Wu Wei, Thompson John A, Bertram Richard, Johnson Frank
Department of Statistics, Florida State University, Tallahassee, FL 32306-4330, USA.
J Neurosci Methods. 2008 Sep 15;174(1):147-54. doi: 10.1016/j.jneumeth.2008.06.033. Epub 2008 Jul 11.
Songbirds are the preeminent animal model for understanding how the brain encodes and produces learned vocalizations. Here, we report a new statistical method, the Kullback-Leibler (K-L) distance, for analyzing vocal change over time. First, we use a computerized recording system to capture all song syllables produced by birds each day. Sound Analysis Pro software [Tchernichovski O, Nottebohm F, Ho CE, Pesaran B, Mitra PP. A procedure for an automated measurement of song similarity. Anim Behav 2000;59:1167-76] is then used to measure the duration of each syllable as well as four spectral features: pitch, entropy, frequency modulation, and pitch goodness. Next, two-dimensional scatter plots of each day of singing are created where syllable duration is on the x-axis and each of the spectral features is represented separately on the y-axis. Each point in the scatter plots represents one syllable and we regard these plots as random samples from a probability distribution. We then apply the standard information-theoretic quantity K-L distance to measure dissimilarity in phonology across days of singing. A variant of this procedure can also be used to analyze differences in syllable syntax.
鸣禽是理解大脑如何编码和产生习得性发声的卓越动物模型。在此，我们报告一种用于分析发声随时间变化的新统计方法——库尔贝克-莱布勒（K-L）距离。首先，我们使用计算机化记录系统捕捉鸟类每天发出的所有歌声音节。然后使用声音分析专业软件[Tchernichovski O，Nottebohm F，Ho CE，Pesaran B，Mitra PP。一种自动测量歌声相似度的程序。动物行为2000；59：1167 - 1176]测量每个音节的时长以及四个频谱特征：音高、熵、频率调制和音高优度。接下来，创建每日歌唱的二维散点图，其中音节时长在x轴上，每个频谱特征分别在y轴上表示。散点图中的每个点代表一个音节，我们将这些图视为来自概率分布的随机样本。然后我们应用标准的信息论量K-L距离来测量不同歌唱日之间音韵学上的差异。此程序的一个变体也可用于分析音节句法的差异。
