Song Scope在复杂声学环境中的鸟鸣自动识别中的应用
Abstract
Conservationists are increasingly using autonomous acoustic recorders to determine the presence/absence and the abundance of bird species. Unlike humans, these record ers can be left in the field for extensive periods of time in any habitat. Although data acquisition is automated, manual processing of recordings is labour intensive, tedious, and prone to bias due to observer variations. Hence automated birdsong recognition is an efficient alternative.
However, only few ecologists and conservationists utilise the existing birdsong rec ognisers to process unattended field recordings because the software calibration time is exceptionally high and requires considerable knowledge in signal processing and underlying systems, making the tools less user-friendly. Even allowing for these dif f iculties, getting accurate results is exceedingly hard. In this review we examine the state-of-the-art, summarising and discussing the methods currently available for each of the essential parts of a birdsong recogniser, and also available software. The key reasons behind poor automated recognition are that field recordings are very noisy, calls from birds that are a long way from the recorder can be faint or corrupted, and there are overlapping calls from many different birds. In addition, there can be large numbers of different species calling in one recording, and therefore the method has to scale to large numbers of species, or at least avoid misclassifying another species as one of particular interest. We found that these areas of importance, particularly the question of noise reduction, are amongst the least researched. In cases where accurate recognition of individual species is essential, such as in conservation work, we suggest that specialised (species-specific) methods of passive acoustic monitoring are required. We also believe that it is important that comparable measures, and datasets, are used to enable methods to be compared.
摘要：
环保主义者越来越多地使用自主声学记录仪来确定鸟类的存在/不存在和丰度。与人类不同，这些记录者可以在任何栖息地长时间留在野外。尽管数据采集是自动化的，但手动处理记录是劳动密集型的、乏味的，并且由于观察者的变化而容易产生偏差。因此，自动鸟鸣识别是一种有效的替代方案。
然而，只有少数生态学家和环保主义者利用现有的鸟鸣识别器来处理无人值守的现场记录，因为软件校准时间非常长，需要大量的信号处理和底层系统知识，这使得这些工具不那么用户友好。即使考虑到这些困难，也很难得到准确的结果。在这篇综述中，我们研究了最先进的技术，总结和讨论了鸟鸣识别器每个基本部分目前可用的方法，以及可用的软件。自动识别不佳的主要原因是现场记录非常嘈杂，距离记录器很远的鸟类的叫声可能很微弱或被破坏，而且许多不同鸟类的叫声重叠。此外，一个记录中可能有大量不同的物种在呼唤，因此该方法必须扩展到大量的物种，或者至少避免将另一个物种误分类为特别感兴趣的物种。我们发现，这些重要领域，特别是降噪问题，是研究最少的领域之一。在准确识别单个物种至关重要的情况下，例如在保护工作中，我们建议需要专门的（特定物种的）被动声学监测方法。我们还认为，使用可比的度量和数据集来比较方法非常重要。
关键词：Song Scope，Wildlife Acoustics，鸟鸣录音、被动声学监测、机器学习、噪声、鸟鸣识别
