Music Recommendation System Based on Facial Emotion Recognition

2020 ◽  
Vol 17 (4) ◽  
pp. 1662-1665
Author(s):  
S. Visnu Dharsini ◽  
B. Balaji ◽  
K. S. Kirubha Hari ◽  
Sridharshini
Keyword(s):  
Video Processing ◽  
Recommendation System ◽  
Market Potential ◽  
Facial Emotion ◽  
Music Recommendation ◽  
Technological Advances ◽  
Digital Video Processing ◽  
Cost Efficient ◽  
Unique Ability ◽  
Music Recommendation System

Face recognition technology has widely attracted attention due to its enormous application value and market potential. It is being implemented in various fields like security system, digital video processing and many such technological advances. Additionally, music is the form of art, which is known to have a greater connection with a person’s emotion. It has got an unique ability to lift up one’s mood. Relatively, this paper focuses on building an efficient music recommendation system which determines the emotion of user using Facial Recognition techniques. The algorithm implemented would prove to be more proficient than the existing systems. Moreover, on a larger dimension, this would render salvage of time and labor invested in performing the process manually. The overall concept of the system is to recognize facial emotion and recommend songs efficiently. The proposed system will be both time and cost efficient.

Expression X: Emotion Based Music Recommendation System

2020 ◽  
Vol 17 (9) ◽  
pp. 3958-3963
Author(s):  
M. V. Manoj Kumar ◽  
B. S. Prashanth ◽  
Syeda Sarah ◽  
Md. Kashif ◽  
H. R. Sneha
Keyword(s):  
Facial Expression ◽  
Success Rate ◽  
Recommendation System ◽  
Facial Emotion ◽  
Music Recommendation ◽  
Mobile Vision ◽  
Music Recommendation System

This Paper proposes method to recommend a set of songs based on the facial emotion state of the user. Emotion state of the user is detected with the help of google mobile vision SDK. The detected emotion state is fed to Expression-X algorithm that would sort the music (based on emotion value is keyed in) and generates a playlist which suites the emotion state of user. Since emotions are calculated based on the facial expression of a user, achieving 100% accuracy is undoubtedly hard as everyone has their own way of expressing emotions facially, with repetitive testing we have achieved 70–75% success rate in detecting the rite emotion state of the user, and generating the suitable set of song recommendation.

scholarly journals Neural Collaborative For Music Recommendation System

2021 ◽  
Vol 1071 (1) ◽  
pp. 012021
Author(s):  
Abba Suganda Girsang ◽  
Antoni Wibowo ◽  
Jason ◽  
Roslynlia
Keyword(s):  
Recommendation System ◽  
Music Recommendation ◽  
Music Recommendation System

Song Recommendations Based on Artists with Cosine Similarity Algorithms and K-Nearest Neighbor

2020 ◽  
Vol 8 (4) ◽  
pp. 367
Author(s):  
Muhammad Arief Budiman ◽  
Gst. Ayu Vida Mastrika Giri
Keyword(s):  
Collaborative Filtering ◽  
Mobile Phones ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
Cosine Similarity ◽  
Music Recommendation ◽  
K Nearest Neighbor ◽  
Filtering Method ◽  
K Nearest Neighbor Algorithm ◽  
Music Recommendation System

The development of the music industry is currently growing rapidly, millions of music works continue to be issued by various music artists. As for the technologies also follows these developments, examples are mobile phones applications that have music subscription services, namely Spotify, Joox, GrooveShark, and others. Application-based services are increasingly in demand by users for streaming music, free or paid. In this paper, a music recommendation system is proposed, which the system itself can recommend songs based on the similarity of the artist that the user likes or has heard. This research uses Collaborative Filtering method with Cosine Similarity and K-Nearest Neighbor algorithm. From this research, a system that can recommend songs based on artists who are related to one another is generated.

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