Abstract
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.
Highlights
In the advancement of music field, many inventions are happening in the music field to obtain more customers and increase the business revenue by running advertisements
Human feelings detection based on human facial emotions, speech is increasing a days (Balamurugan, 2017; A roulanandam, 2020)
The experimental findings indicate an accuracy of 80% on the outcome, but since human emotions change over time, complex estimation and identification of human emotion is a key feature in music recommendation systems (Abdat, 2011; Garikapati, 2020)
Summary
In the advancement of music field, many inventions are happening in the music field to obtain more customers and increase the business revenue by running advertisements. Since music is connected with listener’s feelings and some researchers state that music is a best solution to resolve mental disorders, sleeping problems, depressions etc (Latchoumi, 2020; Pavan, 2020). The implementation of designing and implementing a content-based music recommendation framework that automatically detects human emotions has a broader reach. This should involve emotion detection, low feature based extraction and interface to connect music recommendation. Human feelings detection based on human facial emotions, speech is increasing a days (Balamurugan, 2017; A roulanandam, 2020). Feeling detection/recognition will play a crucial role in several alternative potential applications like music diversion and human-computer interaction systems
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