Music Genre Classification with LSTM based on Time and Frequency Domain Features
Yinhui Yi, Xiaohui Zhu, Yong Yue, Wei Wang
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.