International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024
p-ISSN: 2395-0072
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Enhanced Music Recommendation via Facial Emotion Recognition Using Deep Learning Prajwala Jadhav1,Dr. Ruchita Kale2, Nayla Mansoori3, Sampada Kambe4, Tanaya Wankhede5 1Student, Dept. of CSE Engineering, PRMIT&R college ,Maharashtra, India 2Professor, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India
3Student, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India 4 Student, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India 5 Student, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India
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Abstract - In today's digital age, music recommendation
personalized music streaming venture. Through the analysis of facial expressions, the system discerns the user's emotional responses to different songs, thereby curating playlists or suggesting specific tracks that harmonize with their mood. This innovative approach transcends conventional music recommendation systems, which primarily hinge on user preferences, fostering a more dynamic and tailored musical journey.
systems play a pivotal role in enhancing user satisfaction and engagement by providing personalized playlists tailored to individual preferences. Traditional approaches primarily rely on user input and historical data to generate recommendations, yet they often overlook the dynamic and nuanced nature of human emotions. In this paper, we propose a novel music recommendation system that leverages facial emotion recognition technology to deliver real-time, emotionally intelligent music suggestions. By analyzing facial expressions captured through computer vision algorithms, the system identifies users' current emotional states and maps them to corresponding musical attributes. This fusion of technology allows for a more immersive and responsive music listening experience, catering to the ever-changing emotional landscapes of users. Through a combination of machine learning algorithms and user feedback mechanisms, the system continuously refines its recommendations, ensuring a personalized and emotionally resonant music journey for each user. Additionally, ethical considerations regarding privacy and data usage are addressed to ensure user trust and consent. Overall, our proposed system represents a significant advancement in the field of music recommendation, promising to revolutionize the way users interact with and experience music in their daily lives.
Once the user's emotional state is ascertained, the system interfaces with an extensive music database housing a diverse array of songs tagged with corresponding emotional attributes. By aligning the user's emotional state with the emotional characteristics of songs, the system adeptly recommends music that complements and amplifies the user's current mood. The Music Recommendation System employing Facial Emotion Detection boasts several advantages. It empowers users to unearth new music resonating with their emotions, thereby offering a more immersive and captivating listening experience. Moreover, it aids users in managing and regulating their emotions by suggesting appropriate music tracks aligned with their desired emotional states. Facial emotion recognition technology, on the other hand, offers a unique opportunity to capture and analyze these emotional cues in real-time. By utilizing computer vision algorithms, cameras, or even smartphone sensors, this technology can detect facial expressions such as smiles, frowns, and raised eyebrows, or furrowed brows. These expressions often correlate with underlying emotions, providing valuable insight into the user's current mood or state of mind. Detecting human emotions through facial expressions presents a formidable challenge for machines, particularly under the realm of Machine Learning. However, leveraging Deep Learning for feature extraction, the Music Recommendation System using Facial Emotion Detection emerges as an innovative solution aimed at enriching users' music listening encounters. This system harnesses computer vision methodologies to scrutinize users' facial expressions in real-time, effectively discerning their emotional nuances. By accurately identifying emotions like happiness, sadness,
Key Words: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNNs),Deep Learning ,Haar Cascads ,Emotion Recognition, Facial Expression.
1.INTRODUCTION Music has a profound impact on human emotions, often evoking a wide range of feelings such as joy, sadness, excitement, or relaxation. Traditional recommendation systems typically rely on explicit user input, such as song ratings or genre preferences, to suggest music that aligns with their tastes. While effective to some extent, these methods may overlook the subtle nuances of a user's emotional state at any given moment. The project integrates computer vision, machine learning, and music recommendation algorithms to craft a unique and
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