  Monaural speech enhancement (SE) provides a versatile and cost-effective
approach to SE tasks by utilizing recordings from a single microphone. However,
the monaural SE lags performance behind multi-channel SE as the monaural SE
methods are unable to extract spatial information from one-channel recordings,
which greatly limits their application scenarios. To address this issue, we
inject spatial information into the monaural SE model and propose a knowledge
distillation strategy to enable the monaural SE model to learn binaural speech
features from the binaural SE model, which makes monaural SE model possible to
reconstruct higher intelligibility and quality enhanced speeches under low
signal-to-noise ratio (SNR) conditions. Extensive experiments show that our
proposed monaural SE model by injecting spatial information via knowledge
distillation achieves favorable performance against other monaural SE models
with fewer parameters.
