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Open Access

A Deep Learning Method for Chinese Singer Identification

School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
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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

singer identificationtimbre modelingdeep learninglong short-term memory

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Tsinghua Science and Technology
Volume 24 Issue 4,
August 2019
Pages 371-378
DOI: 10.26599/TST.2018.9010121
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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Received: 12 July 2018
Accepted: 02 September 2018
Published: 07 March 2019
© The author(s) 2019
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