Abstract
We propose a new approach for playing music automatically using facial emotion. Most of the existing approaches involve playing music manually, using wearable computing devices, or classifying based on audio features. Instead, we propose to change the manual sorting and playing. We have used a Convolutional Neural Network for emotion detection. For music recommendations, Pygame & Tkinter are used. Our proposed system tends to reduce the computational time involved in obtaining the results and the overall cost of the designed system, thereby increasing the system’s overall accuracy. Testing of the system is done on the FER2013 dataset. Facial expressions are captured using an inbuilt camera. Feature extraction is performed on input face images to detect emotions such as happy, angry, sad, surprise, and neutral. Automatically music playlist is generated by identifying the current emotion of the user. It yields better performance in terms of computational time, as compared to the algorithm in the existing literature.
Highlights
Many of the studies in recent years admit that humans reply and react to music and this music has a high impression on the activity of the human brain
# Inserting Songs into Playlist for track in songtracks: self.playlist.insert (END, track). This will result in the recommended playlist for the user in the GUI of the music player by showing captions according to detected emotions
We evaluated a number of the studies which use support vector machine (SVM), extreme learning machine (ELM), and convolutional neural network [12]
Summary
Many of the studies in recent years admit that humans reply and react to music and this music has a high impression on the activity of the human brain. Interaction between individuals may be a major aspect of lifestyle It reveals perfect details and much of data among humans, whether they are in the form of body language, speech, facial expression, or emotions [3]. With the increase in technology for digital signal processing and other effective feature extraction algorithms, automated emotion detection in multimedia attributes like music or movies is growing rapidly and this system can play an important role in many potential applications like human-computer interaction systems and music entertainment. The proposed system detects the emotions of a person, if the person has a negative emotion, a certain playlist will be shown that includes the most related types of music that will enhance his mood. Implementation of facial emotion detection is performed using Convolutional Neural Network which gives approximately 95.14% of accuracy [2]
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