Music Genre Classification with LSTM based on Time and Frequency Domain Features

Abstract

Deep features generated from deep learning models contain more information for music classification than short-term features. This paper uses a long-short term memory (LSTM) model to generate deep features and achieve music genre classification. Firstly, two short-term features of Zero crossing rate (ZCR) and mel-frequency spectral coefficients (MFCC) are extracted from music in digital form, which is a time-domain feature and frequency-domain feature, respectively. Then these two features are fed to LSTM to generate deep features. Finally, we use support vector machine (SVM) and k-nearest neighbors (KNN) respectively to classify the music genre based on these deep features. Experimental results show that using LSTM can significantly increase the accuracy of music genre classification.