Bitwise Neural Networks for Efficient Single-Channel Source Separation

Abstract

We present Bitwise Neural Networks (BNN) as an efficient hardware-friendly solution to single-channel source separation tasks in resource-constrained environments. In the proposed BNN system, we replace all the real-valued operations during the feedforward process of a Deep Neural Network (DNN) with bitwise arithmetic (e.g. the XNOR operation between bipolar binaries in place of multiplications). Thanks to the fully bitwise run-time operations, the BNN system can serve as an alternative solution where efficient real-time processing is critical, for example real-time speech enhancement in embedded systems. Furthermore, we also propose a binarization scheme to convert the input signals into bit strings so that the BNN parameters learn the Boolean mapping between input binarized mixture signals and their target Ideal Binary Masks (IBM). Experiments on the single-channel speech denoising tasks show that the efficient BNN-based source separation system works well with an acceptable performance loss compared to a comprehensive real-valued network, while consuming a minimal amount of resources.