CityNet-Deep learning tools for urban ecoacoustic assessment
CityNet-Deep learning tools for urban ecoacoustic assessment
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DOI：
10.1111/2041-210x.13114
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发表时间：
2019-02-01
影响因子：
6.6
通讯作者：
Jones, Kate E.
中科院分区：
文献类型：
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作者：
Fairbrass, Alison J.;Firman, Michael;Jones, Kate E.
城市支持独特而有价值的生态社区，但由于评估生物多样性的困难，对城市野生动物的了解有限。生态声学调查是评估生境的一种有用方法，从录音中测得的生物声音被用作种群丰度和/或活动的代表。然而，现有的算法系统地超过和低估了生物活动的措施，在录音中存在典型的城市非生物的声音。我们开发了CityNet，这是一种使用卷积神经网络（CNN）的深度学习系统，用于测量城市中的可听生物（CityBioNet）和人为（CityAnthroNet）声学活动。CNN是在英国大伦敦收集的注释音频记录的大型数据集上训练的。使用一个测试数据集，我们将CityBioNet和CityAnthroNet的精确度和召回率分别与最好的替代算法进行比较：四个声学指数：声学复杂性指数，声学多样性指数，生物声学指数和标准化差异声景指数，以及最先进的鸟鸣检测CNN（bulbul）。我们还比较了非生物声音对CityBioNet和白头翁预测的影响。最后，我们应用城市网络来描述城市声景观的声学模式在两个网站沿着城市化梯度。CityBioNet在精确度和召回率方面是测量生物活性的最佳算法，其次是bulbul，而Acoustic Indices表现最差。CityAnthroNet的表现优于标准化差异声景指数，但比CityBioNet与竞争算法相比的优势要小。CityBioNet的预测受到机械声音的影响，而空中交通和风的声音影响了白头翁的预测。在整个城市化梯度，我们表明，CityNet从现实世界的城市音频数据中产生了现实的日常模式的生物和人为的声学活动。使用CityNet，可以从音频记录中自动测量城市中的生物和人为声学活动。如果嵌入一个自主传感系统，城市网可以产生大规模的城市环境数据，并促进调查人类活动对野生动物的影响。这些算法、代码和预训练模型与两个专家注释的城市音频数据集相结合，可免费提供，以促进城市的自动化环境监测。
Cities support unique and valuable ecological communities, but understanding urban wildlife is limited due to the difficulties of assessing biodiversity. Ecoacoustic surveying is a useful way of assessing habitats, where biotic sound measured from audio recordings is used as a proxy for population abundance and/or activity. However, existing algorithms systematically over and underestimate measures of biotic activity in the presence of typical urban non-biotic sounds in recordings. We develop CityNet, a deep learning system using convolutional neural networks (CNNs), to measure audible biotic (CityBioNet) and anthropogenic (CityAnthroNet) acoustic activity in cities. The CNNs were trained on a large dataset of annotated audio recordings collected across Greater London, UK. Using a held-out test dataset, we compare the precision and recall of CityBioNet and CityAnthroNet separately to the best available alternative algorithms: four Acoustic Indices: Acoustic Complexity Index, Acoustic Diversity Index, Bioacoustic Index, and Normalised Difference Soundscape Index, and a state-of-the-art bird call detection CNN (bulbul). We also compare the effect of non-biotic sounds on the predictions of CityBioNet and bulbul. Finally we apply CityNet to describe acoustic patterns of the urban soundscape in two sites along an urbanisation gradient. CityBioNet was the best performing algorithm for measuring biotic activity in terms of precision and recall, followed by bulbul, whereas the Acoustic Indices performed worst. CityAnthroNet outperformed the Normalised Difference Soundscape Index, but by a smaller margin than CityBioNet achieved against the competing algorithms. The CityBioNet predictions were impacted by mechanical sounds, whereas air traffic and wind sounds influenced the bulbul predictions. Across an urbanisation gradient, we show that CityNet produced realistic daily patterns of biotic and anthropogenic acoustic activity from real-world urban audio data. Using CityNet, it is possible to automatically measure biotic and anthropogenic acoustic activity in cities from audio recordings. If embedded within an autonomous sensing system, CityNet could produce environmental data for cites at large-scales and facilitate investigation of the impacts of anthropogenic activities on wildlife. The algorithms, code and pretrained models are made freely available in combination with two expert-annotated urban audio datasets to facilitate automated environmental surveillance in cities.