一种基于改进CRNN的轻量化乐谱识别方法

蒋凌云; 鞠金恒; 徐佳; 肖甫

doi:10.12263/DZXB.20230031

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一种基于改进CRNN的轻量化乐谱识别方法

学术论文 | 更新时间：2025-12-08

- 一种基于改进CRNN的轻量化乐谱识别方法
- A Lightweight Music Recognition Method Based on Improved CRNN
- 电子学报 2023年51卷第11期页码：3167-3175
- 作者机构：
  
  南京邮电大学计算机学院、软件学院、网络空间安全学院，江苏南京 210003
- 作者简介：
  
  [ "蒋凌云女，1978年4月出生，湖南永州人.现为南京邮电大学副教授，硕士生导师.主要研究方向为信息网络与智能信息处理. E-mail: jianglingyun@njupt.edu.cn" ]
  [ "鞠金恒男，1997年8月出生，江苏南京人.现为南京邮电大学硕士.主要研究方向为图像识别与时间序列处理. E-mail: 1558332432@qq.com" ]
  [ "徐佳（通迅作者）男，1980年10月年出生，江苏常州人.现为南京邮电大学教授，博士生导师.主要研究方向为无线充电网络、智能信息处理.中国电子学会会员编号：E190011107M. E-mail: xujia@njupt.edu.cn" ]
  [ "肖甫男，1980年10月出生，湖南省邵阳市.现为南京邮电大学教授，博士生导师.主要研究方向为物联网感知与计算、数据中心网络、智能信息处理.中国电子学会会员编号：E190029628M. E-mail: xiaof@njupt.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62372250);江苏省333高层次人才培养工程项目(BRA2020065)
- DOI：10.12263/DZXB.20230031
  中图分类号： TP391.4
- 收稿：2023-01-06，
  
  修回：2023-08-25，
  
  纸质出版：2023-11-25
- 稿件说明：
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蒋凌云,鞠金恒,徐佳等.一种基于改进CRNN的轻量化乐谱识别方法[J].电子学报,2023,51(11):3167-3175.

JIANG Ling-yun,JU Jin-heng,XU Jia,et al.A Lightweight Music Recognition Method Based on Improved CRNN[J].ACTA ELECTRONICA SINICA,2023,51(11):3167-3175.
蒋凌云,鞠金恒,徐佳等.一种基于改进CRNN的轻量化乐谱识别方法[J].电子学报,2023,51(11):3167-3175. DOI： 10.12263/DZXB.20230031.

JIANG Ling-yun,JU Jin-heng,XU Jia,et al.A Lightweight Music Recognition Method Based on Improved CRNN[J].ACTA ELECTRONICA SINICA,2023,51(11):3167-3175. DOI： 10.12263/DZXB.20230031.

摘要

基于深度学习的乐谱识别方法提高了识别精度，但存在模型训练单次迭代耗时长、总迭代轮数多的问题.本文提出了一种改进卷积循环神经网络的轻量化乐谱识别方法CRNN-lite（lightweight Convolutional Recurrent Neural Networks），该方法在卷积层引入残差式深度可分离卷积，减少计算量并加速特征图的提取；在循环层使用双向简单循环单元，采用并行计算避免了串行计算的强依赖问题；在转录层调节交叉熵函数参数，针对性地学习不均衡样本数据.实验结果表明，该方法提高训练速度，单次迭代耗时为基准网络的43%，在失真图像数据上符号错误率为1.12%，序列错误率为14.5%，错误率指标均优于对比方案.

Abstract

The deep learning-based music score recognition method has improved recognition accuracy

while there is a dilemma of long single iteration time and multiple total iterations in model training. This work proposes CRNN-lite (lightweight Convolutional Recurrent Neural Networks) for music score recognition. CRNN-lite introduced residual depth separable convolution into the convolution layer

which reduced the computation and speeds up the feature map extraction. The bidirectional simple recurrent unit was used in the recurrent layer

and the strong dependence on serial computation was avoided by parallel computation. The parameters of the cross entropy function were adjusted at the transcription layer to learn unbalanced sample data. The results show that the proposed method improves the training speed

the single iteration time is 43% of the benchmark network

the symbol error rate is 1.12% and the sequence error rate is 14.5% on the distorted image data. The error rate indexes are better than the comparison scheme.

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references

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