Analyzing the Music Perception Based on Physiological Signals

Anuharshini, K; Sivaranjani, M; Sowmiya, M.; Mahesh, V.; Geethanjali, B.

doi:10.1109/icaccs.2019.8728546

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Using existing literature, we will investigate effects and outcomes of sad music that are otherwise unexplained by the existing classification and will consequently posit novel sad states. We will consider the valence and arousal of different states, given their relevance to emotion literature (Russell, 1980) and implication in producing varied outcomes when listening to sad music (Anuharshini et al, 2019).…”

Section: Statesmentioning

confidence: 99%

Analysis and Classification of Music-Induced States of Sadness

2022

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The enjoyment and pleasure derived from sad music has sparked fascination among researchers due to its seemingly paradoxical nature in producing positive affect. Research is yet to develop a comprehensive understanding of this “paradox.” Contradictory findings have resulted in a great variability within the literature, meaning results and interpretations can be difficult to derive. Consequently, this review collated the current literature, seeking to utilize the variability in the findings to propose a model of differential sad states, providing a means for past and future findings to be interpreted. The proposed model is based on theoretical understanding, as such it requires full empirical support. Comparisons to alternative models, theoretical, clinical, and cognitive implications, as well as future directions are discussed.

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Section: Statesmentioning

confidence: 99%

Analysis and Classification of Music-Induced States of Sadness

2022

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“…Besides a lot of applications of the ECG and HRV in the clinical domain and research, they have been used to analyse how music with different emotional character affects the heart [ [29] , [30] , [31] ], and some of these studies have been carried out with both musicians [ 32 ] as non-musicians [ 29 ]. Although the effects of music on heart behaviour have been studied, research has not established the best way of audio stimulation [ 18 ] and because until now it is still unclear which musical element is related to the observed changes, it is required to develop systematic high-quality research on the effects of music on the heart [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Judgement of valence of musical sounds by hand and by heart, a machine learning paradigm for reading the heart

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Heliyon

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The intention of the experiment is to investigate whether different sounds have influence on heart signal features in the situation the observer is judging the different sounds as positive or negative. As the heart is under (para)sympathetic control of the nervous system this experiment could give information about the processing of sound stimuli beyond the conscious processing of the subject. As the nature of the influence on the heart signal is not known these signals are to be analysed with AI/machine learning techniques. Heart rate variability (HRV) is a variable derived from the R-R interval peaks of electrocardiogram which exposes the interplay between the sympathetic and parasympathetic nervous system. In addition to its uses as a diagnostic tool and an active part in the clinic and research domain, the HRV has been used to study the effects of sound and music on the heart response; among others, it was observed that heart rate is higher in response to exciting music compared with tranquilizing music while heart rate variability and its low-frequency and high-frequency power are reduced. Nevertheless, it is still unclear which musical element is related to the observed changes. Thus, this study assesses the effects of harmonic intervals and noise stimuli on the heart response by using machine learning. The results show that noises and harmonic intervals change heart activity in a distinct way; e.g., the ratio between the axis of the ellipse fitted in the Poincaré plot increased between harmonic intervals and noise exposition. Moreover, the frequency content of the stimuli produces different heart responses, both with noise and harmonic intervals. In the case of harmonic intervals, it is also interesting to note how the effect of consonance quality could be found in the heart response.

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Analyzing the Music Perception Based on Physiological Signals

Cited by 2 publications

References 9 publications

Analysis and Classification of Music-Induced States of Sadness

Analysis and Classification of Music-Induced States of Sadness

Judgement of valence of musical sounds by hand and by heart, a machine learning paradigm for reading the heart

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