Publication: Music footprint recognition via sentiment, identity, and setting identification
dc.contributor.affiliation | DA-IICT, Gandhinagar | |
dc.contributor.author | Phatnani, Kirtana Sunil | |
dc.contributor.author | Patil, Hemant | |
dc.date.accessioned | 2025-08-01T13:09:02Z | |
dc.date.issued | 01-07-2022 | |
dc.description.abstract | Emotional contagion is said to occur when an origin (i.e., any sensory stimuli) emanating emotions causes the observer to feel the same emotions. In this paper, we explore the identification and quantification of emotional contagion produced by music in human beings. We survey 50 subjects who answer: what type of music they hear when they are happy, excited, sad, angry, and affectionate. In the analysis of the distribution, we observe that predominantly the emotional state of the subjects does influence the choice of�tempo�of the musical piece. We define the footprint in three dimensions, namely, sentiment, time, and identification. We unpack each song by unraveling sentiment analysis in time, using lexicons and tenses, along with the identity via pronouns used. In this study, we wish to quantify and visualize the emotional journey of the listener through music. The results of this can be extended to the elicitation of emotional contagion within any story, poem, and conversations. | |
dc.format.extent | 22247-22262 | |
dc.identifier.citation | Kirtana Sunil Phatnani and Patil, Hemant A, "Music footprint recognition via sentiment, identity, and setting identification," In: Multimedia Tools and Applications, IEEE, 02 Jan. 2022. doi: 10.1007/s11042-021-11430-w. | |
dc.identifier.doi | 10.1007/s11042-021-11430-w | |
dc.identifier.issn | 1573-7721 | |
dc.identifier.scopus | 2-s2.0-85122262349 | |
dc.identifier.uri | https://ir.daiict.ac.in/handle/dau.ir/1560 | |
dc.identifier.wos | WOS:000737106100002 | |
dc.language.iso | en | |
dc.publisher | Springer | |
dc.relation.ispartofseries | Vol. 81; No. 16 | |
dc.source | Multimedia Tools and Applications | |
dc.source.uri | https://link.springer.com/article/10.1007/s11042-021-11430-w | |
dc.title | Music footprint recognition via sentiment, identity, and setting identification | |
dspace.entity.type | Publication | |
relation.isAuthorOfPublication | fdb7041b-280e-498b-b2ee-34f9bc351f4c | |
relation.isAuthorOfPublication.latestForDiscovery | fdb7041b-280e-498b-b2ee-34f9bc351f4c |