基于深度特征融合的鸟鸣识别方法及其可解释性分析

doi:10.17520/biods.2023087

生物多样性 ›› 2023, Vol. 31 ›› Issue (7): 23087. DOI: 10.17520/biods.2023087

基于深度特征融合的鸟鸣识别方法及其可解释性分析

蔡建民¹, 何培宇¹, 杨智鹏², 李露莹², 赵启军³, 潘帆¹^,^*()

1.四川大学电子信息学院, 成都 610065
2.成都信息工程大学电子工程学院, 成都 610225
3.四川大学计算机学院, 成都 610065

收稿日期:2023-03-08 接受日期:2023-06-14 出版日期:2023-07-20 发布日期:2023-06-29
通讯作者: *E-mail: panfan@scu.edu.cn
作者简介:*E-mail: panfan@scu.edu.cn
基金资助:
国家自然科学基金(62066042);中央高校基本科研业务费专项资金(2022SCU12008);四川省重点研发项目(2022YFG0045)

A deep feature fusion-based method for bird sound recognition and its interpretability analysis

Jianmin Cai¹, Peiyu He¹, Zhipeng Yang², Luying Li², Qijun Zhao³, Fan Pan¹^,^*()

1. School of Electronic Information, Sichuan University, Chengdu 610065
2. College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225
3. School of Computer Science (School of Software), Sichuan University, Chengdu 610065

Received:2023-03-08 Accepted:2023-06-14 Online:2023-07-20 Published:2023-06-29
Contact: *E-mail: panfan@scu.edu.cn

摘要/Abstract

摘要：

鸟鸣识别是生态监测的重要手段, 为进一步提升鸟鸣识别的准确性和鲁棒性, 本文提出了1种新的基于深度特征融合的鸟鸣识别方法。该方法首先利用深度特征提取网络对鸟鸣的对数梅尔谱图和补充特征集的深度特征进行提取, 再将两种深度特征进行融合, 最后使用轻量级梯度提升机(light gradient boosting machine, lightGBM)分类器进行分类。本文充分利用深度神经网络的特征提取能力以及lightGBM的分类性能, 将特征提取和特征分类过程进行分离, 从而实现了高准确率的鸟鸣识别。实验结果显示, 本文提出的方法在北京百鸟数据集中取得了目前已知的最佳结果, 模型的平均准确率达到了98.70%, 平均F₁分数达到了98.84%。相比传统方法, 深度融合特征在鸟鸣识别任务上准确率提升了5.62%以上。同时, 引入的lightGBM分类器使分类准确率提升了3.02%。此外, 在CLO-43SD和BirdCLEF2022比赛的数据集中, 本文方法也展现出卓越的性能, 分别取得了98.32%和91.12%的平均准确率。本文还引入了类激活图对不同类型鸟鸣的识别结果进行可解释性分析, 揭示了神经网络对不同类型鸟鸣的注意力区域差异, 为后续的特征选择和模型优化提供了理论依据。研究结果表明, 本文方法有效提高了鸟鸣识别的准确率, 在3个数据集的测试中均展现出较好的性能, 能够为基于鸟鸣识别的生态监测提供有力的技术支撑。

关键词: 鸟鸣识别, 特征融合, 可解释性分析, 深度学习, lightGBM

Abstract

Background: Bird sound recognition is a crucial tool for ecological monitoring. However, current research still faces the challenges of achieving low recognition rates in complex datasets and a lack of robustness. Moreover, there is a noticeable absence of interpretability analysis for deep learning model in the existing research.

Methods: Firstly, we utilized a deep feature extraction network to extract features from the logarithmic Mel-spectrogram of bird sound and the deep features of the supplementary feature set. These two types of deep features were then fused and fed into a light gradient boosting machine (lightGBM) classifier for classification. Class activation maps were applied to perform interpretability analysis on deep learning models to understand how the models recognize bird sound.

Results: The experimental results demonstrated that the proposed method in this paper achieved state-of-the-art results on the Beijing Bird Dataset, with an average accuracy of 98.70% and an average F₁ score of 98.84%. Compared to traditional methods, the deep fusion features show a significant improvement in accuracy for bird sound recognition, with an increase of at least 5.62%. Additionally, the introduction of the lightGBM classifier contributed to a 3.02% improvement in classification accuracy. Furthermore, the proposed method exhibited outstanding performance on the CLO-43SD and BirdCLEF2022 competition datasets, achieving average accuracies of 98.32% and 91.12%, respectively. The result of the class activation maps revealed that the disparities in attentional regions within the neural network for each specific bird sound type.

Conclusion: The method proposed in this paper effectively improves the accuracy of bird sound recognition and demonstrates excellent performance on three datasets, offering strong technical support for ecological monitoring based on bird sound recognition. This analysis serves as a theoretical foundation for subsequent endeavors in feature selection and model optimization.

Key words: bird sound recognition, feature fusion, interpretability analysis, deep learning, lightGBM

蔡建民, 何培宇, 杨智鹏, 李露莹, 赵启军, 潘帆 (2023) 基于深度特征融合的鸟鸣识别方法及其可解释性分析. 生物多样性, 31, 23087. DOI: 10.17520/biods.2023087.

Jianmin Cai, Peiyu He, Zhipeng Yang, Luying Li, Qijun Zhao, Fan Pan (2023) A deep feature fusion-based method for bird sound recognition and its interpretability analysis. Biodiversity Science, 31, 23087. DOI: 10.17520/biods.2023087.

