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Tsinghua Science and Technology 2019, 24(4): 371-378 https://doi.org/10.26599/TST.2018.9010121
Open Access | Issue | Published: 07 March 2019

A Deep Learning Method for Chinese Singer Identification

Show Author's Information Zebang Shen,  Binbin Yong,  Gaofeng Zhang,  Rui Zhou,  Qingguo Zhou( )
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
Keywords:
deep learning, singer identification, timbre modeling, long short-term memory
Cite this article:
Shen Z, Yong B, Zhang G, et al. A Deep Learning Method for Chinese Singer Identification. Tsinghua Science and Technology, 2019, 24(4): 371-378. https://doi.org/10.26599/TST.2018.9010121
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Abstract Full text About this article

As a subfield of Multimedia Information Retrieval (MIR), Singer IDentification (SID) is still in the research phase. On one hand, SID cannot easily achieve high accuracy because the singing voice is difficult to model and always disturbed by the background instrumental music. On the other hand, the performance of conventional machine learning methods is limited by the scale of the training dataset. This study proposes a new deep learning approach based on Long Short-Term Memory (LSTM) and Mel-Frequency Cepstral Coefficient (MFCC) features to identify the singer of a song in large datasets. The results of this study indicate that LSTM can be used to build a representation of the relationships between different MFCC frames. The experimental results show that the proposed method achieves better accuracy for Chinese SID in the MIR-1K dataset than the traditional approaches.


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A Deep Learning Method for Chinese Singer Identification

Show Author's information Zebang Shen, Binbin Yong, Gaofeng Zhang, Rui Zhou, Qingguo Zhou( )
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.

Abstract

As a subfield of Multimedia Information Retrieval (MIR), Singer IDentification (SID) is still in the research phase. On one hand, SID cannot easily achieve high accuracy because the singing voice is difficult to model and always disturbed by the background instrumental music. On the other hand, the performance of conventional machine learning methods is limited by the scale of the training dataset. This study proposes a new deep learning approach based on Long Short-Term Memory (LSTM) and Mel-Frequency Cepstral Coefficient (MFCC) features to identify the singer of a song in large datasets. The results of this study indicate that LSTM can be used to build a representation of the relationships between different MFCC frames. The experimental results show that the proposed method achieves better accuracy for Chinese SID in the MIR-1K dataset than the traditional approaches.

Keywords: deep learning, singer identification, timbre modeling, long short-term memory

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Received: 12 July 2018
Accepted: 02 September 2018
Published: 07 March 2019
Issue date: August 2019

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© The author(s) 2019

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61402210 and 60973137), the Program for New Century Excellent Talents in University (No. NCET-12-0250), the Major Project of High Resolution Earth Observation System (No. 30-Y20A34-9010-15/17), the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA03030100), the Gansu Sci. & Tech. Program (Nos. 1104GKCA049, 1204GKCA061, and 1304GKCA018), the Fundamental Research Funds for the Central Universities (No. lzujbky-2016-140), and Google Research Awards and Google Faculty Research Awards, China. We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Jetson TX1 used for this research.

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