TY - GEN
T1 - Music Genre Classification with LSTM based on Time and Frequency Domain Features
AU - Yi, Yinhui
AU - Zhu, Xiaohui
AU - Yue, Yong
AU - Wang, Wei
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/23
Y1 - 2021/4/23
N2 - 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.
AB - 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.
KW - Deep features
KW - LSTM
KW - MFCC
KW - Music classification
KW - ZCR
UR - http://www.scopus.com/inward/record.url?scp=85113346224&partnerID=8YFLogxK
U2 - 10.1109/ICCCS52626.2021.9449177
DO - 10.1109/ICCCS52626.2021.9449177
M3 - Conference Proceeding
AN - SCOPUS:85113346224
T3 - 2021 IEEE 6th International Conference on Computer and Communication Systems, ICCCS 2021
SP - 678
EP - 682
BT - 2021 IEEE 6th International Conference on Computer and Communication Systems, ICCCS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Computer and Communication Systems, ICCCS 2021
Y2 - 23 April 2021 through 26 April 2021
ER -