  In recent years there have been many deep learning approaches towards the
multi-speaker source separation problem. Most use Long Short-Term Memory -
Recurrent Neural Networks (LSTM-RNN) or Convolutional Neural Networks (CNN) to
model the sequential behavior of speech. In this paper we propose a novel
network for source separation using an encoder-decoder CNN and LSTM in
parallel. Hyper parameters have to be chosen for both parts of the network and
they are potentially mutually dependent. Since hyper parameter grid search has
a high computational burden, random search is often preferred. However, when
sampling a new point in the hyper parameter space, it can potentially be very
close to a previously evaluated point and thus give little additional
information. Furthermore, random sampling is as likely to sample in a promising
area as in an hyper space area dominated with poor performing models.
Therefore, we use a Bayesian hyper parameter optimization technique and find
that the parallel CNN-LSTM outperforms the LSTM-only and CNN-only model.
