Meloform:Emotion or Weather Based Music Recommendation System
Angel Maria Jose ∗1, Aparna Rajesh ∗2,Kavyamol P S ∗3, Nitika Rose Jacks ∗4,Rakhi Ramachandran Nair ∗5
College Of Engineering,Kidangoor
Kottayam, Kerala, India
angelmariaj70@gmail.com∗1 , lamnotaparna@gmail.com∗2 , kavyamolps29@gmail.com∗3 , nitikarosejacks314@gmail.com∗4 , rakhinairtvla@gmail.com∗5
ABSTRACT:The present research proposes a novel mu- sic recommendation approach that considers the mood of the user or the weather to enhance the listening experience. The system proposed in this work has two major aspects: emotion recognition and weather assessment. By analyz- ing the facial expressions of the user, it can determine the user’s mood now and suggest songs accordingly. Addition- ally, it employs actual weather data to adjust playlists ac- cording to the mood of the day. For instance, on a sunny day, the system can suggest energetic and cheerful songs, and on a rainy day it can suggest more soothing and re- flective songs. The inside-outside solution with regard to music choice takes into account the user’s mood as well as ambient weather. The system offers a blend of mood and weather recommendations, thus enhancing the individual’s experience of the environment as well as their internal af- fective states,leading to a more dynamic and richer experi- ence of music. This new approach makes music an infinitely more effective tool for expression of the emotions and com- munication with the world, which, unexpectedly, boosts the daily experiences and overall well-being of the listener. In this way, it has the potential to transform our use of music because it is set to become an integral and dynamic part of our daily lives. Music can have a great influence on emo- tions and even behaviors. A well-crafted playlist can capture a person’s mood, maintain concentration at the appropriate times, or successfully combat stress after a long day.
INDEX TERMS: Music Recommendation System, Emotion-Based Music, Weather Detection, Mood-Based Playlist, Adaptive Recommendations, User-Centered Music, Real-Time Emotion Analysis
