Emotion Based Music Recommendation System

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.

International Journal for Research in Applied Science and Engineering Technology

International Journal of Computer Applications

Human face expressions directly express what's going on inside a person. Detecting someone's emotions is not a difficult task for a human but for a machine it can be difficult. Also when it comes to sear a song according to our mood, is very difficult and confusing task. So, keeping in mind here we have designed a model which can easily detect what's going on inside a human and recommend him few songs according to his mood using facial expressions. On the basis of the prediction of emotions, the goal is to play the song that best fits the mood reflected by our expression. The major task in the paper involves the detection of human face, extract the features of face and detect emotion, predict the emotions of new face, and play song on the basis of that emotion.

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.

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, 2022

A user's emotion or mood can be detected by his/her facial expressions. These expressions can be derived from the live feed via the system's camera. A lot of research is being conducted in the field of Computer Vision and Machine Learning, where machines are trained to identify various human emotions or moods. Machine Learning provides various techniques through which human emotions can be detected. Music is a great connector. Music players and other streaming apps have a high demand as these apps can be used anytime, anywhere and can be combined with daily activities, traveling, sports, etc. People often use music as a means of mood regulation, specifically to change a bad mood, increase energy level or reduce tension. Also, listening to the right kind of music at the right time may improve mental health. Thus, human emotions have a strong relationship with music. In our proposed system, a mood-based music player is created which performs real time mood detection and suggests songs as per detected mood. The objective of this system is to analyze the user's image, predict the expression of the user and suggest songs suitable to detect mood.

International Journal of Computer Science and Mobile Computing, 2021

Visual sentiment analysis, which investigates humans' emotional responses to visual stimuli such as images and videos, has been a fascinating and challenging problem. It attempts to recognize the high-level content of visual data. The success of current models can be attributed to the development of robust computer vision algorithms. The majority of existing models attempt to solve the problem by recommending either robust features or more complex models. The main proposed inputs are visual features from the entire image or video. Local areas have received little attention, which we believe is important to the emotional response of humans to the entire image. Image recognition is used to find people in images and analyze their sentiments or emotions. The CNN algorithm is used in this project to accomplish this task. Given an image, it will search for faces, identify them, place a rectangle in their positions, and describe the emotion found with a percentage of emotions displayed. The emotion output will be in audio format. After detecting emotions based on predicted emotions, songs stored in the system is automatically played, such as a happy song when the emotion is happy and a sad song when the emotion is sad.

IRJET, 2023

This research paper presents a development of a web-application with a facial recognition system that uses computer vision, algorithms and machine learning approaches to effectively determine a user’s emotions in real-time. The system interprets facial features such as the eyes, mouth, and forehead to detect emotions, including happiness, sadness, neutral, and rock. Based on the detected emotion and the selected language and singer, the system recommends songs that best fit the user’s mood and preferences. To train the deep learning model, FER-2013 dataset of labeled facial images is used. The system is implemented in real- time video input, providing personalized recommendations to the user based on their mood. The proposed system has the potential to revolutionize the way we listen to music and enhance our wellbeing. By providing personalized recommendations based on the user’s emotions and preferences, the system could improve the user’s music listening experience and mood. To evaluate the system’s performance we conducted experiments using a dataset of labeled facial images. The results showed that the system accurately detects emotions, with an average accuracy of 81conducted a user study to evaluate the system’s effectiveness in providing personalized recommendations. The results showed that the system was successful in providing recommendations that matched the user’s mood and preferences

In the evolving landscape of music recommendation systems, the ability to tailor playlists based on user emotions presents a novel approach to enhancing user experience. This review paper explores the intersection of emotion recognition and music personalization, focusing on the utilization of facial expression analysis as a pivotal tool for discerning emotional states. We examine various methodologies employed in emotion detection, including machine learning techniques and computer vision algorithms, and their effectiveness in interpreting facial cues. Additionally, the paper reviews existing frameworks for integrating emotional data into music recommendation systems, highlighting the challenges and opportunities presented by this innovative approach. Through a comprehensive analysis of current research and practical applications, we discuss the implications of emotion-driven playlists on user engagement, satisfaction, and the overall impact on the music industry. Ultimately, this review aims to provide insights into the future of personalized music experiences, advocating for the adoption of emotion-aware technologies in enhancing the dynamic relationship between users and music.

IRJET, 2020

Recent studies ensure that humans respond and react to music which music incorporates a high impact on person brain activity. The common yank listens up to four hours of music daily. Individuals tend to concentrate on music that supported their mood and interests. This project focuses on making Associate in Nursing application to counsel songs for user-supported their mood by capturing facial expressions. Facial features could be a type of nonverbal communication. Pc vision is Associate in Nursing knowledge domain field that helps convey a high-level understanding of digital pictures or videos to computers. In this system, PC vision elements square measure accustomed to verify the user's feeling through facial expressions. Once the feeling is recognized, the system suggests a play-list for that feeling, saving a great deal of your time for a user over choosing and taking part in songs manually. Emotion-Based Music Player additionally keeps track of user's details like the number of plays for every song, types of songs supported class and interest level, and reorganizes the play-list on every occasion. The system additionally notifies user concerning the songs that square measure so that they will be deleted or changed. Listening to music affects the human brain activities. Feeling based mostly music player with the machine-driven listing will facilitate users to take care of a selected emotional state. This analysis proposes Associate in Nursing feeling based mostly music player that creates playlists supported captured photos of the user. This enhances the system's potency, faster and automatic. The most goal is to cut back the general machine time and also the cost of the designed system. It additionally aims at increasing the accuracy of the system. The foremost vital goal is to create an amendment to the mood of a person if it's a negative one like unhappy, depressed. This model is valid by testing the system against user-dependent and user freelance dataset. The methodology of finding this downside is to make a completely useful app (Front End and Back finish) that solves this downside, ranging from the face there a straightforward and understandable interface anyone will use, this interface is connected to the rear finish. On the back finish, the most rule during this project is to make a Convolutional Neural Network help within the goal of achieving high accuracy rate, as a result of the Convolutional Neural Network is the best within the science of building any network that works with pictures, and conjointly their area unit plenty of similar analysis papers that achieved o.k. seen success during this field of analysis. A completely useful app that engineered to unravel this downside (Desktop Only) and conjointly trained nearly 28000 pictures with totally different states of emotions (Happy, Sad, Angry, and Normal) with an high accuracy rate that is, "85%" for coaching and "83%" for testing rate, the application is with success suggesting music by suggesting single songs that match any user's feeling.