  This paper proposes an unsupervised DNN-based speech enhancement approach
founded on deep priors (DPs). Here, DP signifies that DNNs are more inclined to
produce clean speech signals than noises. Conventional methods based on DP
typically involve training on a noisy speech signal using a random noise
feature as input, stopping training only a clean speech signal is generated.
However, such conventional approaches encounter challenges in determining the
optimal stop timing, experience performance degradation due to environmental
background noise, and suffer a trade-off between distortion of the clean speech
signal and noise reduction performance. To address these challenges, we utilize
two DNNs: one to generate a clean speech signal and the other to generate
noise. The combined output of these networks closely approximates the noisy
speech signal, with a loss term based on spectral kurtosis utilized to separate
the noisy speech signal into a clean speech signal and noise. The key advantage
of this method lies in its ability to circumvent trade-offs and early stopping
problems, as the signal is decomposed by enough steps. Through evaluation
experiments, we demonstrate that the proposed method outperforms conventional
methods in the case of white Gaussian and environmental noise while effectively
mitigating early stopping problems.
