Song Scope在復雜聲學環境中的鳥鳴自動識別中的應用
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：
環保主義者越來越多地使用自主聲學記錄儀來確定鳥類的存在/不存在和豐度。與人類不同，這些記錄者可以在任何棲息地長時間留在野外。盡管數據采集是自動化的，但手動處理記錄是勞動密集型的、乏味的，并且由于觀察者的變化而容易產生偏差。因此，自動鳥鳴識別是一種有效的替代方案。
然而，只有少數生態學家和環保主義者利用現有的鳥鳴識別器來處理無人值守的現場記錄，因為軟件校準時間非常長，需要大量的信號處理和底層系統知識，這使得這些工具不那么用戶友好。即使考慮到這些困難，也很難得到準確的結果。在這篇綜述中，我們研究了最先進的技術，總結和討論了鳥鳴識別器每個基本部分目前可用的方法，以及可用的軟件。自動識別不佳的主要原因是現場記錄非常嘈雜，距離記錄器很遠的鳥類的叫聲可能很微弱或被破壞，而且許多不同鳥類的叫聲重疊。此外，一個記錄中可能有大量不同的物種在呼喚，因此該方法必須擴展到大量的物種，或者至少避免將另一個物種誤分類為特別感興趣的物種。我們發現，這些重要領域，特別是降噪問題，是研究最少的領域之一。在準確識別單個物種至關重要的情況下，例如在保護工作中，我們建議需要專門的（特定物種的）被動聲學監測方法。我們還認為，使用可比的度量和數據集來比較方法非常重要。
關鍵詞：Song Scope，Wildlife Acoustics，鳥鳴錄音、被動聲學監測、機器學習、噪聲、鳥鳴識別
