  We present a method for audio denoising that combines processing done in both
the time domain and the time-frequency domain. Given a noisy audio clip, the
method trains a deep neural network to fit this signal. Since the fitting is
only partly successful and is able to better capture the underlying clean
signal than the noise, the output of the network helps to disentangle the clean
audio from the rest of the signal. This is done by accumulating a fitting score
per time-frequency bin and applying the time-frequency domain filtering based
on the obtained scores. The method is completely unsupervised and only trains
on the specific audio clip that is being denoised. Our experiments demonstrate
favorable performance in comparison to the literature methods. Our code and
samples are available at github.com/mosheman5/DNP and as supplementary. Index
Terms: Audio denoising; Unsupervised learning
