  We propose an end-to-end model based on convolutional and recurrent neural
networks for speech enhancement. Our model is purely data-driven and does not
make any assumptions about the type or the stationarity of the noise. In
contrast to existing methods that use multilayer perceptrons (MLPs), we employ
both convolutional and recurrent neural network architectures. Thus, our
approach allows us to exploit local structures in both the frequency and
temporal domains. By incorporating prior knowledge of speech signals into the
design of model structures, we build a model that is more data-efficient and
achieves better generalization on both seen and unseen noise. Based on
experiments with synthetic data, we demonstrate that our model outperforms
existing methods, improving PESQ by up to 0.6 on seen noise and 0.64 on unseen
noise.
