Examining the impact of vocal attractiveness on team performance

DeGroot, Timothy; Vâlcea, Sorin; Hamdani, Maria Riaz

doi:10.1007/s12144-021-02642-2

Cited by 1 publication

(1 citation statement)

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“…Psychology, as the theoretical basis of the performer's mental state, fully exerts the role of guidance to help performers overcome tension and relieve psychological pressure. Recently, people's research on psychology has become more and more profound, and more performers have realized the influence of psychology on vocal performance [2][3]. A perfect vocal performance requires not only the support of the performer's vocal skills but also solid psychological support to avoid mistakes.…”

Section: Introductionmentioning

confidence: 99%

Improving Musical Expression by Capturing Psychological Changes with CNN Support

Li,

Shi,

Pan

2024

J Multimed Inf Syst

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In vocal performance, capturing psychological changes through electroencephalography (EEG) and integrating them with music can improve musical expression. We propose an EEG-based emotion recognition model using continuous convolutional neural networks (CNN). EEG signals in different frequency bands are extracted as features using differential entropy. An EEG enhancement method reassigns variable importance to channels by suppressing redundant information. The model is evaluated on the DEAP dataset for emotion recognition. Combining a rest-state baseline signal before each trial significantly improves accuracy. Following the simulation results, the suggested continuous CNN model has an average classification accuracy of 95.36% and 95.31 for arousal and valence on 22 channels, respectively. This is close to the average accuracy of 32 channels. Likewise, it can help capture the psychological changes of vocal performers and improve musical expression.

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Section: Introductionmentioning

confidence: 99%