  In hearing aids, the presence of babble noise degrades hearing
intelligibility of human speech greatly. However, removing the babble without
creating artifacts in human speech is a challenging task in a low SNR
environment. Here, we sought to solve the problem by finding a `mapping'
between noisy speech spectra and clean speech spectra via supervised learning.
Specifically, we propose using fully Convolutional Neural Networks, which
consist of lesser number of parameters than fully connected networks. The
proposed network, Redundant Convolutional Encoder Decoder (R-CED), demonstrates
that a convolutional network can be 12 times smaller than a recurrent network
and yet achieves better performance, which shows its applicability for an
embedded system: the hearing aids.
