A Comparison of Machine Learning Algorithms for Music Genre Recognition

Abstract

Music information retrieval includes the classification of music genres as a key component. However, due to the complexity of music composition, existing artificial classification cannot achieve accurate results. In the music genre classification scenario, machine learning can effectively process large and complex data, bringing more accurate and personalized results for music recommendation. This article will start from three articles and use GTZAN as a data set to compare 3 traditional machine learning methods, including SVM, random forest and logistic regression, together with 2 deep learning models, involving convolutional and feedforward neural networks (CNN and FFNN) in music genres. classification accuracy. The results show that the classification accuracy will be affected by multiple factors such as the characteristics of the audio, whether data processing is performed and so on. In addition, the performance of deep learning has not been significantly better than traditional machine learning as expected. At the same time, the accuracy of CNN and FFNN is heavily dependent on whether the audio is processed into a spectrogram.