提高虎鲸个体识别性能的呼叫和分量评估

N. Nichols, L. Atlas, A. Bowles, M. Roch
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引用次数: 1

摘要

本实验的目的是确定SD1(1a/b)发声的初始宽带分量对识别个体虎鲸(Orcinus orca)的贡献。先前的研究表明,使用SD1(1a/b)发声的分类效果比使用SD3(1)发声的分类效果好23%。这一观察结果的一个可能的理论是,在SD1呼叫开始时存在宽带嗡嗡声。从理论上讲,发声的宽带嗡嗡声更连续地采样了发声产生机制的频率响应(经典地描述为语音源-滤波器模型中的滤波器),并可能有助于观察到的识别增加。实验使用Hubbs-SeaWorld Research Institute提供的发声,包括4只鲸鱼(2只雄性,2只雌性)各20只SD1(1a/b)发声。宽带部分从发声中手工分割。利用梅尔频倒谱系数特征向量,利用高斯混合模型对完整和分割的发声进行分类。使用完整的发声,总体准确率为75±2%,使用95%的置信区间。仅使用分段宽带分量,使用95%的置信区间,总体精度为56 +/- 2%。表现的机会是25%。这些结果不能明确地支持或拒绝源滤波器模型,但确实指出需要集中研究开发适当的特征向量,以便使用声学线索进行个人识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Call and component evaluation for improved performance of recognition of killer whale individuals
The objective of this experiment was to determine the contribution of the initial broad band component of the SD1(1a/b) vocalization towards recognition of individual killer whales (Orcinus orca). Prior research showed classification using the SD1(1a/b) vocalization performed 23% better compared to classification using the SD3(1) vocalization. One possible theory for this observation was the presence of a broad band buzz at the initiation of the SD1 call. It was theorized the broad band buzz of the vocalization was more continuously sampling the frequency response of the vocal production mechanism, (classically described as the filter in the source-filter model of speech) and potentially contributed to the observed increase in recognition. Experiments were performed with vocalizations provided by Hubbs-SeaWorld Research Institute and consisted of 20 SD1(1a/b) vocalizations for each of four whales (2 male, 2 female). The broadband component was hand segmented from the vocalization. Classification was performed on the full and segmented vocalizations with a Gaussian mixture model, using mel-frequency cepstral coefficient feature vectors. Using the full vocalization, overall accuracy was 75 +/- 2% using a 95% confidence interval. Using only the segmented broad band component, overall accuracy was 56 +/- 2% using a 95% confidence interval. Chance performance was 25%. These results cannot definitively support or reject a source filter model, but do point to the need for focused research to develop appropriate feature vectors for individual identification using acoustic cues.
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