  This paper proposes a Bitwise Gated Recurrent Unit (BGRU) network for the
single-channel source separation task. Recurrent Neural Networks (RNN) require
several sets of weights within its cells, which significantly increases the
computational cost compared to the fully-connected networks. To mitigate this
increased computation, we focus on the GRU cells and quantize the feedforward
procedure with binarized values and bitwise operations. The BGRU network is
trained in two stages. The real-valued weights are pretrained and transferred
to the bitwise network, which are then incrementally binarized to minimize the
potential loss that can occur from a sudden introduction of quantization. As
the proposed binarization technique turns only a few randomly chosen parameters
into their binary versions, it gives the network training procedure a chance to
gently adapt to the partly quantized version of the network. It eventually
achieves the full binarization by incrementally increasing the amount of
binarization over the iterations. Our experiments show that the proposed BGRU
method produces source separation results greater than that of a real-valued
fully connected network, with 11-12 dB mean Signal-to-Distortion Ratio (SDR). A
fully binarized BGRU still outperforms a Bitwise Neural Network (BNN) by 1-2 dB
even with less number of layers.
