Emotion Based Music Recommendation System

IEEE Transactions on Consumer Electronics, 2012

Recently, many researches of modeling or measuring human feeling have been conducted to understand human emotions. However, researches on music-related human emotions have much difficulty due to the subjective perception of emotions. We selected low-level musical features which may trigger human emotions, based on TV music program's audience rating information and the corresponding music. In this program, audience was requested to rate music of the contestants and to select their preferred music based on their emotional feelings. In addition, we implemented personalized music recommendation system using selected features, context information and listening history. In the experimental results, we confirmed that selected features can be reliable features when these features are used in music recommendation systems. 1

International Journal For Multidisciplinary Research, 2024

An emotions-based music player is a software that detects the emotions of an individual with the help of a webcam and then analyses the mood of an individual and then recommends songs that may improve the mood of the user. Music has been proven to show positive effects on the overall mood of the user if the user is in a bad mood. Hence, we may use music to alleviate the mood of the individual. The system will capture an image of the user with the help of a webcam and then analyze the image for the type of emotion. For example, if the image shows an angry man, then the system will suggest appropriate songs to help improve the current mood of the user. We have included and implemented deep learning. This is a crucial part of our project.

2022

designed using a specific architecture to have clients on both web and mobile devices. So, there is a server-side module that computes sentiments and recommends music, and two clients take images and play the received music. In order to achieve the desired results, the system uses deep learning techniques for sentiment analysis and then a match between the computed sentiment and a list of songs that is proper for it. The system is also complemented with a feedback system which can be further used for improving the sentiment analysis module. It uses two signs: one thumbs up icon for validating the emotion and one thumb down icon for a wrong detected emotion.

Frontiers in Artificial Intelligence and Applications, 2021

Chinese traditional music has been proved to be effective in emotion regulation for thousands of years. Five different groups of Chinese traditional music which have been proved can regulate different emotions (Angry, Depressed, Feverish, Desperate, Sorrowful) in the literature. 54 audios features are extracted by using the Librosa library for each music group. Five features are manually selected using histogram analysis which show significant difference between the five groups of music. Combined with KNN, SVM and Deep forest classification algorithms, the five manually selected audio features are shown to have better classification performance than traditional feature selection algorithms, like PCA and LDA. We hypothesize that these five significant audio features may be the underlying basis why so such music can effectively perform emotion regulation. Based on this classification models, prototype emotion regulation music recommendation interface (TJ-ERMR) was built that can be us...

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.

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022

Music Emotion Recognition (MER) is an interesting research topic in artificial intelligence field for recognizing the emotions from the music. The recognition methods and tools for the music signals are growing fast recently. With recent development of the signal processing, machine learning and algorithm optimization, the recognition accuracy is approaching perfection. In this research we are focused on three different significant parts of MER, that are features, learning methods and music emotion theory, to explain and illustrate how to effectively build MER systems. Numerous music players have been created with capabilities like fast forward, backward, variable playback speed (seek and time compression), local playback, and streaming playback with multicast broadcasts in the modern world due to the rapid improvements in multimedia and technology. Although these capabilities serve the user’s fundamental needs, the user is still required to actively browse through the music playlist and choose songs depending on his present state of mind and behavior. Here we are using tensoflow, mediapipe, cv2 library for training data using the face expressions. After training, model would be able to recognize face and by streamlit library from the expressions by the user it will suggest songs playlist and user would be able to play the song by his/her choice.

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 (ML), where machines are trained to identify various human emotions or moods. Machine Learning provides various techniques through which human emotions can be detected. 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.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

The goal of this project is to develop an emotion-based music recommender system that provides personalized music recommendations based on the user's current emotional state. The system incorporates an emotion detector that analyses user's facial expressions, to determine their current mood. Based on the detected emotion, the system recommends music tracks that match the user's current emotional state.

2021

Music can stimulate emotions within us; hence is often called the “language of emotion.” This study explores emotion as an additional feature in generating a playlist with a deep learning model to improve the current music recommendation system. This study will sample emotions from certain subjects for each song in a sample of the data. Since the effect of music on emotion is subjective and is different person to person, this study would need a considerable number of subjects to reduce subjectivity. Due to the limited resources, a portion of the data will be labeled with emotion from subjects and the rest of the data will be labeled by using an active learning model. A content-based recommendation system will be built using a GAN (Generative Adversarial Network). This research led to creating two recommendation models, one utilizing emotion while the other did not. The Cosine Similarity and the Euclidean Distance where the two metrics used to judge validity of the models. The result...

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.