Intelligent Music Style Classification System Based on K-Nearest Neighbor Algorithm and Artificial Neural Network

Nowadays, open network resources allow people to freely search and enjoy all kinds of music products. Music classification plays an important role in music information retrieval. It can quickly find users' favorite music varieties in massive network resources and recommend them to enjoy. The music style classification system selects the most popular pop music, uses Python library for audio processing, and uses GTZAN data set to find out the similarity of different music and classify it based on the K-nearest neighbor algorithm (KNN) and artificial neural network (ANN), so as to form ten different styles. Machine learning and problem identification become the key, which is also the innovation of this project: in addition to the original network database of the system, users can upload their favorite or adapted and original music sound to the system. The system automatically identifies the music style label and can be incorporated into the database to establish their favorite “song list. “Innovation and development is the theme of the times. Technology is blurring the boundary between music consumers and creators. I hope that the development of this system can make more people love music, not only as music consumers, but also music creators who can realize their own innovative ideas. If so, people's creativity will have an exhibition stage combined with entertainment, the innovative spirit will be held high, and the music cultures will prosper and bloom in its various varieties. The display of creative talent and the cultivation of innovative spirit are the ultimate goal of the system.