Music Emotion Recognition Using K-Nearest Neighbors Algorithm

We listen to music every day and feel various emotions from different music, it brings us to the right mood and bonds us to other people and creates a shared experience. Depending on the situation and mental state different people may get affected diversely. However, we can extract common music emotional information even from people with diverse backgrounds and cultures. In this research paper, we develop a system that uses music to retrieve human emotions using the k-Nearest Neighbors (kNN) machine learning algorithm and compare the classifier with others to find the most accurate. How do we define music as happy or sad? Low-level features such as beat, pitch, rhythm, valence and tempo which made up the music, are used in the classification of music emotion. For example, high pitched music or music with a high frequency is usually associated with a happy and energizing state, and the opposite for slow music, which makes us feel sad and calm. The proposed system can be very useful in music-related systems and applications such as music retrieval, recommendation and understanding. Also, various disciplines such as musicology, physiology, psychology and cognitive sciences refer to music emotion recognition.