Authors :
Pranav Sonawane; Pranil Sonawane; Abhijit More; Ashutosh Munde; Rupali Jadhav
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/3yu5djfn
Scribd :
https://tinyurl.com/5e7rdr72
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR355
Abstract :
It can be very befuddling for people to choose
which music to tune in to from a wide run of alternatives
accessible. Different proposal frameworks have been
made for particular spaces like music, feasting, and
shopping, catering to the user's inclinations. Our
essential objective is to supply music recommendations
that adjust with the user's taste. By analyzing facial
expressions and client feelings, ready to pick up
experiences into their current mental or enthusiastic
state. Music and recordings offer a extraordinary
opportunity to show clients with a huge number of
choices based on their slants and past data. It is well
known that humans make use of facial expressions to
express more clearly what they want to say and the
context in which they meant their words. More than 60
percent of the users believe that at a certain point of time
the number of songs present in their songs library is so
large that they are unable to figure out the song which
they have to play. By developing a recommendation
system, it could assist a user to make a decision
regarding which music one should listen to helping the
user to reduce his/her stress levels. The user would not
have to waste any time in searching or to look up for
songs and the best track matching the user’s mood is
detected, and songs would be shown to the user
according to his/her mood. The image of the user is
captured with the help of a webcam. The user’s picture
is taken and thenas per the mood/emotion of the user an
appropriate song from the playlist of the user is shown
matching the user’s requirement.
Keywords :
Music Recommendation System, Facial Emotion Recognition, Recommendation, User Preferences, Emotional States, UserEngagement.
It can be very befuddling for people to choose
which music to tune in to from a wide run of alternatives
accessible. Different proposal frameworks have been
made for particular spaces like music, feasting, and
shopping, catering to the user's inclinations. Our
essential objective is to supply music recommendations
that adjust with the user's taste. By analyzing facial
expressions and client feelings, ready to pick up
experiences into their current mental or enthusiastic
state. Music and recordings offer a extraordinary
opportunity to show clients with a huge number of
choices based on their slants and past data. It is well
known that humans make use of facial expressions to
express more clearly what they want to say and the
context in which they meant their words. More than 60
percent of the users believe that at a certain point of time
the number of songs present in their songs library is so
large that they are unable to figure out the song which
they have to play. By developing a recommendation
system, it could assist a user to make a decision
regarding which music one should listen to helping the
user to reduce his/her stress levels. The user would not
have to waste any time in searching or to look up for
songs and the best track matching the user’s mood is
detected, and songs would be shown to the user
according to his/her mood. The image of the user is
captured with the help of a webcam. The user’s picture
is taken and thenas per the mood/emotion of the user an
appropriate song from the playlist of the user is shown
matching the user’s requirement.
Keywords :
Music Recommendation System, Facial Emotion Recognition, Recommendation, User Preferences, Emotional States, UserEngagement.
