A Deep Learning Method for Chinese Singer Identification

Zebang Shen; Binbin Yong; Gaofeng Zhang; Rui Zhou; Qingguo Zhou

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A Deep Learning Method for Chinese Singer Identification

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摘要

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 MeI-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-1 K dataset than the traditional approaches.

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《清华大学学报（英文版）》 |2019年第4期|371-378|共8页
作者
Zebang Shen; Binbin Yong; Gaofeng Zhang; Rui Zhou; Qingguo Zhou;
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作者单位

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

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

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

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

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

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A Deep Learning Method for Chinese Singer Identification

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