🎹 Moodify - an emotion-based music recommendation system that uses AI/ML models to analyze text, speech, and facial expressions, providing personalized music recommendations across web and mobile platforms.

This Python-based AI project utilizes OpenCV for facial recognition and a pre-trained deep learning model to analyze facial expressions. By identifying your current mood, the system leverages YouTube's search capabilities to recommend music that aligns with your emotions.

An application which detects our emotions at real time using webcam feed and smartly classifies your playlist into genres, at last playing a song that suits the mood specified by the facial analysis.

A Music-Recommendation Flask web app that recommends music based on our facial expressions using FER-2013 Dataset and Spotify API.

Music Recommendation System Based on Emotion (Python) This Python-based project recommends music by detecting the user's emotions through facial expressions, text, or voice input. Using machine learning models, it suggests songs that align with the user's mood for a personalized listening experience.

A music recommendation system using facial emotion detection. The system captures real-time facial expressions, classifies emotions, and suggests music tracks based on the detected mood. Integrated with the Spotify API for personalized music recommendations. Built with Python, OpenCV, and deep learning models.

Developed an AI-driven music recommendation system that detects facial expressions in real-time and plays music based on the detected emotions. Utilized OpenCV for face detection, FER (Facial Expression Recognition) for emotion classification, and TensorFlow for model training using the FER2013 dataset.

The main objective of this work is to develop an application that reads our facial expressions and recommend music according to our mood.  Whenever a user looks in the camera while using the app, it reads their expressions and suggest songs accordingly. In order to do that  we use various strategies to implement recommendation engine.

An application which detects our emotions at real time using webcam feed and smartly classifies your playlist into genres, at last playing a song that suits the mood specified by the facial analysis. The application provides a pre-trained model for emotion or mood recognition which has been trained on Kaggle's 'Fer2013' dataset.

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Music Recommendation System using emotion by facial expression.

🎹 Moodify - an emotion-based music recommendation system that uses AI/ML models to analyze text, speech, and facial expressions, providing personalized music recommendations across web and mobile platforms.

A music recommendation system using facial emotion detection. The system captures real-time facial expressions, classifies emotions, and suggests music tracks based on the detected mood. Integrated with the Spotify API for personalized music recommendations. Built with Python, OpenCV, and deep learning models.

This project combines deep learning techniques, utilizing Convolutional Neural Networks (CNN) for facial emotion recognition, with a filter-based music recommendation system. The system, implemented in Python using TensorFlow and OpenCV, detects the user's emotional state from their facial expressions and suggests personalized music recommendations

Emotion-Driven Music Recommendations is a app that recommends music based on the detected facial expression of the user. It uses Firebase Cloud Storage to manage and stream music in real-time.

A mood-based music recommendation system built using Flask and Machine Learning. Detects user mood via text, voice, or facial expression and suggests songs from Spotify or YouTube accordingly.

In this project, we presented a model to recommend a music based on the emotion based detected from the facial expression. This project proposed & developed an emotion based music recommendation system using face recognition System.

Well, spotify's recommendations sucks. So I thought why not make it better: MusAIc is an AI-powered music application that bridges emotion recognition with personalized music recommendations. The app detects your mood in real time using facial expressions and then plays songs that match your emotions through the Spotify API.

Moodify is an AI-powered music recommendation app that detects a user's mood through facial expressions and suggests songs accordingly. It captures an image using the webcam, analyzes the mood using a deep learning model, and fetches a matching song from Spotify.

Facial expressions are captured using an inbuilt camera. Feature extraction is performed on input face images to detect emotions such as happy, angry, sad, surprise, and neutral. Automatically music playlist is generated by identifying the current emotion of the user. Convolutional Neural Network is used for emotion detection. For music recommendations, Pygame & Tkinter are used.

Developed a music recommendation system using facial expression recognition (OpenCV & CNN) to analyze user emotions and suggest personalized songs. The project involved training deep learning model to detect emotions, integrating it with an api, and creating a user-friendly interface.

A music recommendation service using facial expression recognition.

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An app that detects our emotions in real time using a webcam feed and intelligently categorizes your playlists into genres

About An application which detects our emotions at real time using webcam feed and smartly classifies your playlist into genres, at last playing a song that suits the mood specified by the facial analysis. The application provides a pre-trained model for emotion or mood recognition which has been trained on Kaggle's 'Fer2013' dataset.

Developed a web application for expressions-based music recommendation using MediaPipe, Keras, OpenCV, and Streamlit-WebRTC in Python. Implemented a model to classify facial expressions in real-time webcam feed.

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