  In this paper, we present RT-GCC-NMF: a real-time (RT), two-channel blind
speech enhancement algorithm that combines the non-negative matrix
factorization (NMF) dictionary learning algorithm with the generalized
cross-correlation (GCC) spatial localization method. Using a pre-learned
universal NMF dictionary, RT-GCC-NMF operates in a frame-by-frame fashion by
associating individual dictionary atoms to target speech or background
interference based on their estimated time-delay of arrivals (TDOA). We
evaluate RT-GCC-NMF on two-channel mixtures of speech and real-world noise from
the Signal Separation and Evaluation Campaign (SiSEC). We demonstrate that this
approach generalizes to new speakers, acoustic environments, and recording
setups from very little training data, and outperforms all but one of the
algorithms from the SiSEC challenge in terms of overall Perceptual Evaluation
methods for Audio Source Separation (PEASS) scores and compares favourably to
the ideal binary mask baseline. Over a wide range of input SNRs, we show that
this approach simultaneously improves the PEASS and signal to noise ratio
(SNR)-based Blind Source Separation (BSS) Eval objective quality metrics as
well as the short-time objective intelligibility (STOI) and extended STOI
(ESTOI) objective speech intelligibility metrics. A flexible, soft masking
function in the space of NMF activation coefficients offers real-time control
of the trade-off between interference suppression and target speaker fidelity.
Finally, we use an asymmetric short-time Fourier transform (STFT) to reduce the
inherent algorithmic latency of RT-GCC-NMF from 64 ms to 2 ms with no loss in
performance. We demonstrate that latencies within the tolerable range for
hearing aids are possible on current hardware platforms.
