63 citations · 364 across the 60 of their papers we have counts for
12 papers · 1 filter
Spatial mixup: Directional loudness modification as data augmentation for sound event localization and detection
Ricardo Falcon-Perez, Kazuki Shimada, Yuichiro Koyama +2
Data augmentation methods have shown great importance in diverse supervised learning problems where labeled data is scarce or costly to obtain. For sound event localization and det…
Multi-ACCDOA: Localizing and Detecting Overlapping Sounds from the Same Class with Auxiliary Duplicating Permutation Invariant Training
Kazuki Shimada, Yuichiro Koyama, Shusuke Takahashi +3
Sound event localization and detection (SELD) involves identifying the direction-of-arrival (DOA) and the event class. The SELD methods with a class-wise output format make the mod…
Automatic DJ Transitions with Differentiable Audio Effects and Generative Adversarial Networks
Bo-Yu Chen, Wei-Han Hsu, Wei-Hsiang Liao +3
A central task of a Disc Jockey (DJ) is to create a mixset of mu-sic with seamless transitions between adjacent tracks. In this paper, we explore a data-driven approach that uses a…
Spatial Data Augmentation with Simulated Room Impulse Responses for Sound Event Localization and Detection
Yuichiro Koyama, Kazuhide Shigemi, Masafumi Takahashi +5
Recording and annotating real sound events for a sound event localization and detection (SELD) task is time consuming, and data augmentation techniques are often favored when the a…
Music Source Separation with Deep Equilibrium Models
Yuichiro Koyama, Naoki Murata, Stefan Uhlich +3
While deep neural network-based music source separation (MSS) is very effective and achieves high performance, its model size is often a problem for practical deployment. Deep impl…
Amicable examples for informed source separation
Naoya Takahashi, Yuki Mitsufuji
This paper deals with the problem of informed source separation (ISS), where the sources are accessible during the so-called \textit{encoding} stage. Previous works computed side-i…
Source Mixing and Separation Robust Audio Steganography
Naoya Takahashi, Mayank Kumar Singh, Yuki Mitsufuji
Audio steganography aims at concealing secret information in carrier audio with imperceptible modification on the carrier. Although previous works addressed the robustness of conce…
Music Demixing Challenge 2021
Yuki Mitsufuji, Giorgio Fabbro, Stefan Uhlich +6
Music source separation has been intensively studied in the last decade and tremendous progress with the advent of deep learning could be observed. Evaluation campaigns such as MIR…
Ensemble of ACCDOA- and EINV2-based Systems with D3Nets and Impulse Response Simulation for Sound Event Localization and Detection
Kazuki Shimada, Naoya Takahashi, Yuichiro Koyama +4
This report describes our systems submitted to the DCASE2021 challenge task 3: sound event localization and detection (SELD) with directional interference. Our previous system base…
Training Speech Enhancement Systems with Noisy Speech Datasets
Koichi Saito, Stefan Uhlich, Giorgio Fabbro +1
Recently, deep neural network (DNN)-based speech enhancement (SE) systems have been used with great success. During training, such systems require clean speech data - ideally, in l…
Preventing Oversmoothing in VAE via Generalized Variance Parameterization
Yuhta Takida, Wei-Hsiang Liao, Chieh-Hsin Lai +3
Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon in which the learned latent space becomes uninformative. This is often related to the hy…
Hierarchical disentangled representation learning for singing voice conversion
Naoya Takahashi, Mayank Kumar Singh, Yuki Mitsufuji
Conventional singing voice conversion (SVC) methods often suffer from operating in high-resolution audio owing to a high dimensionality of data. In this paper, we propose a hierarc…