TY - GEN
T1 - MCRN
T2 - 27th International Conference on Neural Information Processing, ICONIP 2020
AU - Mao, Yuxu
AU - Zhong, Guoqiang
AU - Wang, Haizhen
AU - Huang, Kaizhu
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Music classification and recommendation have received wide-spread attention in recent years. However, content-based deep music classification approaches are still very rare. Meanwhile, existing music recommendation systems generally rely on collaborative filtering. Unfortunately, this method has serious cold start problem. In this paper, we propose a simple yet effective convolutional neural network named MCRN (short for music classification and recommendation network), for learning the audio content features of music, and facilitating music classification and recommendation. Concretely, to extract the content features of music, the audio is converted into “spectrograms” by Fourier transform. MCRN can effectively extract music content features from the spectrograms. Experimental results show that MCRN outperforms other compared models on music classification and recommendation tasks, demonstrating its superiority over previous approaches.
AB - Music classification and recommendation have received wide-spread attention in recent years. However, content-based deep music classification approaches are still very rare. Meanwhile, existing music recommendation systems generally rely on collaborative filtering. Unfortunately, this method has serious cold start problem. In this paper, we propose a simple yet effective convolutional neural network named MCRN (short for music classification and recommendation network), for learning the audio content features of music, and facilitating music classification and recommendation. Concretely, to extract the content features of music, the audio is converted into “spectrograms” by Fourier transform. MCRN can effectively extract music content features from the spectrograms. Experimental results show that MCRN outperforms other compared models on music classification and recommendation tasks, demonstrating its superiority over previous approaches.
KW - Convolutional neural networks
KW - Information retrieval
KW - Music classification and recommendation
KW - Music spectrogram dataset
UR - http://www.scopus.com/inward/record.url?scp=85097298849&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-63820-7_88
DO - 10.1007/978-3-030-63820-7_88
M3 - Conference Proceeding
AN - SCOPUS:85097298849
SN - 9783030638191
T3 - Communications in Computer and Information Science
SP - 771
EP - 779
BT - Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
A2 - Yang, Haiqin
A2 - Pasupa, Kitsuchart
A2 - Leung, Andrew Chi-Sing
A2 - Kwok, James T.
A2 - Chan, Jonathan H.
A2 - King, Irwin
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 18 November 2020 through 22 November 2020
ER -