43 citations · 114 across the 18 of their papers we have counts for
10 papers · 1 filter
STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events
Archontis Politis, Kazuki Shimada, Parthasaarathy Sudarsanam +7
This report presents the Sony-TAu Realistic Spatial Soundscapes 2022 (STARS22) dataset for sound event localization and detection, comprised of spatial recordings of real scenes co…
Distortion Audio Effects: Learning How to Recover the Clean Signal
Johannes Imort, Giorgio Fabbro, Marco A. Martínez Ramírez +3
Given the recent advances in music source separation and automatic mixing, removing audio effects in music tracks is a meaningful step toward developing an automated remixing syste…
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…
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…
Metric Learning with Background Noise Class for Few-shot Detection of Rare Sound Events
Kazuki Shimada, Yuichiro Koyama, Akira Inoue
Few-shot learning systems for sound event recognition have gained interests since they require only a few examples to adapt to new target classes without fine-tuning. However, such…