Rough Set-Based Approach for Automatic Emotion Classification of Music

doi:10.3745/jips.04.0032

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…In Table 5, MLP with the augmented data performs the best scores of 79.64% and 85.61% in valence and arousal scales, respectively. Similarly, MLP with original data in experiment 1 also performs better result in valence, which is around 6% higher than SOA(1), and 2% higher than SOA (2). But the score in arousal is 3% less than SOA(2) and 2% higher than SOA(1).…”

Section: Discussionmentioning

confidence: 61%

“…The huge number of music can be specified by mood, which can help us to easily retrieve a proper set of music. There are two major aspects for automatically evaluating mood of music; one is mood classification [1,2] and the other is mood regression. In the mood classification the category named by an adjective term is automatically given to a song.…”

Section: Introductionmentioning

confidence: 99%

“…The authors [2,6] concludes that number of musical features like tempo, pitch, rhythm and harmony makes listener perceive and correctly identify a specific intended emotional expressions of mood like sad, fear, humorous, happy, and exiting. The study of lyric-based music mood recognition [7] has been performed to observe the relationship between content of lyrics and mood using OPM songs.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Music Mood Detection Using Transfer Learning and Multilayer Perceptron

Bhattarai

Lee

2019

IJFIS

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This paper proposes an automatic mood detection of music with a composition of transfer learning and multilayer. The five layered convolutional neural network pre-trained on Million Song dataset is used to extract the features from EmoMusic dataset. We obtain a set of features from the different five layers, which is fed into multilayer perceptron (MLP)-based regression. Through the regression network we estimate the mood of music on Thayer's two-dimensional emotion space, which consists of the axes corresponding to arousal and valence. Because the EmoMusic dataset does not provide enough number of data for training, we augment the data by time stretching to make it tripled. We perform the experiment with the augmented data as well as the original EmoMusic dataset. Box and whisker plot along with the mean of 10-fold cross-validation has been used for evaluating the proposed mood detection. In terms of the percentage of R 2 score for measure of accuracy, the proposed MLP shows state-of-the-art estimates for the augmented EmoMusic dataset.

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Section: Discussionmentioning

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Music Mood Detection Using Transfer Learning and Multilayer Perceptron

Bhattarai

Lee

2019

IJFIS

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“…Over the past two decades, affective computing research has intensively studied methods for emotion recognition [33], emotion expression [21][22][23][24] and emotion response [27]. However, only a few studies have looked at 3D emotional contents.…”

Section: Discussionmentioning

confidence: 99%

Comparative analysis of affective and physiological responses to emotional movies

Kim

Cho

Park

2018

Hum. Cent. Comput. Inf. Sci.

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IntroductionThe three-dimensional movies are popular among today's generation. Following the success of the blockbuster movie Avatar, interest in 3D movies has increased significantly, leading to the production of more 3D movies. 3D televisions, 3D broadcasting and 3D Blu-ray movies also hit the market. More recently, virtual reality becomes a popular trend for consumers who want even more immersive 3D experiences. A few months ago Paramount Pictures launches the first virtual reality movie theater.Along with the rising popularity in 3D technologies, the biggest concern of creating 3D contents is to offer deeper and more meaningful experiences. Many works have shown that 3D images help users experience excitement, a feeling of presence, and enjoyment, and even performance advantages for depth-related tasks [1]. Despite the growing interest in 3D contents and technologies, few are concerned about viewers' perceptual and emotional responses to 3D stimuli.Much 3D related research is focused on technical factors, such as the fidelity of stereoscopic depth and reduction of visual discomforts while the viewer is watching 3D [2][3][4]. For example, subjective evaluation was used to compare visual fatigue induced by AbstractIn this research, we investigated on user's affective and physiological responses to the emotional movies. The emotional movies provide continuous feedback such as changes in saturation, brightness and contrast of the movie in response to users' emotional states. In the user study, the subjects watched the emotional movies, edited by fearful and joyful scenes, and presented in 2D and 3D formats. The subject's self-reported emotion responses and their physiological signals were analyzed. The results highlight the importance of scenes (such as, the color, tone, and brightness of the scene) than 2D or 3D format in understanding the impact of users' emotional responses. More physiological emotional changes, positive user responses and higher correlation rate between physiological and subjective responses were evident for joyful scene than fearful scene when presented in 3D format. Kim et al. Hum. Cent. Comput. Inf. Sci. (2018) et al. Hum. Cent. Comput. Inf. Sci. (2018) 8:15 2D and 3D high-definition television (HDTV) [5], but subjective evaluations often show considerable variation between individuals. RESEARCHPage 2 of 17 KimOn the other hand, physiological measurements provide a more objective evaluation for studying visual fatigue induced by a 3D display [6]. For example, the study on influence of 3D TV based on physiological signals measured the viewers' emotional states [7,8]. Results showed a correlation between the effect of physiological responses and emotional states. This indicates that physiological responses can be potentially ample indicators of emotionally engaging 3D video.Human emotion is a complex phenomenon with both affective and physiological components. The affective component refers to the users' subjective feeling while the physiological component refers to the physiological...

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“…Baniya and Lee [14] propose an approach for automatic emotion classification based on the rough set (RS) theory. Since the output after rule generation process is classification rules, this approach can be inherently characterized as a kind of categorical approach in emotion recognition.…”

Section: Related Workmentioning

confidence: 99%

Advances in Algorithm, Multimedia, and Network for Future IT

2017

J Inf Process Syst

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Rough Set-Based Approach for Automatic Emotion Classification of Music

Cited by 10 publications

References 7 publications

Automatic Music Mood Detection Using Transfer Learning and Multilayer Perceptron

Automatic Music Mood Detection Using Transfer Learning and Multilayer Perceptron

Comparative analysis of affective and physiological responses to emotional movies

Advances in Algorithm, Multimedia, and Network for Future IT

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