11 citations · 15 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…