可变序列鸟鸣声中元素类别和边界的自动识别
Automatic recognition of element classes and boundaries in the birdsong with variable sequences
摘要：
对顺序发声的研究通常需要分析长连续声音中的发声。在诸如发育研究或跨代研究中，必须分析几天或几个月的发声，强烈需要自动识别的方法。虽然已经深入研究了用于应用目的的自动语音识别方法，但盲目地将其应用于生物目的可能不是最佳解决方案。这是因为，与人类语音识别不同，顺序发声的分析通常需要准确提取定时信息。在本研究中，我们提出了自动化系统适合识别鸟鸣，最深入研究的顺序发声之一，专注于鸟鸣的三个属性。首先，一首歌是一系列的声乐元素，称为音符，可以分为类别。其次，鸟鸣的时间结构是精确控制的，这意味着时间信息在歌曲分析中是重要的。最后，根据一定的概率规则产生音符，这可能有助于准确的歌曲识别。我们将鸟鸣声识别过程分为三个子步骤：局部分类、边界检测和全局排序，每个子步骤对应鸟鸣声的三个属性。我们比较了几种不同的方式安排这三个步骤的性能。作为结果，我们证明了深度神经网络和隐马尔可夫模型的混合模型在识别具有可变音符序列的鸟鸣中是有效的。我们提出了适当的安排的方法，根据是否需要准确的边界检测。同时，我们设计了一个新的度量标准来联合评估音符分类和边界检测的准确性。我们的方法应该是适用的，小的修改和调整，在其他物种的歌曲，持有的三个属性的顺序发声。
英文摘要：
Researches on sequential vocalization often require analysis of vocalizations in long continuous sounds. In such studies as developmental ones or studies across generations in which days or months of vocalizations must be analyzed, methods for automatic recognition would be strongly desired. Although methods for automatic speech recognition for application purposes have been intensively studied, blindly applying them for biological purposes may not be an optimal solution. This is because, unlike human speech recognition, analysis of sequential vocalizations often requires accurate extraction of timing information. In the present study we propose automated systems suitable for recognizing birdsong, one of the most intensively investigated sequential vocalizations, focusing on the three properties of the birdsong. First, a song is a sequence of vocal elements, called notes, which can be grouped into categories. Second, temporal structure of birdsong is precisely controlled, meaning that temporal information is important in song analysis. Finally, notes are produced according to certain probabilistic rules, which may facilitate the accurate song recognition. We divided the procedure of song recognition into three sub-steps: local classification, boundary detection, and global sequencing, each of which corresponds to each of the three properties of birdsong. We compared the performances of several different ways to arrange these three steps. As results, we demonstrated a hybrid model of a deep neural network and a hidden Markov model is effective in recognizing birdsong with variable note sequences. We propose suitable arrangements of methods according to whether accurate boundary detection is needed. Also we designed the new measure to jointly evaluate the accuracy of note classification and boundary detection. Our methods should be applicable, with small modification and tuning, to the songs in other species that hold the three properties of the sequential vocalization.
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