  Traditional convolutional layers extract features from patches of data by
applying a non-linearity on an affine function of the input. We propose a model
that enhances this feature extraction process for the case of sequential data,
by feeding patches of the data into a recurrent neural network and using the
outputs or hidden states of the recurrent units to compute the extracted
features. By doing so, we exploit the fact that a window containing a few
frames of the sequential data is a sequence itself and this additional
structure might encapsulate valuable information. In addition, we allow for
more steps of computation in the feature extraction process, which is
potentially beneficial as an affine function followed by a non-linearity can
result in too simple features. Using our convolutional recurrent layers we
obtain an improvement in performance in two audio classification tasks,
compared to traditional convolutional layers. Tensorflow code for the
convolutional recurrent layers is publicly available in
https://github.com/cruvadom/Convolutional-RNN.
