  We present a hybrid framework that leverages the trade-off between temporal
and frequency precision in audio representations to improve the performance of
speech enhancement task. We first show that conventional approaches using
specific representations such as raw-audio and spectrograms are each effective
at targeting different types of noise. By integrating both approaches, our
model can learn multi-scale and multi-domain features, effectively removing
noise existing on different regions on the time-frequency space in a
complementary way. Experimental results show that the proposed hybrid model
yields better performance and robustness than using each model individually.
