[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117365-en":3,"doc-seo-117365-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117365,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Emotion Based Music Recommendation System Using Machine Learning and AI","Music influences and reflects human emotions, yet conventional recommendation systems typically ignore the listener’s changing emotional state, reducing personalization and engagement. This work proposes an emotion-based music recommendation system that applies AI and machine learning to detect user emotions in real time using facial expression analysis and natural language processing. A recommendation algorithm matches detected emotions to suitable tracks from a music database, improving recommendation accuracy and user satisfaction versus standard methods. Future work includes multi-modal emotion detection expansion and real-time user feedback exploration.","Emotion Based Music Recommendation System Using Machine Learning and AI  \nParag Pardhi1, Sakshi Deshmukh2, Dr. Suman Sen Gupta3  \n1,2School of Science, G H Raisoni University, Amravati, Maharashtra, India 3Assistant Professor, G H Raisoni University, Amravati, Maharashtra, India  \n ABSTRACT  How to cite this paper: Parag Pardhi | Sakshi Deshmukh | Dr. Suman Sen Gupta \"Emotion Based Music Recommendation System Using Machine Learning and AI\" Published in  \nInternational Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-5, October 2024, pp.329-336, URL:  \n[www.ijtsrd.com/papers/ijtsrd69367.pdf](www.ijtsrd.com/papers/ijtsrd69367.pdf)  \nCopyright © 2024 by author (s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article  \nMusic plays a significant role in influencing and reflecting human  \nemotions. Traditional music recommendation systems, however,  \noften fail to consider the listener's emotional state, leading to less  \npersonalized user experiences. An emotion-based music  \nrecommendation system that leverages artificial intelligence (AI) and  \nmachine learning (ML) techniques to identify and respond to user  \nemotions. The system utilizes facial expression analysis and natural  \nlanguage processing to detect emotions in real-time. A  \nrecommendation algorithm then matches these emotions with  \nappropriate music tracks, drawing from a diverse music database.  \nExperimental results demonstrate that the emotion-based  \nrecommendation system significantly improves the accuracy of  \nrecommendations and user satisfaction compared to standard  \nrecommendation methods. The findings suggest that incorporating  \nemotional context into music recommendation systems can enhance  \npersonalization and user engagement. Future research directions  \ninclude expanding the system's emotion detection capabilities  \nthrough multi-modal input and exploring real-time user feedback for  \ndistributed under the  \nterms of the Creative Commons Attribution License (CC BY 4.0)([http://creativecommons.org/licenses/by/4.0](http://creativecommons.org/licenses/by/4.0))  \nThe project will commence with data collection from various  \nsources, including APIs from platforms like Spotify and Genius, to  \ngather song metadata, lyrics, and audio characteristics. We will  \nemploy advanced NLP techniques to analyze sentiment and  \ndynamic adjustments.  \ncategorize songs into emotions such as happiness, sadness, energy,  \nKEYWORDS: Emotion recognition, music recommendation, AI, machine learning, facial expression analysis, NLP, personalization, sentiment analysis, real-time detection  \nI. INTRODUCTION  \nMusic is a universal language with the profound ability to evoke and influence human emotions, serving as a companion during various emotional states such as joy, sadness, excitement, or relaxation. With the proliferation of music streaming platforms, personalized music recommendation systems have become an integral part of the user experience. These systems use various algorithms to suggest songs based on user preferences, listening history, or genre popularity. While they have achieved significant success in delivering tailored recommendations, they often fail to account for the listener's ever-changing emotional states, resulting in a less immersive and engaging user experience.  \nTraditional music recommendation systems primarily rely on static user profiles, collaborative filtering, and content-based filtering, focusing on factors like genre, artist, tempo, and user ratings. However, music consumption is a dynamic process, heavily influenced by the listener's current mood and emotional context. For example, a user may prefer upbeat, fast-tempo music while feeling energetic but may seek softer, slower tunes when feeling melancholic. Ignoring these emotional variations can limit the accuracy and effectiveness of music recommendations. Therefore, there is a gro","cbCaif3KaGMvoSH9","https://ap.wps.com/l/cbCaif3KaGMvoSH9","pdf",1125557,1,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Music, emotions, and personalization gaps\n## Emotion-based recommendation as a solution\n# Emotion Recognition Techniques\n## Modalities: facial expressions, voice, physiological signals, and text sentiment\n# Proposed System Overview\n## Data collection and NLP-based sentiment analysis","[{\"question\":\"What problem does the emotion-based music recommendation system address?\",\"answer\":\"It addresses the limitation of traditional music recommenders that rely on static preferences and fail to account for a listener’s real-time emotional state, leading to less immersive experiences.\"},{\"question\":\"How does the system detect a user’s emotions in real time?\",\"answer\":\"It uses facial expression analysis and natural language processing to identify emotions as they occur during user interaction.\"},{\"question\":\"How are detected emotions used to generate music recommendations?\",\"answer\":\"A recommendation algorithm matches the detected emotions to appropriate music tracks sourced from a diverse music database.\"}]","Emotion Based Music Recommendation System Using Machine Learning and AI | 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problem does the emotion-based music recommendation system address?","Question",{"text":74,"@type":75},"It addresses the limitation of traditional music recommenders that rely on static preferences and fail to account for a listener’s real-time emotional state, leading to less immersive experiences.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the system detect a user’s emotions in real time?",{"text":79,"@type":75},"It uses facial expression analysis and natural language processing to identify emotions as they occur during user interaction.",{"name":81,"@type":72,"acceptedAnswer":82},"How are detected emotions used to generate music recommendations?",{"text":83,"@type":75},"A recommendation algorithm matches the detected emotions to appropriate music tracks sourced from a diverse music 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