Music Emotion Recognition

Abstract

Music Emotion Recognition (MER) is an interesting research topic in artificial intelligence field for recognizing the emotions from the music. The recognition methods and tools for the music signals are growing fast recently. With recent development of the signal processing, machine learning and algorithm optimization, the recognition accuracy is approaching perfection. In this research we are focused on three different significant parts of MER, that are features, learning methods and music emotion theory, to explain and illustrate how to effectively build MER systems. Numerous music players have been created with capabilities like fast forward, backward, variable playback speed (seek and time compression), local playback, and streaming playback with multicast broadcasts in the modern world due to the rapid improvements in multimedia and technology. Although these capabilities serve the user’s fundamental needs, the user is still required to actively browse through the music playlist and choose songs depending on his present state of mind and behavior. Here we are using tensoflow, mediapipe, cv2 library for training data using the face expressions. After training, model would be able to recognize face and by streamlit library from the expressions by the user it will suggest songs playlist and user would be able to play the song by his/her choice.