79 citations · 405 across the 40 of their papers we have counts for
25 papers · 1 filter
Array Geometry-Robust Attention-Based Neural Beamformer for Moving Speakers
Marvin Tammen, Tsubasa Ochiai, Marc Delcroix +3
Although mask-based beamforming is a powerful speech enhancement approach, it often requires manual parameter tuning to handle moving speakers. Recently, this approach was augmente…
Neural network-based virtual microphone estimation with virtual microphone and beamformer-level multi-task loss
Hanako Segawa, Tsubasa Ochiai, Marc Delcroix +5
Array processing performance depends on the number of microphones available. Virtual microphone estimation (VME) has been proposed to increase the number of microphone signals arti…
Modified Parametric Multichannel Wiener Filter \\for Low-latency Enhancement of Speech Mixtures with Unknown Number of Speakers
Ning Guo, Tomohiro Nakatani, Shoko Araki +1
This paper introduces a novel low-latency online beamforming (BF) algorithm, named Modified Parametric Multichannel Wiener Filter (Mod-PMWF), for enhancing speech mixtures with unk…
Multi-Stream Extension of Variational Bayesian HMM Clustering (MS-VBx) for Combined End-to-End and Vector Clustering-based Diarization
Marc Delcroix, Naohiro Tawara, Mireia Diez +6
Combining end-to-end neural speaker diarization (EEND) with vector clustering (VC), known as EEND-VC, has gained interest for leveraging the strengths of both methods. EEND-VC esti…
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…
Listen only to me! How well can target speech extraction handle false alarms?
Marc Delcroix, Keisuke Kinoshita, Tsubasa Ochiai +3
Target speech extraction (TSE) extracts the speech of a target speaker in a mixture given auxiliary clues characterizing the speaker, such as an enrollment utterance. TSE addresses…
Switching Independent Vector Analysis and Its Extension to Blind and Spatially Guided Convolutional Beamforming Algorithms
Tomohiro Nakatani, Rintaro Ikeshita, Keisuke Kinoshita +3
This paper develops a framework that can perform denoising, dereverberation, and source separation accurately by using a relatively small number of microphones. It has been empiric…
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…
Comparison of remote experiments using crowdsourcing and laboratory experiments on speech intelligibility
Ayako Yamamoto, Toshio Irino, Kenichi Arai +4
Many subjective experiments have been performed to develop objective speech intelligibility measures, but the novel coronavirus outbreak has made it very difficult to conduct exper…
End-to-End Dereverberation, Beamforming, and Speech Recognition with Improved Numerical Stability and Advanced Frontend
Wangyou Zhang, Christoph Boeddeker, Shinji Watanabe +7
Recently, the end-to-end approach has been successfully applied to multi-speaker speech separation and recognition in both single-channel and multichannel conditions. However, seve…
Speaker activity driven neural speech extraction
Marc Delcroix, Katerina Zmolikova, Tsubasa Ochiai +2
Target speech extraction, which extracts the speech of a target speaker in a mixture given auxiliary speaker clues, has recently received increased interest. Various clues have bee…
Multimodal Attention Fusion for Target Speaker Extraction
Hiroshi Sato, Tsubasa Ochiai, Keisuke Kinoshita +3
Target speaker extraction, which aims at extracting a target speaker's voice from a mixture of voices using audio, visual or locational clues, has received much interest. Recently…
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…
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…
Integration of variational autoencoder and spatial clustering for adaptive multi-channel neural speech separation
Katerina Zmolikova, Marc Delcroix, Lukáš Burget +2
In this paper, we propose a method combining variational autoencoder model of speech with a spatial clustering approach for multi-channel speech separation. The advantage of integr…
Multi-path RNN for hierarchical modeling of long sequential data and its application to speaker stream separation
Keisuke Kinoshita, Thilo von Neumann, Marc Delcroix +2
Recently, the source separation performance was greatly improved by time-domain audio source separation based on dual-path recurrent neural network (DPRNN). DPRNN is a simple but e…
Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR
Thilo von Neumann, Christoph Boeddeker, Lukas Drude +4
Most approaches to multi-talker overlapped speech separation and recognition assume that the number of simultaneously active speakers is given, but in realistic situations, it is t…
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…
Improving noise robust automatic speech recognition with single-channel time-domain enhancement network
Keisuke Kinoshita, Tsubasa Ochiai, Marc Delcroix +1
With the advent of deep learning, research on noise-robust automatic speech recognition (ASR) has progressed rapidly. However, ASR performance in noisy conditions of single-channel…
Tackling real noisy reverberant meetings with all-neural source separation, counting, and diarization system
Keisuke Kinoshita, Marc Delcroix, Shoko Araki +1
Automatic meeting analysis is an essential fundamental technology required to let, e.g. smart devices follow and respond to our conversations. To achieve an optimal automatic meeti…
Overdetermined independent vector analysis
Rintaro Ikeshita, Tomohiro Nakatani, Shoko Araki
We address the convolutive blind source separation problem for the (over-)determined case where (i) the number of nonstationary target-sources is less than that of microphones…
Improving speaker discrimination of target speech extraction with time-domain SpeakerBeam
Marc Delcroix, Tsubasa Ochiai, Katerina Zmolikova +4
Target speech extraction, which extracts a single target source in a mixture given clues about the target speaker, has attracted increasing attention. We have recently proposed Spe…
End-to-end training of time domain audio separation and recognition
Thilo von Neumann, Keisuke Kinoshita, Lukas Drude +4
The rising interest in single-channel multi-speaker speech separation sparked development of End-to-End (E2E) approaches to multi-speaker speech recognition. However, up until now,…
Maximum likelihood convolutional beamformer for simultaneous denoising and dereverberation
Tomohiro Nakatani, Keisuke Kinoshita
This article describes a probabilistic formulation of a Weighted Power minimization Distortionless response convolutional beamformer (WPD). The WPD unifies a weighted prediction er…