Department of Electrical Engineering, Yale University, New Haven, Connecticut 06511, USA.
J Acoust Soc Am. 2020 Nov;148(5):3270. doi: 10.1121/10.0002651.
Classifying foliage targets using echolocation is important for recognizing landmarks by bats using ultrasonic emissions and blind human echolocators (BEs) using palatal clicks. Previous attempts to classify foliage used ultrasonic frequencies and single sensor (monaural) detection. Motivated by the echolocation capabilities of BEs, a biomimetic sonar emitting audible clicks acquired 5600 binaural echoes from five sequential emissions that probed two foliage targets at aspect angles separated by 18°. Echo spectrograms formed feature vector inputs to artificial neural networks (ANNs) for classifying two targets, Ficus benjamina and Schefflera arboricola, with leaf areas that differ by a factor of four. Classification performances of ANNs without and with hidden layers were analyzed using tenfold cross-validation. Performance improved with input feature size, with binaural echo classification outperforming that using monaural echoes for the same number of emissions and for the same number of echoes. Linear classification accuracy was comparable to that using nonlinear classification with both achieving fewer than 1% errors with binaural spectrogram features from five sequential emissions. This result was better by a factor of 20 compared to previous classification of these targets using only the time envelopes of the same echoes.
使用回声定位对树叶目标进行分类，对于蝙蝠使用超声波发射和盲目使用腭部点击的人类回声定位器（BEs）识别地标非常重要。之前对树叶的分类尝试使用了超声波频率和单传感器（单声道）检测。受 BEs 的回声定位能力的启发，一种仿生声纳发出可听点击，从五个连续发射中获取了 5600 个双耳回声，探测了两个叶片目标，方位角相差 18°。回声声谱图形成特征向量输入到人工神经网络（ANNs）中，用于对两种目标（榕属植物和南洋杉）进行分类，这两种目标的叶片面积相差四倍。使用十重交叉验证分析了没有和具有隐藏层的 ANN 的分类性能。随着输入特征大小的增加，分类性能得到了提高，双耳回声的分类性能优于使用相同数量的发射和相同数量的回声的单声道回声。线性分类精度与使用非线性分类的精度相当，使用相同数量的五个连续发射的双耳声谱图特征，两种分类方法的错误率都不到 1%。与之前仅使用相同回声的时间包络对这些目标进行分类相比，这一结果要好 20 倍。
