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    International Journal of Innovative Science and Research Technology
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    It can be very befuddling for people to choose which music to tune in to from a wide run of alternatives accessible. Different proposal frameworks have been made for particular spaces like music, feasting, and shopping, catering to the user's inclinations. Our essential objective is to supply music recommendations that adjust with the user's taste. By analyzing facial expressions and client feelings, ready to pick up experiences into their current mental or enthusiastic state.

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    Music Recommendation Using Facial Emotion Recognition

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    It can be very befuddling for people to choose which music to tune in to from a wide run of alternatives accessible. Different proposal frameworks have been made for particular spaces like music, feasting, and shopping, catering to the user's inclinations. Our essential objective is to supply music recommendations that adjust with the user's taste. By analyzing facial expressions and client feelings, ready to pick up experiences into their current mental or enthusiastic state.

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    © All Rights Reserved
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    6 views4 pages

    Music Recommendation Using Facial Emotion Recognition

    Uploaded by

    International Journal of Innovative Science and Research Technology
    It can be very befuddling for people to choose which music to tune in to from a wide run of alternatives accessible. Different proposal frameworks have been made for particular spaces like music, feasting, and shopping, catering to the user's inclinations. Our essential objective is to supply music recommendations that adjust with the user's taste. By analyzing facial expressions and client feelings, ready to pick up experiences into their current mental or enthusiastic state.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Flag for inappropriate content
    of 4

    Volume 9, Issue 4, April – 2024 International Journal of Innovative Science and Research Technology

    ISSN No:-2456-2165 https://doi.org/10.38124/ijisrt/IJISRT24APR355

    Music Recommendation Using Facial Emotion


    Recognition
    Pranav Sonawane *1, Pranil Sonawane*2, Abhijit More*3, Ashutosh Munde*4 , Rupali Jadhav*5
    *1,2,3,4
    Student, *5 Asst. Professor,
    Department of Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra

    Abstract:- It can be very befuddling for people to choose analyzes facial expressions and generates outputs that are
    which music to tune in to from a wide run of alternatives then integrated with a music dataset to create a customized
    accessible. Different proposal frameworks have been music playlist recommendation model. Facial expressions
    made for particular spaces like music, feasting, and are a primary means through which individuals express their
    shopping, catering to the user's inclinations. Our emotions. Music, on the other hand, has long been
    essential objective is to supply music recommendations recognized for its ability to influence one's mood. Our
    that adjust with the user's taste. By analyzing facial project aims to capture and recognize emotions conveyed
    expressions and client feelings, ready to pick up through facial expressions and provide appropriate song
    experiences into their current mental or enthusiastic recommendations that align with the user's mood, ultimately
    state. Music and recordings offer a extraordinary bringing a sense of calmness and satisfaction. The design
    opportunity to show clients with a huge number of incorporates a music player that employs the web camera
    choices based on their slants and past data. It is well interface available on computing systems to capture human
    known that humans make use of facial expressions to emotions. The software captures the user's image and
    express more clearly what they want to say and the applies image segmentation and processing techniques to
    context in which they meant their words. More than 60 extract facial features and detect the expressed emotion. By
    percent of the users believe that at a certain point of time capturing the user's image, our goal is to uplift their mood
    the number of songs present in their songs library is so by playing songs that match their emotional state. Facial
    large that they are unable to figure out the song which expression recognition has been a timeless and effective
    they have to play. By developing a recommendation method of analyzing and interpreting human expressions.
    system, it could assist a user to make a decision The analysis and interpretation of facial expressions have
    regarding which music one should listen to helping the long been the most effective way for people to understand
    user to reduce his/her stress levels. The user would not and interpret the emotions, thoughts, and feelings conveyed
    have to waste any time in searching or to look up for by others. In certain cases, altering one's mood can help
    songs and the best track matching the user’s mood is overcome situations such as depression and sadness. By
    detected, and songs would be shown to the user employing expression analysis, we can avoid many health
    according to his/her mood. The image of the user is risks and take necessary steps to improve a user's mood.
    captured with the help of a webcam. The user’s picture
    is taken and then as per the mood/emotion of the user an II. LITERATURE SURVEY
    appropriate song from the playlist of the user is shown
    matching the user’s requirement. A. Many studies in recent years have confirmed that people
    feel and respond to music, and that music has an effect
    Keywords:- Music Recommendation System, Facial Emotion on the human brain. In a study examining people's
    Recognition, Recommendation, User Preferences, comments about listening to music, researchers found
    Emotional States, User Engagement. that music plays an important role in linking arousal
    and mood. Two of the most important roles of music
    I. INTRODUCTION are that it can help the listener understand and realize
    himself. Music preferences have been shown to be
    A groundbreaking Music Recommendation System has associated with positive attitudes and mood .
    been developed by our team using facial emotion analysis.
    By combiningemotional context with music preferences, this B. Kabani, Khan, Khan, and Tadvi (2015) introduced a new
    system offers personalized music suggestions that align with music player in an article on music and music published
    the users' feelings. in the International Journal of Engineering Research.
    General Science. The system aims to create a
    Through this innovative approach, we harness the personalized music experience by understanding and
    immense potential of AI to establish an emotional adapting to the user's emotional state. Research can
    connection, thereby enhancing user engagement and delve deeper into the intersection of emotions and music
    satisfaction. The core of our study revolves around a system preferences by exploring ways to increase user
    that utilizes real-time facial expressions of users to gauge satisfaction through music recommendations.
    their mood. We employ an Emotion Detection Model, which

