activity
20182023
most citedJointly optimal denoising, dereverberation, and source separation

45 citations · 135 across the 18 of their papers we have counts for

collaborators

28 papers

eess.AS20221 cited

Mask-based Neural Beamforming for Moving Speakers with Self-Attention-based Tracking

Tsubasa Ochiai, Marc Delcroix, Tomohiro Nakatani +1

Beamforming is a powerful tool designed to enhance speech signals from the direction of a target source. Computing the beamforming filter requires estimating spatial covariance mat…

eess.AS202127 cited

Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation

Tomohiro Nakatani, Rintaro Ikeshita, Keisuke Kinoshita +2

This paper proposes an approach for optimizing a Convolutional BeamFormer (CBF) that can jointly perform denoising (DN), dereverberation (DR), and source separation (SS). First, we…

cs.SD2021

Independent Deeply Learned Tensor Analysis for Determined Audio Source Separation

Naoki Narisawa, Rintaro Ikeshita, Norihiro Takamune +4

We address the determined audio source separation problem in the time-frequency domain. In independent deeply learned matrix analysis (IDLMA), it is assumed that the inter-frequenc…

cs.SD20211 cited

PILOT: Introducing Transformers for Probabilistic Sound Event Localization

Christopher Schymura, Benedikt Bönninghoff, Tsubasa Ochiai +5

Sound event localization aims at estimating the positions of sound sources in the environment with respect to an acoustic receiver (e.g. a microphone array). Recent advances in thi…

cs.SD2021

Exploiting Attention-based Sequence-to-Sequence Architectures for Sound Event Localization

Christopher Schymura, Tsubasa Ochiai, Marc Delcroix +4

Sound event localization frameworks based on deep neural networks have shown increased robustness with respect to reverberation and noise in comparison to classical parametric appr…

cs.SD2021

Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial Domain

Julio Wissing, Benedikt Boenninghoff, Dorothea Kolossa +6

Estimating the positions of multiple speakers can be helpful for tasks like automatic speech recognition or speaker diarization. Both applications benefit from a known speaker posi…