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链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2023087

https://www.biodiversity-science.net/CN/Y2023/V31/I7/23087

图/表 8

参考文献 18

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模型 Model	参数名称 Parameter name	参数值 Parameter value
深度特征提取网络 Deep feature extraction network	优化器 Optimizer	Adam
	学习率 Learning rate	0.01
	时期数 Epochs	200
	批大小 Batch size	16
	损失函数 Loss function	分类交叉熵 Categorical_cross-entropy
分类器 Classifier	学习率 Learning rate	0.01
	加速方法 Boosting method	GBDT
	最大深度 Max depth	4

模型 Model	参数名称 Parameter name	参数值 Parameter value
深度特征提取网络 Deep feature extraction network	优化器 Optimizer	Adam
	学习率 Learning rate	0.01
	时期数 Epochs	200
	批大小 Batch size	16
	损失函数 Loss function	分类交叉熵 Categorical_cross-entropy
分类器 Classifier	学习率 Learning rate	0.01
	加速方法 Boosting method	GBDT
	最大深度 Max depth	4

模型 Model	平均准确率 Average accuracy (%)	平均F₁分数 Average F₁-score (%)
logMel + mobileNetV3	90.20	90.11
logMel + EfficientNetV2	93.08	93.20
logMelMAPS + EfficientNetV2	95.69	95.73
EGeMAPS + EfficientNetV2	77.41	77.45
LogEGeMAPS + EfficientNetV2	91.56	91.42
深度logEGeMAPS + lightGBM Deep logEGeMAPS + lightGBM	97.13	97.12
深度logMelMAPS + lightGBM Deep logMelMAPS + lightGBM	98.71	98.69
深度融合特征(mobileNetV3) + lightGBM Deep fusion features (mobileNetV3) + lightGBM	97.77	97.76
深度融合特征 + SVM Deep fusion features + SVM	98.83	98.82
深度融合特征 + Random Forest Deep fusion features + Random Forest	98.82	98.81
深度融合特征 + XGBoost Deep fusion features + XGBoost	98.64	98.63
深度融合特征 + lightGBM Deep fusion features + lightGBM	98.70	98.82

模型 Model	平均准确率 Average accuracy (%)	平均F₁分数 Average F₁-score (%)
logMel + mobileNetV3	90.20	90.11
logMel + EfficientNetV2	93.08	93.20
logMelMAPS + EfficientNetV2	95.69	95.73
EGeMAPS + EfficientNetV2	77.41	77.45
LogEGeMAPS + EfficientNetV2	91.56	91.42
深度logEGeMAPS + lightGBM Deep logEGeMAPS + lightGBM	97.13	97.12
深度logMelMAPS + lightGBM Deep logMelMAPS + lightGBM	98.71	98.69
深度融合特征(mobileNetV3) + lightGBM Deep fusion features (mobileNetV3) + lightGBM	97.77	97.76
深度融合特征 + SVM Deep fusion features + SVM	98.83	98.82
深度融合特征 + Random Forest Deep fusion features + Random Forest	98.82	98.81
深度融合特征 + XGBoost Deep fusion features + XGBoost	98.64	98.63
深度融合特征 + lightGBM Deep fusion features + lightGBM	98.70	98.82

模型 Model	平均准确率 Average accuracy (%)	平均F₁分数 Average F₁-score (%)	参考文献 Reference
GWO-KELM	91.16	88.54	李大鹏, 2022^①① 李大鹏 (2022) 自然场景下鸟鸣声识别算法研究, 硕士学位论文, 南京信息工程大学, 南京.)
LogMel + CRNN	92.89	89.64	Adavanne et al, 2017
LogMel + CNN	91.12	88.47	Bold et al, 2019
logMel + DSRN + DilatedSAM + BiLSTM	96.58	96.51	李大鹏, 2022^①① 李大鹏 (2022) 自然场景下鸟鸣声识别算法研究, 硕士学位论文, 南京信息工程大学, 南京.)
深度融合特征 + lightGBM Deep fusion features + lightGBM	98.70	98.82	本文 This study

基于深度特征融合的鸟鸣识别方法及其可解释性分析

A deep feature fusion-based method for bird sound recognition and its interpretability analysis

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参考文献 18

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模型 Model	平均准确率 Average accuracy (%)	平均F₁分数 Average F₁-score (%)	参考文献 Reference
logMel + SVM	93.96	-	Salamon et al, 2016
深度特征-1-2-3 + 最小最大归一化 + KNN Deep fusion feature-1-2-3 + Max-Min Normalization + KNN	93.89	-	吉训生等, 2022
深度融合特征 + lightGBM Deep fusion features + lightGBM	98.32	98.04	本文 This study

模型 Model	平均准确率 Average accuracy (%)	平均F₁分数 Average F₁-score (%)	参考文献 Reference
BirdCLEF2022比赛public最优 BirdCLEF2022 competition optimal model in public dataset	-	91.28	https://www.kaggle.com/competitions/birdclef-2022/leaderboard?t ab=public
深度logMelMAPS + lightGBM Deep logEGeMAPS + lightGBM	88.53	88.32	本文 This study
深度融合特征 + SVM Deep fusion features + SVM	89.40	89.22	本文 This study
深度融合特征 + Random Forest Deep fusion features + Random Forest	85.47	84.77	本文 This study
深度融合特征 + XGBoost Deep fusion features + XGBoost	85.71	85.47	本文 This study
深度融合特征 + lightGBM Deep fusion features + lightGBM	91.12	91.05	本文 This study

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