3 citations · 5 across the 5 of their papers we have counts for
5 papers
FINALLY: fast and universal speech enhancement with studio-like quality
Nicholas Babaev, Kirill Tamogashev, Azat Saginbaev +6
In this paper, we address the challenge of speech enhancement in real-world recordings, which often contain various forms of distortion, such as background noise, reverberation, an…
Regularized Distribution Matching Distillation for One-step Unpaired Image-to-Image Translation
Denis Rakitin, Ivan Shchekotov, Dmitry Vetrov
Diffusion distillation methods aim to compress the diffusion models into efficient one-step generators while trying to preserve quality. Among them, Distribution Matching Distillat…
UnDiff: Unsupervised Voice Restoration with Unconditional Diffusion Model
Anastasiia Iashchenko, Pavel Andreev, Ivan Shchekotov +2
This paper introduces UnDiff, a diffusion probabilistic model capable of solving various speech inverse tasks. Being once trained for speech waveform generation in an unconditional…
Iterative autoregression: a novel trick to improve your low-latency speech enhancement model
Pavel Andreev, Nicholas Babaev, Azat Saginbaev +2
Streaming models are an essential component of real-time speech enhancement tools. The streaming regime constrains speech enhancement models to use only a tiny context of future in…
FFC-SE: Fast Fourier Convolution for Speech Enhancement
Ivan Shchekotov, Pavel Andreev, Oleg Ivanov +2
Fast Fourier convolution (FFC) is the recently proposed neural operator showing promising performance in several computer vision problems. The FFC operator allows employing large r…