On Modelling Emotional Responses to Rhythm Features

Cu, Jocelynn; Cabredo, Rafael; Legaspi, Roberto; Suarez, Merlin Teodosia

doi:10.1007/978-3-642-32695-0_85

Cited by 3 publications

(5 citation statements)

References 2 publications

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“…For studies of rhythmically responsive motor areas, Grahn and Brett (2007) suggested that basal ganglia and SMAs may mediate rhythmic perception outside of motor creation. Some scientists have also used rhythmic features to create a Thayers-based model of emotion to investigate the association between emotion and rhythm (Cu et al, 2012). Böck et al (2016) provided a then state-of-the-art method for extracting combined beats and low-tempo rhythms from audio sources.…”

Section: Rhythmmentioning

confidence: 99%

A Literature Review on Music Parameter Extraction and Visualization

Jin,

Zairul,

Salih

2024

IJARBSS

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Music visualization research is extremely complex and dynamic. Several researchers have applied various methods to persevere in the study of all aspects that make up music. The complexity of music also includes factors such as waveform, frequency, pitch, rhythm, tempo, timbre, and chords. Researchers in recent years have studied the extraction of single elements, visualization, or cross-discipline for these aspects. As far as the current research is concerned, most of the disciplines related to music visualization are focused on computers, psychology, sports science, and other related disciplines. Research on the elements of music itself has focused on music visualization, music element extraction, music association, music emotion, and the study of several important aspects of music, such as waveform, frequency, pitch, rhythm, tempo, timbre, and chord. After reviewing the research, this paper has found that with the continuous development of science and technology, music visualization has a progressive intersection with computer science, artificial intelligence, and neural networks. Thus, future research can continue to interact more with computer science.

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Section: Rhythmmentioning

confidence: 99%

A Literature Review on Music Parameter Extraction and Visualization

Jin,

Zairul,

Salih

2024

IJARBSS

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“…Although pitch [77] and timbre [78] have been employed to recognize emotion in music, the role of rhythm seems more rudimentary. In fact, the rhythmic features [26] had been used in modeling the emotional responses and acquired a reasonable performance. An all-encompassing representation of the rhythm may be the temporal organization of sound, tightly connected with meter, which refers to the periodic composition in music [23].…”

Section: Musical Features Employed In the Modelmentioning

confidence: 99%

“…Initially, the psychological music models are many [2,, but the physiological music models are rare [3]. Secondly, the psychological music models [2, and physiological musical experiments [1,111] are many, but studies of the psychological rhythm model [26] and the physiological rhythmic experiment [112] are rare. Thirdly, almost all studies concern the responses during the music, but discussions about the responses after the music are limited [113].…”

Section: Advantages Of the Proposedmentioning

confidence: 99%

“…Hence valence is negatively correlated to . Figure 4(b) shows that [26,31,45] and [26,27,31,45] dominate arousal and [27] and [27] dominate valence in the perceived models. The results of arousal for the perceived models are the same as the psychological experiments.…”

Section: Comparison Of the Modelsmentioning

confidence: 99%

“…Thirdly, the felt emotion [28,35] is infrequently examined compared to the perceived emotion. Finally, most works employ a wide range of acoustic features, while the interest in a single feature moves from pitch [40,44] to timbre [28] and rhythm [26]. For the emotion space, Russell [13] proposed the valence/arousal space and Thayer [14] reduced it to four labels; the following synonyms are commonly used: valence as pleasantness [38]; arousal as activity [36,45]; tension [29,32,36,40,44] as interest [43], expectancy [40], strength [38], potency [38], and resonance [27].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Musical Rhythms Affect Heart Rate Variability: Algorithm and Models

Wang

Huang

2014

Advances in Electrical Engineering

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There were a lot of psychological music experiments and models but there were few psychological rhythm experiments and models. There were a lot of physiological music experiments but there were few physiological music models. There were few physiological rhythm experiments but there was no physiological rhythm model. We proposed a physiological rhythm model to fill this gap. Twenty-two participants, 4 drum loops as stimuli, and electrocardiogram (ECG) were employed in this work. We designed an algorithm to map tempo, complexity, and energy into two heart rate variability (HRV) measures, the standard deviation of normal-to-normal heartbeats (SDNN) and the ratio of low- and high-frequency powers (LF/HF); these two measures form the physiological valence/arousal plane. There were four major findings. Initially, simple and loud rhythms enhanced arousal. Secondly, the removal of fast and loud rhythms decreased arousal. Thirdly, fast rhythms increased valence. Finally, the removal of fast and quiet rhythms increased valence. Our work extended the psychological model to the physiological model and deepened the musical model into the rhythmic model. Moreover, this model could be the rules of automatic music generating systems.

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