Search Results for author: Shusuke Takahashi

Found 20 papers, 10 papers with code

MoLA: Motion Generation and Editing with Latent Diffusion Enhanced by Adversarial Training

no code implementations4 Jun 2024 Kengo Uchida, Takashi Shibuya, Yuhta Takida, Naoki Murata, Shusuke Takahashi, Yuki Mitsufuji

For high-quality and fast generation, we employ a variational autoencoder and latent diffusion model, and improve the performance with adversarial training.

Motion Synthesis

STARSS23: An Audio-Visual Dataset of Spatial Recordings of Real Scenes with Spatiotemporal Annotations of Sound Events

1 code implementation NeurIPS 2023 Kazuki Shimada, Archontis Politis, Parthasaarathy Sudarsanam, Daniel Krause, Kengo Uchida, Sharath Adavanne, Aapo Hakala, Yuichiro Koyama, Naoya Takahashi, Shusuke Takahashi, Tuomas Virtanen, Yuki Mitsufuji

While direction of arrival (DOA) of sound events is generally estimated from multichannel audio data recorded in a microphone array, sound events usually derive from visually perceptible source objects, e. g., sounds of footsteps come from the feet of a walker.

Sound Event Localization and Detection

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

Diffusion-based Signal Refiner for Speech Separation

no code implementations10 May 2023 Masato Hirano, Kazuki Shimada, Yuichiro Koyama, Shusuke Takahashi, Yuki Mitsufuji

We experimentally show that our refiner can provide a clearer harmonic structure of speech and improves the reference-free metric of perceptual quality for arbitrary preceding model architectures.

Denoising Speech Enhancement +1

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

STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events

2 code implementations4 Jun 2022 Archontis Politis, Kazuki Shimada, Parthasaarathy Sudarsanam, Sharath Adavanne, Daniel Krause, Yuichiro Koyama, Naoya Takahashi, Shusuke Takahashi, Yuki Mitsufuji, Tuomas Virtanen

Additionally, the report presents the baseline system that accompanies the dataset in the challenge with emphasis on the differences with the baseline of the previous iterations; namely, introduction of the multi-ACCDOA representation to handle multiple simultaneous occurences of events of the same class, and support for additional improved input features for the microphone array format.

Sound Event Localization and Detection

SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization

1 code implementation16 May 2022 Yuhta Takida, Takashi Shibuya, WeiHsiang Liao, Chieh-Hsin Lai, Junki Ohmura, Toshimitsu Uesaka, Naoki Murata, Shusuke Takahashi, Toshiyuki Kumakura, Yuki Mitsufuji

In this paper, we propose a new training scheme that extends the standard VAE via novel stochastic dequantization and quantization, called stochastically quantized variational autoencoder (SQ-VAE).

Quantization

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

Preventing Oversmoothing in VAE via Generalized Variance Parameterization

no code implementations17 Feb 2021 Yuhta Takida, Wei-Hsiang Liao, Chieh-Hsin Lai, Toshimitsu Uesaka, Shusuke Takahashi, Yuki Mitsufuji

Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon in which the learned latent space becomes uninformative.

Decoder

AR-ELBO: Preventing Posterior Collapse Induced by Oversmoothing in Gaussian VAE

no code implementations1 Jan 2021 Yuhta Takida, Wei-Hsiang Liao, Toshimitsu Uesaka, Shusuke Takahashi, Yuki Mitsufuji

Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon that the learned latent space becomes uninformative.

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

Cannot find the paper you are looking for? You can Submit a new open access paper.