43 citations · 70 across the 6 of their papers we have counts for
7 papers
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…
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…
Exploring Optimal DNN Architecture for End-to-End Beamformers Based on Time-frequency References
Yuichiro Koyama, Bhiksha Raj
Acoustic beamformers have been widely used to enhance audio signals. Currently, the best methods are the deep neural network (DNN)-powered variants of the generalized eigenvalue an…
Efficient Integration of Multi-channel Information for Speaker-independent Speech Separation
Yuichiro Koyama, Oluwafemi Azeez, Bhiksha Raj
Although deep-learning-based methods have markedly improved the performance of speech separation over the past few years, it remains an open question how to integrate multi-channel…
Exploring the Best Loss Function for DNN-Based Low-latency Speech Enhancement with Temporal Convolutional Networks
Yuichiro Koyama, Tyler Vuong, Stefan Uhlich +1
Recently, deep neural networks (DNNs) have been successfully used for speech enhancement, and DNN-based speech enhancement is becoming an attractive research area. While time-frequ…
W-Net BF: DNN-based Beamformer Using Joint Training Approach
Yuichiro Koyama, Bhiksha Raj
Acoustic beamformers have been widely used to enhance audio signals. The best current methods are DNN-powered variants of the generalized eigenvalue beamformer, and DNN-based filte…