Pearre Ben, Perkins L Nathan, Markowitz Jeffrey E, Gardner Timothy J
Department of Biology, Boston University, Boston, Massachusetts, United States of America.
Department of Neurobiology, Harvard Medical School, Boston, Massachusetts, United States of America.
PLoS One. 2017 Jul 28;12(7):e0181992. doi: 10.1371/journal.pone.0181992. eCollection 2017.
The song of the adult male zebra finch is strikingly stereotyped. Efforts to understand motor output, pattern generation, and learning have taken advantage of this consistency by investigating the bird's ability to modify specific parts of song under external cues, and by examining timing relationships between neural activity and vocal output. Such experiments require that precise moments during song be identified in real time as the bird sings. Various syllable-detection methods exist, but many require special hardware, software, and know-how, and details on their implementation and performance are scarce. We present an accurate, versatile, and fast syllable detector that can control hardware at precisely timed moments during zebra finch song. Many moments during song can be isolated and detected with false negative and false positive rates well under 1% and 0.005% respectively. The detector can run on a stock Mac Mini with triggering delay of less than a millisecond and a jitter of σ ≈ 2 milliseconds.
成年雄性斑胸草雀的歌声具有显著的刻板性。为了理解运动输出、模式生成和学习，人们利用了这种一致性，通过研究鸟类在外部线索下修改歌曲特定部分的能力，以及通过检查神经活动与发声输出之间的时间关系。此类实验要求在鸟类歌唱时实时识别歌曲中的精确时刻。现有的各种音节检测方法，但许多方法需要特殊的硬件、软件和专业知识，并且关于它们的实现和性能的细节很少。我们提出了一种准确、通用且快速的音节检测器，它可以在斑胸草雀歌唱期间的精确时刻控制硬件。歌曲中的许多时刻都可以被分离和检测出来，误报率和漏报率分别远低于1%和0.005%。该检测器可以在普通的Mac Mini上运行，触发延迟小于一毫秒，抖动为σ≈2毫秒。
