Analyses for biodiversity monitoring based on passive acoustic monitoring
(PAM) recordings is time-consuming and challenged by the presence of background
noise in recordings. Existing models for sound event detection (SED) worked
only on certain avian species and the development of further models required
labeled data. The developed framework automatically extracted labeled data from
available platforms for selected avian species. The labeled data were embedded
into recordings, including environmental sounds and noise, and were used to
train convolutional recurrent neural network (CRNN) models. The models were
evaluated on unprocessed real world data recorded in urban KwaZulu-Natal
habitats. The Adapted SED-CRNN model reached a F1 score of 0.73, demonstrating
its efficiency under noisy, real-world conditions. The proposed approach to
automatically extract labeled data for chosen avian species enables an easy
adaption of PAM to other species and habitats for future conservation projects.

基于被动声学监测（PAM）录音进行生物多样性监测的分析既耗时又受到录音中背景噪声的挑战，现有的声音事件检测（SED）模型仅适用于特定的鸟类物种，进一步模型的发展需要标记数据。本研究开发的框架从可用平台自动提取选定鸟类物种的标记数据，将标记数据嵌入到包括环境声音和噪声在内的录音中，用于训练卷积递归神经网络（CRNN）模型。该模型在记录于南非库鲁纳塔尔地区城市栖息地的未经处理的真实世界数据上进行评估，修正的 SED-CRNN 模型达到了 0.73 的 F1 分数，证明其在嘈杂的真实环境条件下的高效性。该研究提出的自动提取选定鸟类物种的标记数据的方法为未来保护项目中轻松实现将 PAM 技术适用于其他物种和栖息地提供了可能。

南非鸟类物种的自动生物声学监测在未标记数据上

Automated Bioacoustic Monitoring for South African Bird Species on  Unlabeled Data

Temperature and humidity are two of the rudimentary factors that must be
controlled during egg incubation. Improper temperature and humidity levels
during the incubation period often result in unwanted conditions. This paper
proposes the design of an efficient Mamdani fuzzy interference system instead
of the widely used Takagi-Sugeno system in this field for controlling the
temperature and humidity levels of an egg incubator. Though the optimum
incubation temperature and humidity levels used here are that of chicken egg,
the proposed methodology is applicable to other avian species as well. Theinput
functions have been used here as per estimated values forsafe hatching using
Mamdani whereas defuzzification method, COA, has been applied for output. From
the model output,a stabilized heat from temperature level and fan speed to
control the humidity level of an egg incubator can be obtained. This maximizes
the hatching rate of healthy chicks under any conditions in the field.

本文提出了利用 Mamdani 模糊干预系统来控制温湿度水平以提高鸟类蛋孵化成功率的方法，并使用鸡蛋的最佳温湿度水平作为试验对象，同时也可以适用于其他鸟类物种。