  Giant pandas, stereotyped as silent animals, make significantly more vocal
sounds during breeding season, suggesting that sounds are essential for
coordinating their reproduction and expression of mating preference. Previous
biological studies have also proven that giant panda sounds are correlated with
mating results and reproduction. This paper makes the first attempt to devise
an automatic method for predicting mating success of giant pandas based on
their vocal sounds. Given an audio sequence of mating giant pandas recorded
during breeding encounters, we first crop out the segments with vocal sound of
giant pandas, and normalize its magnitude, and length. We then extract acoustic
features from the audio segment and feed the features into a deep neural
network, which classifies the mating into success or failure. The proposed deep
neural network employs convolution layers followed by bidirection gated
recurrent units to extract vocal features, and applies attention mechanism to
force the network to focus on most relevant features. Evaluation experiments on
a data set collected during the past nine years obtain promising results,
proving the potential of audio-based automatic mating success prediction
methods in assisting giant panda reproduction.
