11 citations · 15 across the 10 of their papers we have counts for
11 papers
4D Reconstruction from Sparse Dynamic Cameras
Kazuki Ozeki, Shun Kenney, Yuto Shibata +6
Although dynamic 3D (i.e., 4D) reconstruction from a monocular dynamic camera has recently advanced, it remains fundamentally limited by depth ambiguity. In this paper, we focus on…
MMAudioReverbs: Video-Guided Acoustic Modeling for Dereverberation and Room Impulse Response Estimation
Akira Takahashi, Ryosuke Sawata, Shusuke Takahashi +1
Although recent video-to-audio (V2A) models excelled at synthesizing semantically plausible sounds from visual inputs, they do not explicitly model room-acoustic effects such as re…
HumanGif: Single-View Human Diffusion with Generative Prior
Shoukang Hu, Takuya Narihira, Kazumi Fukuda +3
Previous 3D human creation methods have made significant progress in synthesizing view-consistent and temporally aligned results from sparse-view images or monocular videos. Howeve…
Hearing Anything Anywhere
Mason Wang, Ryosuke Sawata, Samuel Clarke +3
Recent years have seen immense progress in 3D computer vision and computer graphics, with emerging tools that can virtualize real-world 3D environments for numerous Mixed Reality (…
The Whole Is Greater than the Sum of Its Parts: Improving Music Source Separation by Bridging Network
Ryosuke Sawata, Naoya Takahashi, Stefan Uhlich +2
This paper presents the crossing scheme (X-scheme) for improving the performance of deep neural network (DNN)-based music source separation (MSS) with almost no increasing calculat…
Diffusion-based Signal Refiner for Speech Enhancement and Separation
Masato Hirano, Ryosuke Sawata, Naoki Murata +2
Although recent speech processing technologies have achieved significant improvements in objective metrics, there still remains a gap in human perceptual quality. This paper propos…
DiffRoll: Diffusion-based Generative Music Transcription with Unsupervised Pretraining Capability
Kin Wai Cheuk, Ryosuke Sawata, Toshimitsu Uesaka +5
In this paper we propose a novel generative approach, DiffRoll, to tackle automatic music transcription (AMT). Instead of treating AMT as a discriminative task in which the model i…
Diffiner: A Versatile Diffusion-based Generative Refiner for Speech Enhancement
Ryosuke Sawata, Naoki Murata, Yuhta Takida +4
Although deep neural network (DNN)-based speech enhancement (SE) methods outperform the previous non-DNN-based ones, they often degrade the perceptual quality of generated outputs.…
Improving Character Error Rate Is Not Equal to Having Clean Speech: Speech Enhancement for ASR Systems with Black-box Acoustic Models
Ryosuke Sawata, Yosuke Kashiwagi, Shusuke Takahashi
A deep neural network (DNN)-based speech enhancement (SE) aiming to maximize the performance of an automatic speech recognition (ASR) system is proposed in this paper. In order to…
Manifold-Aware Deep Clustering: Maximizing Angles between Embedding Vectors Based on Regular Simplex
Keitaro Tanaka, Ryosuke Sawata, Shusuke Takahashi
This paper presents a new deep clustering (DC) method called manifold-aware DC (M-DC) that can enhance hyperspace utilization more effectively than the original DC. The original DC…
All for One and One for All: Improving Music Separation by Bridging Networks
Ryosuke Sawata, Stefan Uhlich, Shusuke Takahashi +1
This paper proposes several improvements for music separation with deep neural networks (DNNs), namely a multi-domain loss (MDL) and two combination schemes. First, by using MDL we…