32 citations · 159 across the 28 of their papers we have counts for
28 papers
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…
Lattice Rescoring Based on Large Ensemble of Complementary Neural Language Models
Atsunori Ogawa, Naohiro Tawara, Marc Delcroix +1
We investigate the effectiveness of using a large ensemble of advanced neural language models (NLMs) for lattice rescoring on automatic speech recognition (ASR) hypotheses. Previou…
How does end-to-end speech recognition training impact speech enhancement artifacts?
Kazuma Iwamoto, Tsubasa Ochiai, Marc Delcroix +4
Jointly training a speech enhancement (SE) front-end and an automatic speech recognition (ASR) back-end has been investigated as a way to mitigate the influence of \emph{processing…
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…
ConceptBeam: Concept Driven Target Speech Extraction
Yasunori Ohishi, Marc Delcroix, Tsubasa Ochiai +6
We propose a novel framework for target speech extraction based on semantic information, called ConceptBeam. Target speech extraction means extracting the speech of a target speake…
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…
SoundBeam: Target sound extraction conditioned on sound-class labels and enrollment clues for increased performance and continuous learning
Marc Delcroix, Jorge Bennasar Vázquez, Tsubasa Ochiai +3
In many situations, we would like to hear desired sound events (SEs) while being able to ignore interference. Target sound extraction (TSE) tackles this problem by estimating the a…
Effective data screening technique for crowdsourced speech intelligibility experiments: Evaluation with IRM-based speech enhancement
Ayako Yamamoto, Toshio Irino, Shoko Araki +4
It is essential to perform speech intelligibility (SI) experiments with human listeners in order to evaluate objective intelligibility measures for developing effective speech enha…
How Bad Are Artifacts?: Analyzing the Impact of Speech Enhancement Errors on ASR
Kazuma Iwamoto, Tsubasa Ochiai, Marc Delcroix +4
It is challenging to improve automatic speech recognition (ASR) performance in noisy conditions with single-channel speech enhancement (SE). In this paper, we investigate the cause…
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…
Few-shot learning of new sound classes for target sound extraction
Marc Delcroix, Jorge Bennasar Vázquez, Tsubasa Ochiai +2
Target sound extraction consists of extracting the sound of a target acoustic event (AE) class from a mixture of AE sounds. It can be realized using a neural network that extracts…
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…
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…
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…
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…
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…
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…
Block Coordinate Descent Algorithms for Auxiliary-Function-Based Independent Vector Extraction
Rintaro Ikeshita, Tomohiro Nakatani, Shoko Araki
In this paper, we address the problem of extracting all super-Gaussian source signals from a linear mixture in which (i) the number of super-Gaussian sources is less than that…
Listen to What You Want: Neural Network-based Universal Sound Selector
Tsubasa Ochiai, Marc Delcroix, Yuma Koizumi +3
Being able to control the acoustic events (AEs) to which we want to listen would allow the development of more controllable hearable devices. This paper addresses the AE sound sele…
Cognitive-driven convolutional beamforming using EEG-based auditory attention decoding
Ali Aroudi, Marc Delcroix, Tomohiro Nakatani +3
The performance of speech enhancement algorithms in a multi-speaker scenario depends on correctly identifying the target speaker to be enhanced. Auditory attention decoding (AAD) m…
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…