As a solitary species, giant pandas do not frequently vocalize. However, they make significantly more vocalizations during the breeding season, implying that vocalizations are essential for coordinating their reproduction and expression of mating preference. Previous studies have also shown that giant panda vocalizations are correlated with mating results and reproduction. This paper makes the first attempt to devise an automatic method for predicting the mating success of giant pandas based on their vocalizations. Using audio recordings of mating giant pandas collected during breeding encounters, we firstly isolated the vocalizations, and normalized their decibels and duration. We then extracted acoustic features from the audio segments and fed the acoustic features into a deep neural network, which classified the mating result into a success or a failure. The proposed deep neural network employs convolution layers followed by bidirectional gated recurrent units to extract features relevant to mating preference. Evaluation experiments on a data set collected during the past nine years obtained promising results, demonstrating the potential of audio-based automatic mating success prediction in assisting giant panda reproduction.

大熊猫作为一种孤独的物种，不会经常发声。但是，它们在繁殖季节发出更多的发声，这意味着发声对于协调它们的繁殖和交配偏好的表达至关重要。先前的研究还表明，大熊猫发声与交配结果和繁殖相关。本文首次尝试设计一种基于大熊猫发声预测大熊猫交配成功的自动方法。使用在繁殖过程中收集的交配大熊猫的录音，我们首先隔离发声，并将其分贝和持续时间标准化。然后，我们从音频片段中提取声学特征，并将声学特征馈入深度神经网络，它将交配结果分类为成功还是失败。所提出的深度神经网络采用卷积层，然后是双向门控递归单元，以提取与交配偏好相关的特征。在过去九年中对数据集进行的评估实验获得了可喜的结果，证明了基于音频的自动交配成功预测在协助大熊猫繁殖方面的潜力。