  Since the advent of deep learning, it has been used to solve various problems
using many different architectures. The application of such deep architectures
to auditory data is also not uncommon. However, these architectures do not
always adequately consider the temporal dependencies in data. We thus propose a
new generic architecture called the Deep Belief Network - Bidirectional Long
Short-Term Memory (DBN-BLSTM) network that models sequences by keeping track of
the temporal information while enabling deep representations in the data. We
demonstrate this new architecture by applying it to the task of music
generation and obtain state-of-the-art results.
