Music Moody - Facial Recognition And Voice Recognition To Detect Mood And Recommend Songs
Volume 6 - Issue 10, October 2023 Edition
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K. Tharmikan, Heisapirashoban.N, M.A. Miqdad Ali Riza, R.R. Stelin Dinoshan, Thusithanjana Thilakarthna
Collaborative Filtering, Music Recommendations, Streaming services, Digital music libraries, Music preferences, Content-based filter algorithms, User-specific, Data preprocessing, Feature engineering, Model training, Matrix factorization algorithm, Latent features, Playlist creation, Mood Detection, Live Voice Recognition, Speech Emotion Recognition, Neural Network, Machine Learning Algorithms, Vocal Features, Emotional State, Song Recommendations, Audio Analysis, Emotional Resonance, Music Recommendation, Facial Emotion Recognition, Personalized Music, Emotional Context, Mood Detection, Emotion-based Recommendations, Stress Reduction, User Engagement, Data-driven Recommendations, Emotional Resonance, Technology Advancements, User Data.
This project aims to develop a comprehensive music recommendation system that provides personalized song suggestions based on the user's individual tastes and current emotional state. The system incorporates four main components: mood detection using live voice recognition techniques, collaborative filtering for playlist generation, multiclassification of songs based on mood, and base and frequency feature extraction. The real-time voice recognition module analyzes the user's voice to extract features like pitch, volume, and tone, which are then used to determine the user's mood state. This information is fed into the mood detection and song recommendation module, which employs a neural network trained on a large dataset of labeled audio recordings to predict the user's mood. Also utilizes collaborative filtering techniques, considering the user's music preferences, listening history, and similarities with other users, to generate personalized song playlists. Additionally, a multiclassification approach using base and frequency features is employed to classify songs into mood categories such as happy, sad, calm, and energetic. This classification allows for better organization and recommendation of songs based on their emotional characteristics. Overall, this project offers a comprehensive approach to personalized music recommendation, leveraging voice recognition, collaborative filtering, and song mood classification to provide users with relevant and enjoyable song suggestions based on their individual tastes and emotional states.
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