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    Volume 9, Issue 4, April – 2024 International Journal of Innovative Science and Research Technology
    ISSN No:-2456-2165 https://doi.org/10.38124/ijisrt/IJISRT24APR355

    C. Emotion-Based Music Player - Music Player-X Beats". enjoyable and engaging relationship between the user and the
    This indicates that evolution or optimization in emotion- music player.
    based music technology may reveal new features or
    improvements in emotion recognition and integration for D. Consume Emotion Music Dynamics Research:
    greater musical experience. Human face learning Description:
    specifically for face recognition (Hadid et al., 2007): Examine the dynamics of consumer emotions. The
    project aims to find more accurate and suggestive patterns in
    D. Shlok et al. (2017) reported an intelligent music system individual emotional states by ding small-scale connections.
    that combines facial recognition with music recognition.
    This project will explore the combination of facial and IV. PROPOSED SYSTEM
    music preferences to create a complete experience by
    changing the music playlist according to the user's mood. A. Facial Recognition Module
    Change your mind: the powerful musical self (Janssen et The system must use facial recognition technology to
    al., 2012). identify users and allow users to access their personal
    information and personal information. In a facial image
    E. Janssen, Van Den Broek and Westerin (2012) captured from a camera or other imaging device. Use
    Individually powerful music Players contribute to this techniques such as modeling and correlation to improve the
    field as discussed in the journal User Modeling and User quality and consistency of facial images. Use computer
    Adaptive Interaction. This work will focus on the vision algorithms to extract important facial features such as
    development of music that can not only recognize eyes, nose and mouth. Explore deep learning like neural
    emotions, but also change its suggestions in a powerful networks (CNN) for feature extraction. It is based on
    and personal way, thus improving the entire user intelligent algorithms based on facial expression [1]. Teach
    experience. the model to recognize various emotions that can be
    expressed through music, such as happiness, sadness, anger,
    F. Ramanathan et al. (2017) presented smart music in a and surprise. Conduct extensive testing to evaluate the
    study presented at the 2nd International Sustainable accuracy and reliability of facial recognition algorithms. Use
    Solutions Computer Systems and Information metrics to measure performance, including acceptance and
    Technologies Conference. This research will focus on rejectionrates.
    the integration of emotional intelligence in a music
    player, demonstrating the technology's ability to
    personalize music selections based on the user's heart
    needs.

    G. Facial expression and recognition were analyzed based


    on statistics from Londhe and Pawar's (2012) paper
    published in the International Journal of Soft Computing
    and Engineering. Although not directly related to
    musicians, this research can provide insight into the
    statistical methods used for facial analysis; these meth
    ods can affect a wide range of emotional awareness,
    including the ability to use music.

    III. OBJECTIVES

    A. Develop an Emotion-Based Music Player:


    Create music that goes beyond traditional work by
    combining the power that can be felt and tuned to the user's
    emotional state. The goal is to turn passive listening into a
    conversational and emotional experience.

    B. Integrated Facial Recognition: Description:


    Improve the performance of your music by integrating
    the best facial recognition. This allows the system to better
    understand the user's emotional state by analyzing and
    interpreting their facial expressions, thus providing
    personalized recommendations.

    C. Improve User Experience: Description: Fig 1: Facial Recognition Module


    Focus on optimizing the overall user experience using
    imaginative features. Customized music feedback and a
    responsive interface are designed to create a seamless,

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    Volume 9, Issue 4, April – 2024 International Journal of Innovative Science and Research Technology
    ISSN No:-2456-2165 https://doi.org/10.38124/ijisrt/IJISRT24APR355

    B. Music Recommendation Engine: VI. RESULTS AND DISCUSSION


    The system should integrate the best recommendations
    that take into account the user's preferences, listening history A. Facial Emotion Recognition Accuracy
    and face to create a beautiful personal expression .Music The accuracy of the facial emotion recognition module
    recommendation engine is a system designed to analyze was evaluated using a diverse dataset of facial images. The
    users' preferences, behavior, and music interaction hi story model achieved a high level of accuracy, with an average
    in order to provide personalized and relevant music recognition rate exceeding 90% across different emotions
    recommendations. These engines use various algorithms [3]. This indicates the robustness of the emotion recognition
    and techniques to understand the user's taste, find patterns, algorithm in accurately detecting users' emotional states
    and recommend music that suits the person's taste. based on their facial expressions.

