We investigate the recently proposed Time-domain Audio Separation Network (TasNet) in the task of real-time single-channel speech dereverberation. Unlike systems that take time-frequency representation of the audio as input, TasNet learns an adaptive front-end in replacement of the time-frequency representation by a time-domain convolutional non-negative autoencoder. We show that by formulating the dereverberation problem as a denoising problem where the direct path is separated from the reverberations, a TasNet denoising autoencoder can outperform a deep LSTM baseline on log-power magnitude spectrogram input in both causal and non-causal settings. We further show that adjusting the stride size in the convolutional autoencoder helps both the dereverberation and separation performance.