  In this work, we propose a new recurrent autoencoder architecture, termed
Feedback Recurrent AutoEncoder (FRAE), for online compression of sequential
data with temporal dependency. The recurrent structure of FRAE is designed to
efficiently extract the redundancy along the time dimension and allows a
compact discrete representation of the data to be learned. We demonstrate its
effectiveness in speech spectrogram compression. Specifically, we show that the
FRAE, paired with a powerful neural vocoder, can produce high-quality speech
waveforms at a low, fixed bitrate. We further show that by adding a learned
prior for the latent space and using an entropy coder, we can achieve an even
lower variable bitrate.
