  Diffusion probabilistic models have demonstrated an outstanding capability to
model natural images and raw audio waveforms through a paired diffusion and
reverse processes. The unique property of the reverse process (namely,
eliminating non-target signals from the Gaussian noise and noisy signals) could
be utilized to restore clean signals. Based on this property, we propose a
diffusion probabilistic model-based speech enhancement (DiffuSE) model that
aims to recover clean speech signals from noisy signals. The fundamental
architecture of the proposed DiffuSE model is similar to that of DiffWave--a
high-quality audio waveform generation model that has a relatively low
computational cost and footprint. To attain better enhancement performance, we
designed an advanced reverse process, termed the supportive reverse process,
which adds noisy speech in each time-step to the predicted speech. The
experimental results show that DiffuSE yields performance that is comparable to
related audio generative models on the standardized Voice Bank corpus SE task.
Moreover, relative to the generally suggested full sampling schedule, the
proposed supportive reverse process especially improved the fast sampling,
taking few steps to yield better enhancement results over the conventional full
step inference process.
