Affective Music Recommendation System Reflecting the Mood of Input Image

2013 ◽  
Author(s):  
Shoto Sasaki ◽  
Tatsunori Hirai ◽  
Hayato Ohya ◽  
Shigeo Morishima
Keyword(s):  
Recommendation System ◽  
Input Image ◽  
Music Recommendation ◽  
Music Recommendation System

scholarly journals Facial Emotion Based Music Recommendation System using computer vision and machine learning techiniques

2021 ◽  
Vol 12 (2) ◽  
pp. 912-917
Author(s):  
ShanthaShalini. K, Et. al.
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Recommendation System ◽  
Input Image ◽  
Machine Learning Techniques ◽  
Music Recommendation ◽  
Emotion Detection ◽  
Learning Techniques ◽  
Human Emotions ◽  
Music Recommendation System

The face is an important aspect in predicting human emotions and mood. Usually the human emotions are extracted with the use of camera. There are many applications getting developed based on detection of human emotions. Few applications of emotion detection are business notification recommendation, e-learning, mental disorder and depression detection, criminal behaviour detection etc. In this proposed system, we develop a prototype in recommendation of dynamic music recommendation system based on human emotions. Based on each human listening pattern, the songs for each emotions are trained. Integration of feature extraction and machine learning techniques, from the real face the emotion are detected and once the mood is derived from the input image, respective songs for the specific mood would be played to hold the users. In this approach, the application gets connected with human feelings thus giving a personal touch to the users. Therefore our projected system concentrate on identifying the human feelings for developing emotion based music player using computer vision and machine learning techniques. For experimental results, we use openCV for emotion detection and music 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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