PFI-TT: Using machine listening for non-invasive monitoring of the status and wellbeing of commercial poultry flocks

这一创新-技术转化伙伴关系(PFI-TT)项目的更广泛影响/商业潜力是提高我们食品供应链中商业化饲养的鸡的福祉和生产率。家禽行业竞争激烈，家禽已成为生产效率最高的动物蛋白之一。美国每年种植数十亿只鸟，其中大多数是由独立农民与较大的生产者合作饲养的。这个PFI项目旨在提供对鸡群的非侵入性、持续的监测，以便及时提醒养殖户注意问题，并帮助养殖户评估其养鸡群的福利和需求。预计最终结果将是更健康、更好地照顾鸟类以及提高农场生产率。拟议的项目使使用机器学习来监控音频环境成为可能，在这种情况下，鸡场，而不需要大量的标签训练数据。通常用于音频机器学习的方法需要大量的标记训练样本集和调谐。然而，人类可以熟悉特定情况下的声音，并在不需要大量训练的情况下注意到发生了不该发生的事情。通过使用学习环境中声音结构和这些声音的典型变化的算法，拟议项目中使用的方法可以类似于人类监听者注意到农场声景中的重大变化(异常)。这些异常的特征是严重的，可以提请农民或研究人员注意，他们可以选择标记或忽略新的声音。然后可以使用标记的声音来自动标记未来的异常。该项目将在多个商业养鸡场部署监听设备，收集连续的录音和元数据，并使用收集的数据来提高监测鸡的福利和环境的系统的性能和实用性。这种倾听方法是非侵入性的，不会对动物造成压力。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact / commercial potential of this Partnerships for Innovation-Technology Translation (PFI-TT) project is to enhance the well-being and productivity of commercially raised chickens in our food supply chain. The poultry industry is highly competitive, and poultry has become one of the most efficient animal proteins to produce. Billions of birds are grown in the United States each year and most are raised by independent farmers in cooperation with larger producers. This PFI project is aimed at providing non-invasive, continuous monitoring of chicken flocks to alert the farmer to problems in a timely manner and aid the farmer in assessing the welfare and needs of his flocks. The end result is expected to be healthier and better-cared for birds as well as improved farm productivity.The proposed project makes it possible to use machine learning to monitor audio environments, in this case chicken farms, without needing extensive labeled training data. The approaches typically used for audio machine learning require extensive labeled training sample sets and tuning. However, humans can become familiar with the sounds in a particular situation, and notice when something occurs that does not belong, without significant training. By using algorithms that learn the structure of sounds in an environment and learn the typical variation of those sounds, the methods used in the proposed project can act similarly to a human listener to notice significant changes (anomalies) in a soundscape on a farm. The anomalies are characterized by severity and can be brought to the attention of a farmer or researchers who can choose to label or ignore the new sound. Labeled sounds can then be used to automatically label future anomalies. This project will deploy listening devices into multiple commercial chicken farms, collect continuous audio recordings and metadata, and use the collected data to improve the performance and usefulness of the systems for monitoring the chickens' welfare and their environment. This listening method is non-invasive and causes no stress to the animals.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.