  In recent decade, many state-of-the-art algorithms on image classification as
well as audio classification have achieved noticeable successes with the
development of deep convolutional neural network (CNN). However, most of the
works only exploit single type of training data. In this paper, we present a
study on classifying bird species by exploiting the combination of both visual
(images) and audio (sounds) data using CNN, which has been sparsely treated so
far. Specifically, we propose CNN-based multimodal learning models in three
types of fusion strategies (early, middle, late) to settle the issues of
combining training data cross domains. The advantage of our proposed method
lies on the fact that We can utilize CNN not only to extract features from
image and audio data (spectrogram) but also to combine the features across
modalities. In the experiment, we train and evaluate the network structure on a
comprehensive CUB-200-2011 standard data set combing our originally collected
audio data set with respect to the data species. We observe that a model which
utilizes the combination of both data outperforms models trained with only an
either type of data. We also show that transfer learning can significantly
increase the classification performance.
