热带蛙群落音频记录中的存在-不存在估计
Presence-absence estimation in audio recordings of tropical frog communities
摘要：
研究青蛙群落的一种非侵入性方法是通过分析含有叫声的声音材料的长期样本。通过开发机器学习工具来提取生态信息，这项艰巨的任务得到了优化。我们探索了一种基于高斯混合模型分类的10种青蛙的似然比音频检测器，并将其应用于估计存在-不存在的音频记录，从实际的两栖动物监测在亚苏尼国家公园在厄瓜多尔亚马逊。一个修改后的滤波器组被用来提取20个倒谱特征，模拟青蛙呼叫的频谱内容。实验研究了训练准确GMM分类器所需的超参数和最小蛙鸣时间。使用64个高斯和12秒的训练时间，分类器在9个物种分类的10倍交叉验证上实现了0.9%的平均加权出错率，而MFCC为3%，PLP为1.8%。为了进行测试，10个Gestival使用所有可用的训练验证数据集进行训练，以研究2001年在两个青蛙社区使用模拟设备记录的141个10分钟长的未识别真实世界音频样本中的23.5小时。为了评估自动存在-不存在估计，我们用10个二进制变量表征音频样本，每个二进制变量对应于青蛙物种，并使用耳机手动标记18个样本的子集。该算法的查全率为87.5%，查准率为100%，平均准确率为96.66%，表明该算法具有良好的泛化能力，并证明了该方法对热带声学环境中真实世界音频记录的有效性。最后，我们将该算法应用于现有的语料库，并显示其潜力，以获得洞察的时间繁殖行为的青蛙。
英文摘要：
One non-invasive way to study frog communities is by analyzing long-term samples of acoustic material containing calls. This immense task has been optimized by the development of Machine Learning tools to extract ecological information. We explored a likelihood-ratio audio detector based on Gaussian mixture model classification of 10 frog species, and applied it to estimate presence-absence in audio recordings from an actual amphibian monitoring performed at Yasuní National Park in the Ecuadorian Amazonia. A modified filter-bank was used to extract 20 cepstral features that model the spectral content of frog calls. Experiments were carried out to investigate the hyperparameters and the minimum frog-call time needed to train an accurate GMM classifier. With 64 Gaussians and 12 seconds of training time, the classifier achieved an average weighted error rate of 0.9% on the 10-fold cross-validation for nine species classification, as compared to 3% with MFCC and 1.8% with PLP features. For testing, 10 GMMs were trained using all the available training-validation dataset to study 23.5 hours in 141, 10-minute long samples of unidentified real-world audio recorded at two frog communities in 2001 with analog equipment. To evaluate automatic presence-absence estimation, we characterized the audio samples with 10 binary variables each corresponding to a frog species, and manually labeled a sub-set of 18 samples using headphones. A recall of 87.5% and precision of 100% with average accuracy of 96.66% suggests good generalization ability of the algorithm, and provides evidence of the validity of this approach to study real-world audio recorded in a tropical acoustic environment. Finally, we applied the algorithm to the available corpus, and show its potentiality to gain insights into the temporal reproductive behavior of frogs.
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