43 citations · 114 across the 18 of their papers we have counts for
18 papers
Stereo Sound Event Localization and Detection with Onscreen/offscreen Classification
Kazuki Shimada, Archontis Politis, Iran R. Roman +10
This paper presents the objective, dataset, baseline, and metrics of Task 3 of the DCASE2025 Challenge on sound event localization and detection (SELD). In previous editions, the c…
Music Foundation Model as Generic Booster for Music Downstream Tasks
WeiHsiang Liao, Yuhta Takida, Yukara Ikemiya +13
We demonstrate the efficacy of using intermediate representations from a single foundation model to enhance various music downstream tasks. We introduce SoniDo, a music foundation…
Zero- and Few-shot Sound Event Localization and Detection
Kazuki Shimada, Kengo Uchida, Yuichiro Koyama +4
Sound event localization and detection (SELD) systems estimate direction-of-arrival (DOA) and temporal activation for sets of target classes. Neural network (NN)-based SELD systems…
STARSS23: An Audio-Visual Dataset of Spatial Recordings of Real Scenes with Spatiotemporal Annotations of Sound Events
Kazuki Shimada, Archontis Politis, Parthasaarathy Sudarsanam +9
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 perce…
Diffusion-Based Speech Enhancement with Joint Generative and Predictive Decoders
Hao Shi, Kazuki Shimada, Masato Hirano +6
Diffusion-based generative speech enhancement (SE) has recently received attention, but reverse diffusion remains time-consuming. One solution is to initialize the reverse diffusio…
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…
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…
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…
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…