Emotion Based Music Recommendation System Project
In this Blog, we follow a new approach to recommend song according to mood of person and it is determined from his picture and based on person mood predicted song recommendations are made best suited. Song Recommendation have existed from a long time but in most of the scenarios the recommendation of song is determined by learning the user preferences over a period of time like looking of past song preferences, time he listens to the music.
Project Description of Song Recommendation System Based on Face Emotion :-
The first process of the model is to take input of user image with the help of python library for Computer Vision called ‘OpenCV’. This will captured image then available for CNN in combination with DNN to make prediction of current mood of user like ‘Happy’ or ‘Sad’.
2.The second step is the usage of Unsupervised Machine Learning Techniques for clustering songs. The song was clustered in either two classes- (class 0) ‘Very Entertaining’ and ‘Relaxed’ (class 1) using k-means algorithm. Then the recommendation is made in order of current popularity of respective songs.
The Final Task is to connect the Machine Learning model with Graphical User Interface that used for Tkinter. Tkinter is python in-built library for developing User Interface.
Important Points for Music Recommendation System:-
- Dataset is used from Kaggle website, here is from https://www.kaggle.com/yamaerenay/spotify-dataset-19212020-160k-tracks.
- This dataset contains more than 160k songs are collected from Spotify Web API, and we can also find data filtered by genre, artist, year in data section.
- It contain features like energy, loudness, causticness and danceability which make clustering algorithm work more effectively.
Library Used:-
OpenCV, Keras, Sklearn, Tensorflow, Spotify, LightGBM, Tkinter, Spotify, Pillow
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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