Search Results for author: Ryosuke Sawata

Found 6 papers, 3 papers with code

The Whole Is Greater than the Sum of Its Parts: Improving DNN-based Music Source Separation

1 code implementation13 May 2023 Ryosuke Sawata, Naoya Takahashi, Stefan Uhlich, Shusuke Takahashi, Yuki Mitsufuji

We modify the target network, i. e., the network architecture of the original DNN-based MSS, by adding bridging paths for each output instrument to share their information.

Music Source Separation

Diffiner: A Versatile Diffusion-based Generative Refiner for Speech Enhancement

1 code implementation27 Oct 2022 Ryosuke Sawata, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Takashi Shibuya, Shusuke Takahashi, Yuki Mitsufuji

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.

Denoising Speech Enhancement

Improving Character Error Rate Is Not Equal to Having Clean Speech: Speech Enhancement for ASR Systems with Black-box Acoustic Models

no code implementations12 Oct 2021 Ryosuke Sawata, Yosuke Kashiwagi, Shusuke Takahashi

In order to optimize the DNN-based SE model in terms of the character error rate (CER), which is one of the metric to evaluate the ASR system and generally non-differentiable, our method uses two DNNs: one for speech processing and one for mimicking the output CERs derived through an acoustic model (AM).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Manifold-Aware Deep Clustering: Maximizing Angles between Embedding Vectors Based on Regular Simplex

no code implementations4 Jun 2021 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.

Clustering Deep Clustering

All for One and One for All: Improving Music Separation by Bridging Networks

5 code implementations8 Oct 2020 Ryosuke Sawata, Stefan Uhlich, Shusuke Takahashi, Yuki Mitsufuji

This paper proposes several improvements for music separation with deep neural networks (DNNs), namely a multi-domain loss (MDL) and two combination schemes.

Music Source Separation

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