Comparison of Multiple Machine Learning Algorithms for Music Genre Classification

Abstract

With the fast advance of the Internet and the continuous improvement of computer technology, speech recognition has been applied in many fields, and speech recognition has broad prospects for development. Music and audio classification technology can add category labels to music based on music content, which is of great significance in the research and application of efficient organization, retrieval and recommendation of music resources. In order to efficiently classify audio from massive online music data and help users to obtain the most suitable music style, a deep learning classification algorithm based on convolutional neural network (CNN) is proposed. To examine its effectiveness, it is compared with traditional machine learning algorithm. First, the original music data set was preprocessed and then feature extraction was carried out to obtain music features and transform them into spectral maps. Traditional machine learning model and deep learning component model were used for simulation experiments. The testing accuracy of the deep learning model is up to 92%, verifying the model's superiority.