Search Results for author: Koichi Saito

Found 6 papers, 2 papers with code

GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration

1 code implementation30 Jan 2023 Naoki Murata, Koichi Saito, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon

Pre-trained diffusion models have been successfully used as priors in a variety of linear inverse problems, where the goal is to reconstruct a signal from noisy linear measurements.

Blind Image Deblurring Denoising +1

Unsupervised vocal dereverberation with diffusion-based generative models

no code implementations8 Nov 2022 Koichi Saito, Naoki Murata, Toshimitsu Uesaka, Chieh-Hsin Lai, Yuhta Takida, Takao Fukui, Yuki Mitsufuji

Removing reverb from reverberant music is a necessary technique to clean up audio for downstream music manipulations.

Training Speech Enhancement Systems with Noisy Speech Datasets

no code implementations26 May 2021 Koichi Saito, Stefan Uhlich, Giorgio Fabbro, Yuki Mitsufuji

Furthermore, we propose a noise augmentation scheme for mixture-invariant training (MixIT), which allows using it also in such scenarios.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Sampling-Frequency-Independent Audio Source Separation Using Convolution Layer Based on Impulse Invariant Method

1 code implementation10 May 2021 Koichi Saito, Tomohiko Nakamura, Kohei Yatabe, Yuma Koizumi, Hiroshi Saruwatari

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 varieties of audio signals.

Audio Source Separation Music Source Separation

U(1) spin Chern-Simons theory and Arf invariants in two dimensions

no code implementations7 May 2020 Takuya Okuda, Koichi Saito, Shuichi Yokoyama

We use the modular matrices to calculate the partition function of the spin Chern-Simons theory on the lens space $L(a,\pm 1)$, and demonstrate the expected dependence on the 3d spin structure.

High Energy Physics - Theory Strongly Correlated Electrons

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