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Ensemble of deep learning, visual and acoustic features for music genre classification
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.
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The goal of this task is to automatically recognize the emotions and themes conveyed in a music recording using machine learning algorithms.
Music is a medium to express emotion. According to literature, music emotion can be quantified continuously as valence and arousal (VA) density distribution on a 2-D space. However, these data are hard to retrieve as they require intense human effort to manually label songs, especially the number of songs become enormous. The goal of this project is to reproduce a model proposed by Chin, Y.-H. et.al (2018), to predict VA density for a new song based on those densities for training songs as well as audio features of both new and training songs. This will help save human labeling effort on new songs in the future. Furthermore, a prototype of content-based music recommender system is built to demonstrate the usability of the algorithm.
Music Recommendation Implementation
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