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Bioacoustics-The International Journal of Animal Sound and Its Recording最新文献

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Software performance for the automated identification of bird vocalisations: the case of two closely related species 自动识别鸟类鸣叫的软件性能:两个密切相关物种的案例
IF 1.8 4区 生物学 Q2 Agricultural and Biological Sciences Pub Date : 2021-07-08 DOI: 10.1080/09524622.2021.1945952
J. Marchal, François Fabianek, Y. Aubry
ABSTRACT Autonomous recording units now facilitate the large collection of audio recordings. However, the analysis of large amounts of acoustic data remains a challenge. The time required for manually searching for bird vocalisations may be equivalent or greater to the duration of audio recordings. This major constraint can be significantly reduced through the use of software developed for automated identification of bird vocalisations in audio recordings. We have compared the performance of four software (CallSeeker, Kaleidoscope Pro, Raven Pro, and Song Scope) and a Convolutional Neural Network (CNN) using audio recordings containing calls of Bicknell’s Thrush and Gray-Cheeked Thrush, as well as the vocalisations of other bird species whose acoustic characteristics overlap with those of our target species. We evaluated all the software on the basis of two main criteria, their ability to detect calls and their ability to classify them correctly by species. Software performance ranged from 30 to 90% in terms of call detection (recall) and from 27 to 99% in terms of correct call classification (precision). CNNs offer a promising solution to the long-standing problem of detecting animal vocalisations in noisy soundscapes, while eliminating the tedious manual step of configuring the algorithms to maximise software performance.
摘要:现在,自主录音单元可以方便地收集大量录音。然而,对大量声学数据的分析仍然是一个挑战。手动搜索鸟类发声所需的时间可能等于或大于音频记录的持续时间。通过使用为录音中鸟类发声的自动识别而开发的软件,可以显著减少这一主要限制。我们比较了四种软件(CallSeeker、Kaleidoscope Pro、Raven Pro和Song Scope)和卷积神经网络(CNN)的性能,使用的录音包括比克内尔画眉和灰颊画眉的叫声,以及其他鸟类的发声,这些鸟类的声学特征与我们的目标物种的声学特征重叠。我们根据两个主要标准对所有软件进行了评估,即它们检测叫声的能力和按物种正确分类的能力。软件性能在呼叫检测(召回)方面为30%至90%,在正确的呼叫分类(精度)方面为27%至99%。细胞神经网络为在嘈杂声景中检测动物发声这一长期存在的问题提供了一个很有前途的解决方案,同时消除了配置算法以最大限度地提高软件性能的繁琐手动步骤。
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引用次数: 11
Audio Data Reduction 音频数据缩减
IF 1.8 4区 生物学 Q2 Agricultural and Biological Sciences Pub Date : 2021-07-02 DOI: 10.4324/9781003092919-9
F. Rumsey, T. McCormick
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引用次数: 0
Synchronization 同步
IF 1.8 4区 生物学 Q2 Agricultural and Biological Sciences Pub Date : 2021-07-02 DOI: 10.4324/9781003092919-14
F. Rumsey, Tim McCormick
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引用次数: 0
Power Amplifiers 功率放大器
IF 1.8 4区 生物学 Q2 Agricultural and Biological Sciences Pub Date : 2021-07-02 DOI: 10.4324/9781003092919-12
F. Rumsey, T. McCormick
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引用次数: 0
Digital Recording and Editing Systems 数字录音和编辑系统
IF 1.8 4区 生物学 Q2 Agricultural and Biological Sciences Pub Date : 2021-07-02 DOI: 10.4324/9781003092919-6
F. Rumsey, T. McCormick
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引用次数: 0
Digital Audio Interfaces and Networking 数字音频接口和网络
IF 1.8 4区 生物学 Q2 Agricultural and Biological Sciences Pub Date : 2021-07-02 DOI: 10.4324/9781003092919-10
F. Rumsey, T. McCormick
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引用次数: 0
Auditory Perception and Sound Quality 听觉感知和音质
IF 1.8 4区 生物学 Q2 Agricultural and Biological Sciences Pub Date : 2021-07-02 DOI: 10.4324/9781003092919-2
F. Rumsey, T. McCormick
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引用次数: 0
Two-Channel Stereo 双通道立体声
IF 1.8 4区 生物学 Q2 Agricultural and Biological Sciences Pub Date : 2021-07-02 DOI: 10.4324/9780080468501-24
F. Rumsey, T. McCormick
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引用次数: 0
Digital Audio Principles 数字音频原理
IF 1.8 4区 生物学 Q2 Agricultural and Biological Sciences Pub Date : 2021-07-02 DOI: 10.4324/9781003092919-5
F. Rumsey, T. McCormick
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引用次数: 0
Audio and Acoustics 音频与声学
IF 1.8 4区 生物学 Q2 Agricultural and Biological Sciences Pub Date : 2021-07-02 DOI: 10.4324/9781003092919-1
F. Rumsey, T. McCormick
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引用次数: 0
期刊
Bioacoustics-The International Journal of Animal Sound and Its Recording
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