45 citations · 113 across the 10 of their papers we have counts for
7 papers
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…
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…
Independent Vector Extraction for Fast Joint Blind Source Separation and Dereverberation
Rintaro Ikeshita, Tomohiro Nakatani
We address a blind source separation (BSS) problem in a noisy reverberant environment in which the number of microphones is greater than the number of sources of interest, and…
A Joint Diagonalization Based Efficient Approach to Underdetermined Blind Audio Source Separation Using the Multichannel Wiener Filter
Nobutaka Ito, Rintaro Ikeshita, Hiroshi Sawada +1
This paper presents a computationally efficient approach to blind source separation (BSS) of audio signals, applicable even when there are more sources than microphones (i.e., the…
Neural Network-based Virtual Microphone Estimator
Tsubasa Ochiai, Marc Delcroix, Tomohiro Nakatani +3
Developing microphone array technologies for a small number of microphones is important due to the constraints of many devices. One direction to address this situation consists of…
Jointly optimal denoising, dereverberation, and source separation
Tomohiro Nakatani, Christoph Boeddeker, Keisuke Kinoshita +3
This paper proposes methods that can optimize a Convolutional BeamFormer (CBF) for jointly performing denoising, dereverberation, and source separation (DN+DR+SS) in a computationa…