Anderson S E, Dave A S, Margoliash D
Department of Organismal Biology and Anatomy, University of Chicago, Illinois 60637, USA.
J Acoust Soc Am. 1996 Aug;100(2 Pt 1):1209-19. doi: 10.1121/1.415968.
The application of dynamic time warping (DTW) to the automated analysis of continuous recordings of animal vocalizations is evaluated. The DTW algorithm compares an input signal with a set of predefined templates representative of categories chosen by the investigator. It directly compares signal spectrograms, and identifies constituents and constituent boundaries, thus permitting the identification of a broad range of signals and signal components. When applied to vocalizations of an indigo bunting (Passerina cyanea) and a zebra finch (Taeniopygia guttata) collected from a low-clutter, low-noise environment, the recognizer identifies syllables in stereotyped songs and calls with greater than 97% accuracy. Syllables of the more variable and lower amplitude indigo bunting plastic song are identified with approximately 84% accuracy. Under restricted recordings conditions, this technique apparently has general applicability to analysis of a variety of animal vocalizations and can dramatically decrease the amount of time spent on manual identification of vocalizations.
对动态时间规整（DTW）算法在动物发声连续记录自动分析中的应用进行了评估。DTW算法将输入信号与一组由研究者选定的、代表不同类别的预定义模板进行比较。它直接比较信号频谱图，并识别其组成部分和组成边界，从而能够识别广泛的信号和信号成分。当应用于从低杂波、低噪声环境中收集的靛蓝彩鹀（Passerina cyanea）和斑胸草雀（Taeniopygia guttata）的发声时，该识别器识别定型歌曲和叫声中的音节的准确率超过97%。对于变化更多、幅度更低的靛蓝彩鹀可塑性歌曲的音节，识别准确率约为84%。在受限的记录条件下，该技术显然普遍适用于各种动物发声的分析，并且可以显著减少人工识别发声所花费的时间。
