6 citations · 7 across the 2 of their papers we have counts for
3 papers
Unsupervised vocal dereverberation with diffusion-based generative models
Koichi Saito, Naoki Murata, Toshimitsu Uesaka +4
Removing reverb from reverberant music is a necessary technique to clean up audio for downstream music manipulations. Reverberation of music contains two categories, natural reverb…
Training Speech Enhancement Systems with Noisy Speech Datasets
Koichi Saito, Stefan Uhlich, Giorgio Fabbro +1
Recently, deep neural network (DNN)-based speech enhancement (SE) systems have been used with great success. During training, such systems require clean speech data - ideally, in l…
Sampling-Frequency-Independent Audio Source Separation Using Convolution Layer Based on Impulse Invariant Method
Koichi Saito, Tomohiko Nakamura, Kohei Yatabe +2
Audio source separation is often used as preprocessing of various applications, and one of its ultimate goals is to construct a single versatile model capable of dealing with the v…