotion.

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Section: Musical Emotionmentioning

confidence: 99%

Effects of the Musical Sound Environment on Communicating Emotion

Meng

Jiang

Liu

et al. 2020

IJERPH

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“…The type of annotation of the emotions is another important criterion of comparison, as well as a key success factor in the field of MER. Although there are some works that consider the composer intention [15], in most of the cases the annotations are carried out according to the emotional perception of the listener. Among these works, there are mainly two approaches for annotating the songs: static and dynamic.…”

Section: Related Workmentioning

confidence: 99%

Emotional classification of music using neural networks with the MediaEval dataset

Medina

Beltrán

2020

Pers Ubiquit Comput

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The proven ability of music to transmit emotions provokes the increasing interest in the development of new algorithms for music emotion recognition (MER). In this work, we present an automatic system of emotional classification of music by implementing a neural network. This work is based on a previous implementation of a dimensional emotional prediction system in which a Multilayer Perceptron (MLP) was trained with the freely available MediaEval database. Although these previous results are good in terms of the metrics of the prediction values, they are not good enough to obtain a classification by quadrant based on the valence and arousal values predicted by the neural network, mainly due to the imbalance between classes in the dataset. To achieve better classification values a pre-processing phase was implemented to stratify and balance the dataset. Three different classifiers have been compared: Linear Support Vector Machine (SVM), Random Forest and MLP. The best results are obtained with the MLP. An averaged F-measure of 50% is obtained in a four-quadrant classification schema. Two binary classification approaches are also presented: one vs. rest (OvR) approach in four-quadrants and binary classifier in valence and arousal. The OvR approach has an average F-measure of 69%, and the second one obtained F-measure of 73% and 69% in valence and arousal respectively. Finally, a dynamic classification analysis with different time windows was performed using the temporal annotation data of the MediaEval database. The results obtained show that the classification F-measures in four quadrants are practically constant, regardless of the duration of the time window. Also, this work reflects some limitations related to the characteristics of the dataset, including: size, class balance, quality of the annotations and the sound features available.

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“…Preceding the valence-arousal-dominance (VAD-3D) circumplex model is the valence-arousal (VA-2D) circumplex model [31]. While the VA-2D model has the earliest and widest empirical support in the literature (eg, [31,32]), the VAD-3D model has shown to be more sensitive in the differentiation of certain affective states (eg, fear and anger) compared with the VA-2D model (eg, [29,33]). In general, a circumplex model can be constructed for an individual by assessing affective states after multiple interactions over time.…”

Section: Introductionmentioning

confidence: 99%

Using the COVID-19 Pandemic to Assess the Influence of News Affect on Online Mental Health-Related Search Behavior Across the United States: Integrated Sentiment Analysis and the Circumplex Model of Affect

Lekkas¹,

Gyorda²,

Price³

et al. 2022

J Med Internet Res

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Background The digital era has ushered in an unprecedented volume of readily accessible information, including news coverage of current events. Research has shown that the sentiment of news articles can evoke emotional responses from readers on a daily basis with specific evidence for increased anxiety and depression in response to coverage of the recent COVID-19 pandemic. Given the primacy and relevance of such information exposure, its daily impact on the mental health of the general population within this modality warrants further nuanced investigation. Objective Using the COVID-19 pandemic as a subject-specific example, this work aimed to profile and examine associations between the dynamics of semantic affect in online local news headlines and same-day online mental health term search behavior over time across the United States. Methods Using COVID-19–related news headlines from a database of online news stories in conjunction with mental health–related online search data from Google Trends, this paper first explored the statistical and qualitative affective properties of state-specific COVID-19 news coverage across the United States from January 23, 2020, to October 22, 2020. The resultant operationalizations and findings from the joint application of dictionary-based sentiment analysis and the circumplex theory of affect informed the construction of subsequent hypothesis-driven mixed effects models. Daily state-specific counts of mental health search queries were regressed on circumplex-derived features of semantic affect, time, and state (as a random effect) to model the associations between the dynamics of news affect and search behavior throughout the pandemic. Search terms were also grouped into depression symptoms, anxiety symptoms, and nonspecific depression and anxiety symptoms to model the broad impact of news coverage on mental health. Results Exploratory efforts revealed patterns in day-to-day news headline affect variation across the first 9 months of the pandemic. In addition, circumplex mapping of the most frequently used words in state-specific headlines uncovered time-agnostic similarities and differences across the United States, including the ubiquitous use of negatively valenced and strongly arousing language. Subsequent mixed effects modeling implicated increased consistency in affective tone (SpinVA β=–.207; P<.001) as predictive of increased depression-related search term activity, with emotional language patterns indicative of affective uncontrollability (FluxA β=.221; P<.001) contributing generally to an increase in online mental health search term frequency. Conclusions This study demonstrated promise in applying the circumplex model of affect to written content and provided a practical example for how circumplex theory can be integrated with sentiment analysis techniques to interrogate mental health–related associations. The findings from pandemic-specific news headlines highlighted arousal, flux, and spin as potentially significant affect-based foci for further study. Future efforts may also benefit from more expansive sentiment analysis approaches to more broadly test the practical application and theoretical capabilities of the circumplex model of affect on text-based data.

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Empirical Research on a Fuzzy Model of Music Emotion Classification Based on Pleasure-Arousal Model

Cited by 10 publications

References 5 publications

Effects of the Musical Sound Environment on Communicating Emotion

Effects of the Musical Sound Environment on Communicating Emotion

Emotional classification of music using neural networks with the MediaEval dataset

Using the COVID-19 Pandemic to Assess the Influence of News Affect on Online Mental Health-Related Search Behavior Across the United States: Integrated Sentiment Analysis and the Circumplex Model of Affect

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