Authors :
Veda Yashas P; Madhuneela N R; Ganashree S M; Dr. H P Mohan Kumar
Volume/Issue :
Volume 8 - 2023, Issue 6 - June
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/5yt4vft5
DOI :
https://doi.org/10.5281/zenodo.8042843
Abstract :
Songs have always been a popular medium
for communicating and understanding human
emotions. Reliable emotion-based categorization
systems can be quite helpful to us in understanding
their relevance. However, the results of the study on
motion-based music classification have not been the
greatest. Here, we introduce EMP, a cross-platform
emotional music player that recommends songs based
on the user's feelings at the time. EMP provides
intelligent mood-based music suggestions by
incorporating emotion context reasoning abilities into
our adaptive music recommendation engine. Our music
player is composed of three modules: the emotion
module, the random music player module, and the
queue-based module. The Emotion Module analyses a
picture of the user's face and uses the CNN algorithm to
detect their mood with an accuracy of more than 95%.
The Music Classification Module gets an outstanding
result by utilizing aural criteria while classifying music
into 4 different mood groups. The recommendation
module suggests music to users by comparing their
feelings to the mood type of the song. taking the user's
preferences into account.
Keywords :
CNN .
Songs have always been a popular medium
for communicating and understanding human
emotions. Reliable emotion-based categorization
systems can be quite helpful to us in understanding
their relevance. However, the results of the study on
motion-based music classification have not been the
greatest. Here, we introduce EMP, a cross-platform
emotional music player that recommends songs based
on the user's feelings at the time. EMP provides
intelligent mood-based music suggestions by
incorporating emotion context reasoning abilities into
our adaptive music recommendation engine. Our music
player is composed of three modules: the emotion
module, the random music player module, and the
queue-based module. The Emotion Module analyses a
picture of the user's face and uses the CNN algorithm to
detect their mood with an accuracy of more than 95%.
The Music Classification Module gets an outstanding
result by utilizing aural criteria while classifying music
into 4 different mood groups. The recommendation
module suggests music to users by comparing their
feelings to the mood type of the song. taking the user's
preferences into account.
