Melomaniac : Emotion Based Music Recommendation System

International Journal for Research in Applied Science and Engineering Technology (IJRASET), 2022

Traditional methods of playing music according to a person's mood required human interaction. Migrating to computer vision technology will enable the automation of the such system. This article describes the implementation details of a real-time facial feature extraction and emotion recognition system. One way to do this is to compare selected facial features from an image to a face database. Recognizing emotions from images has become one of the active research topics in image processing and applications based on human-computer interaction. The expression on the face is detected using a convolutional neural network (CNN) for classifying human emotions from dynamic facial expressions in real time. The FER dataset is utilized to train the model which contains approximately 30,000 facial RGB images of different expressions. Expression-based music players aim to scan and interpret data and build playlists accordingly. It will suggest a playlist of songs based on your facial expressions. This is an additional feature of the existing feature of a music player.

International Journal for Research in Applied Science and Engineering Technology

Journal of Informatics Electrical and Electronics Engineering (JIEEE), A2Z Journals, 2021

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.

IRJET, 2021

Recent studies confirm that humans respond and react to music which music encompasses a high impact on person's brain activity. the common American listens up to four hours of music on a daily basis. Everyone wants to pay attention music of their individual taste, mostly supported their mood. Users always face the task of manually browsing the music and to form a playlist supported their current mood. The proposed project is incredibly efficient which generates a music playlist supported this mood of users. Facial expressions are the most effective way of expressing ongoing mood of the person. the target of this project is to suggest songs for users supported their mood by capturing facial expressions. Facial expressions are captured through webcam and such expressions are fed into learning algorithm which provides most probable emotion. Once the emotion is recognized, the system suggests a playlist for that emotion, thus saves plenty of your time for a user. Once the emotion is detected by CNN then the emotion is employed by Spotify API then the Spotify API generates a playlist according the emotion of the user. stock prices accurately. The experiment results show that prediction accuracy is over 80%.

IRJET, 2021

Facial expression is one of the most difficult and highly convoluted procedures that have been undertaken in the image processing paradigm. Facial expression can be used for other purposes such as recognizing a person's mood as humans convey most of their emotions through their facial expressions. Identification of a person's mood is one of the most useful implementations as it can be used in various applications to improve the quality of life for an individual. Therefore, for this purpose, there has been an extensive analysis of the related works in this survey article for the purpose of reaching our approach for song recommendation through the mood analysis of the individual. Our prescribed approach utilizes convolutional neural networks along with fuzzy classification for effective song recommendation. This approach will be expanded in future research articles on this topic.

International Journal of Advance Research Ideas and Innovations in Technology

One of the most essential components of an individual's body is the human face and it acts as the main indicator for the behavioural and the emotional state of the individual face and it's very important for the human to extracting the required input from the human face can be done by using the camera directly. The mean of this examination is making Facial Expression Recognition (FER) conspire by Utilizing the CNN Algorithm and tensor flow to recognize the face by the camera. Facial expression analysis is used in a different way to detect human emotions. There are four types of emotions are recognized: happy, sad, angry, neutral depends on the mood. The playlist itself have the songs in the database, it plays the songs according to the mood detect by the Camera. This research paper is effective because we are using the different algorithm i.e. CNN model which is based on Machine Learning which gives accuracy and reduces the time to recognize the emotions.

2021

It is said that health is wealth. Here, health refers to both physical health and mental health. People take various measures to take care of their physical health but ignore their mental health which can lead to depression and even diseases like diabetes mellitus and so on. Emotion detection can help us to diagnose our mental health status. Therefore, this paper proposes a theory for emotion detection and then a recommendation of a song to enhance the user's mood by using the features provided by deep learning and image processing. Here, convolutional neural network-based (CNN) LeNet architecture has been used for emotion detection. The KDEF dataset is used for feeding input to the CNN model and then training it. The model has been trained for detecting the emotion. After training the model, a training accuracy of 98.03% and a validation accuracy of 97.96% have been achieved for correctly recognizing the seven different emotions, that is, sad, disgust, happy, afraid, neutral, angry and surprise through facial expressions.

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021

Every individual human might have completely different faces; however, their expressions tell us the same story and it notably plays a significant role in extraction of an individual’s emotions and behavior. Music is the purest form of art and a medium of expression, which is known to have a greater connection with a person’s emotions. It has a novel ability to lift one’s mood. This project system focuses on building an efficient music player which works on emotion of user using facial recognition techniques. The facial features extracted will generate a system thereby reducing the effort and time involved in doing it manually. Facial data is captured by employing a camera. The emotion module makes use of deep learning techniques to spot the exact mood relative to that expression. The accuracy of mood detection module in the system for real time footage is above 80%; while for static pictures it is 95 to one hundred percent. Therefore, it brings out higher accuracy relating to time and performance.

IJARCCE

The development of a Music Recommendation System involved the utilization of the FER-2013 and Age, Gender (Facial Data) datasets. The system utilizes the CNN architecture, commonly employed for such purposes, to train three separate models: Emotion, Gender, and Age. To enhance the models' performance, additional layers are incorporated into the training phase. These models are subsequently employed as classifiers. To predict the user's mood, age, and gender, a snapshot of the user captured through the camera is forwarded to the trained models. Based on the outcomes of these classifiers, various playlists sourced from a database are suggested to the user. The goal is to create a functional and user-friendly environment for music selection. Once the playlists are proposed, the user can select their desired playlist and begin listening to the recommended music.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

The study of human emotional responses to visual stimuli such as photos and films, known as visual sentiment analysis, has proven a fascinating and difficult problem. It attempts to comprehend the high-level information of visual data. The development of strong algorithms from computer vision is responsible for the present models' success. The majority of existing models attempt to overcome the problem by recommending either robust features or more sophisticated models. Visual elements from the entire image or video are the key proposed inputs in particular. Local areas have received less attention, which we believe is important to the emotional reaction of humans to the entire image. Image recognition is used to locate people in photos, analyse their emotions, and play emotion-related tunes based on their feelings. This repo accomplishes this goal by utilising the Google platform's Vision services. Given an image, it would look for faces, identify them, draw a rectangle around them, and describe the emotion it discovered.