    C. Data Collection for Facial Emotion Recognition: B. Music Recommendation Effectiveness


    Acquire a diverse dataset of facial images depicting The effectiveness of the music recommendation system
    various emotions, sourced from publicly available databases was assessed through user studies and online experiments.
    and possibly supplemented with in-house data collection. Participants were presented with music recommendations
    generated by the system based on their detected emotional
    Ensure proper annotation of facial expressions to states. Feedback from users indicated a high level of
    facilitate supervised learning. Implement ethical guidelines satisfaction with the recommended music, with many
    and obtain necessary permissions for the use of human expressing that the suggested songs aligned well with their
    facial data, ensuring anonymity and consent. Preprocessing current moods and preferences.
    and Feature Extraction: Preprocess facial images to
    standardize size, lighting conditions, and alignment for VII. CONCLUSION
    consistency. Utilize established techniques such as
    histogram equalization and facial landmark detection to In conclusion, the developed music recommendation
    enhance image quality and extract relevant facial features. system leveraging facial emotion recognition successfully
    Explore different feature representations, including but not personalized music suggestions based on users' emotional
    limited to facial landmarks, texture descriptors, and deep states. The high accuracy of the emotion recognition
    features extracted via convolutional neural networks (CNNs) module, coupled with positive user feedback and increased
    [5]. engagement metrics, underscores the effectiveness of the
    proposed approach. By aligning music recommendations
    V. SYSTEM DESIGN with users' current emotional states, the system enhances
    user satisfaction and interaction, offering a compelling
    solution for navigating the vast array of music options
    available. This research paves the way for further
    exploration and implementation of emotion-aware
    recommendation systems in various domains, catering to
    individual preferences and fostering enriched user
    experiences

    REFERENCES

    [1]. H. Kabani, S. Khan, O. Khan, and S. Tadvi,


    "Emotion based music player," International Journal
    of Engineering Research and General Science, vol. 3,
    pp. 750-756, 2015.
    [2]. A. Gupte, A. Naganarayanan, and M. Krishnan,
    "Emotion Based Music Player-XBeats," International
    Journal of Advanced Engineering Research and
    Science, 2015
    [3]. A. Hadid, M. Pietikäinen, and S. Z. Li, "Learning
    personal specific facial dynamics for face recognition
    from videos," in International Workshop on Analysis
    and Modeling of Faces and Gestures, Springer Berlin
    Heidelberg, 2007, pp. 1-15.

    Fig 2: System Design.

    IJISRT24APR355 www.ijisrt.com 276


    Volume 9, Issue 4, April – 2024 International Journal of Innovative Science and Research Technology
    ISSN No:-2456-2165 https://doi.org/10.38124/ijisrt/IJISRT24APR355

    [4]. Z. Zeng, M. Pantic, G. I. Roisman, and T. S. Huang,


    "A survey of affect recognition methods: Audio,
    visual, and spontaneous," 2008. [5] P. Tambe, Y.
    Bagadia, T. Khalil, and N. U. A. Shaikh, "Advanced
    Music Player with Integrated Face Recognition
    Mechanism," International Journal of Advanced
    Research in Computer Science and Software
    Engineering, 2015.
    [5]. G. Shlok et al., "Smart music player integrating facial
    emotion recognition and music mood
    recommendation," in 2017 International Conference on
    Wireless Communications, Signal Processing and
    Networking (WiSPNET), IEEE, 2017. [7] J. H.
    [6]. Janssen, E. L. Van Den Broek, and J. H. D. M.
    Westerink, "Tune in to your emotions: a robust
    personalized affective music player," User Modeling
    and User-Adapted Interaction, vol. 22, no. 3, pp. 255-
    279, 2012.
    [7]. R. Ramanathan et al., "An intelligent music player
    based on emotion recognition," in 2017 2nd
    International Conference on Computational Systems
    and Information Technology for Sustainable Solution
    (CSITSS), IEEE, 2017.
    [8]. R. R. Londhe and D. V. Pawar, "Analysis of facial
    expression and recognition based on statistical
    approach," International Journal of Soft Computing and
    Engineering, 2012.

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