Music Interpretation and Emotion Perception: A Computational and Neurophysiological Investigation
This study investigates emotional expression and perception in music performance using computational and neurophysiological methods. The influence of different performance settings, such as repertoire, diatonic modal etudes, and improvisation, as well as levels of expressiveness, on performers' emotional communication and listeners' reactions is explored. Professional musicians performed various tasks, and emotional annotations were provided by both performers and the audience. Audio analysis revealed that expressive and improvisational performances exhibited unique acoustic features, while emotion analysis showed stronger emotional responses. Neurophysiological measurements indicated greater relaxation in improvisational performances. This multimodal study highlights the significance of expressivity in enhancing emotional communication and audience engagement.
Code (0)
등록된 구현이 없습니다.
Tasks
Emotion RecognitionSimilar Papers 제목 키워드 기반
Neurophysiological Investigation of Context Modulation based on Musical Stimulus
There are numerous studies which suggest that perhaps music is truly the language of emotions. Music seems to have an almost willful, evasive quality, defying simple explanation, and indeed requires deeper neurophysiolog…
Wearable Music2Emotion : Assessing Emotions Induced by AI-Generated Music through Portable EEG-fNIRS Fusion
Emotions critically influence mental health, driving interest in music-based affective computing via neurophysiological signals with Brain-computer Interface techniques. While prior studies leverage music's accessibility…
Affective Music Information Retrieval
Much of the appeal of music lies in its power to convey emotions/moods and to evoke them in listeners. In consequence, the past decade witnessed a growing interest in modeling emotions from musical signals in the music i…
Emotion RecognitionInformation RetrievalMusic Information RetrievalRetrievalPersonalized Dynamic Music Emotion Recognition with Dual-Scale Attention-Based Meta-Learning
Dynamic Music Emotion Recognition (DMER) aims to predict the emotion of different moments in music, playing a crucial role in music information retrieval. The existing DMER methods struggle to capture long-term dependenc…
Emotion RecognitionInformation RetrievalMeta-LearningMusic Emotion Recognition+1Aesthetic Matters in Music Perception for Image Stylization: A Emotion-driven Music-to-Visual Manipulation
Emotional information is essential for enhancing human-computer interaction and deepening image understanding. However, while deep learning has advanced image recognition, the intuitive understanding and precise control …
EEGImage StylizationRhythm