moodSing is a music recommendation app that suggests music suited to the weather around the user as well as their current mood and preferred genre. These features bypass the need for users to wander and search for music that fits their mood and helps introduce the users to new music.
Front End
- Katie Dickson
- Ismahan Jamea
Back End
- Jacob Cowan - Git Master
- Andrew Crow - Project Manager
- Robert Dalton
Thank you to the developers of the following APIs for making moodSing possible!
And thank you to the researchers who provided us with the information necessary to map moods to music! (See Citations Below)
https://docs.google.com/spreadsheets/d/1P7G5RYoqyl5em3w8AbFFX_33M3S1rd1eRJU5-pTjDdA/edit?usp=sharing
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Warriner, A.B., Kuperman, V. & Brysbaert, M. Norms of valence, arousal, and dominance for 13,915 English lemmas. Behav Res 45, 1191–1207 (2013). https://doi.org/10.3758/s13428-012-0314-x
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Castillo, Susana & Wallraven, Christian & Cunningham, Douglas. (2014). The semantic space for facial communication. Computer Animation and Virtual Worlds. 25. 10.1002/cav.1593. https://www.researchgate.net/figure/Two-dimensional-circumplex-space-model-and-its-emotional-samples-plotted-on-the_fig3_50805681
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Schuller, B., Dorfner, J. & Rigoll, G. Determination of Nonprototypical Valence and Arousal in Popular Music: Features and Performances. J AUDIO SPEECH MUSIC PROC. 2010, 735854 (2010). https://doi.org/10.1155/2010/735854