  Clipping is a common nonlinear distortion that occurs whenever the input or
output of an audio system exceeds the supported range. This phenomenon
undermines not only the perception of speech quality but also downstream
processes utilizing the disrupted signal. Therefore, a real-time-capable,
robust, and low-response-time method for speech declipping (SD) is desired. In
this work, we introduce DDD (Demucs-Discriminator-Declipper), a
real-time-capable speech-declipping deep neural network (DNN) that requires
less response time by design. We first observe that a previously untested
real-time-capable DNN model, Demucs, exhibits a reasonable declipping
performance. Then we utilize adversarial learning objectives to increase the
perceptual quality of output speech without additional inference overhead.
Subjective evaluations on harshly clipped speech shows that DDD outperforms the
baselines by a wide margin in terms of speech quality. We perform detailed
waveform and spectral analyses to gain an insight into the output behavior of
DDD in comparison to the baselines. Finally, our streaming simulations also
show that DDD is capable of sub-decisecond mean response times, outperforming
the state-of-the-art DNN approach by a factor of six.
