63 citations · 364 across the 60 of their papers we have counts for
6 papers · 1 filter
Densely connected multidilated convolutional networks for dense prediction tasks
Naoya Takahashi, Yuki Mitsufuji
Tasks that involve high-resolution dense prediction require a modeling of both local and global patterns in a large input field. Although the local and global structures often depe…
ACCDOA: Activity-Coupled Cartesian Direction of Arrival Representation for Sound Event Localization and Detection
Kazuki Shimada, Yuichiro Koyama, Naoya Takahashi +2
Neural-network (NN)-based methods show high performance in sound event localization and detection (SELD). Conventional NN-based methods use two branches for a sound event detection…
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…
Adversarial attacks on audio source separation
Naoya Takahashi, Shota Inoue, Yuki Mitsufuji
Despite the excellent performance of neural-network-based audio source separation methods and their wide range of applications, their robustness against intentional attacks has bee…
D3Net: Densely connected multidilated DenseNet for music source separation
Naoya Takahashi, Yuki Mitsufuji
Music source separation involves a large input field to model a long-term dependence of an audio signal. Previous convolutional neural network (CNN)-based approaches address the la…
Sound Event Localization and Detection Using Activity-Coupled Cartesian DOA Vector and RD3net
Kazuki Shimada, Naoya Takahashi, Shusuke Takahashi +1
Our systems submitted to the DCASE2020 task~3: Sound Event Localization and Detection (SELD) are described in this report. We consider two systems: a single-stage system that solve…