137 citations · 221 across the 13 of their papers we have counts for
16 papers
Subspace Track-before-Detect for Passive Multi-Target Tracking with Unknown Emitted Signals
Nobutaka Ito, Yoshiaki Bando
Passive multi-target tracking (MTT) aims to infer the time-varying kinematic and activity states of an unknown number of sources that emit unknown and possibly nonstationary signal…
Gaussian Process Regression of Steering Vectors With Physics-Aware Deep Composite Kernels for Augmented Listening
Diego Di Carlo, Shoichi Koyama, Nugraha Aditya Arie +3
This paper investigates continuous representations of steering vectors over frequency and microphone/source positions for augmented listening (e.g., spatial filtering and binaural…
SHAMaNS: Sound Localization with Hybrid Alpha-Stable Spatial Measure and Neural Steerer
Diego Di Carlo, Mathieu Fontaine, Aditya Arie Nugraha +2
This paper describes a sound source localization (SSL) technique that combines an -stable model for the observed signal with a neural network-based approach for modeling steerin…
Run-Time Adaptation of Neural Beamforming for Robust Speech Dereverberation and Denoising
Yoto Fujita, Aditya Arie Nugraha, Diego Di Carlo +3
This paper describes speech enhancement for realtime automatic speech recognition (ASR) in real environments. A standard approach to this task is to use neural beamforming that can…
Infrastructure-less Localization from Indoor Environmental Sounds Based on Spectral Decomposition and Spatial Likelihood Model
Satoki Ogiso, Yoshiaki Bando, Takeshi Kurata +1
Human and/or asset tracking using an attached sensor units helps understand their activities. Most common indoor localization methods for human tracking technologies require expens…
Neural Fast Full-Rank Spatial Covariance Analysis for Blind Source Separation
Yoshiaki Bando, Yoshiki Masuyama, Aditya Arie Nugraha +1
This paper describes an efficient unsupervised learning method for a neural source separation model that utilizes a probabilistic generative model of observed multichannel mixtures…
Neural Steerer: Novel Steering Vector Synthesis with a Causal Neural Field over Frequency and Source Positions
Diego Di Carlo, Aditya Arie Nugraha, Mathieu Fontaine +1
We address the problem of accurately interpolating measured anechoic steering vectors with a deep learning framework called the neural field. This task plays a pivotal role in redu…
DNN-Free Low-Latency Adaptive Speech Enhancement Based on Frame-Online Beamforming Powered by Block-Online FastMNMF
Aditya Arie Nugraha, Kouhei Sekiguchi, Mathieu Fontaine +2
This paper describes a practical dual-process speech enhancement system that adapts environment-sensitive frame-online beamforming (front-end) with help from environment-free block…
Direction-Aware Adaptive Online Neural Speech Enhancement with an Augmented Reality Headset in Real Noisy Conversational Environments
Kouhei Sekiguchi, Aditya Arie Nugraha, Yicheng Du +3
This paper describes the practical response- and performance-aware development of online speech enhancement for an augmented reality (AR) headset that helps a user understand conve…
Direction-Aware Joint Adaptation of Neural Speech Enhancement and Recognition in Real Multiparty Conversational Environments
Yicheng Du, Aditya Arie Nugraha, Kouhei Sekiguchi +3
This paper describes noisy speech recognition for an augmented reality headset that helps verbal communication within real multiparty conversational environments. A major approach…
Generalized Fast Multichannel Nonnegative Matrix Factorization Based on Gaussian Scale Mixtures for Blind Source Separation
Mathieu Fontaine, Kouhei Sekiguchi, Aditya Nugraha +2
This paper describes heavy-tailed extensions of a state-of-the-art versatile blind source separation method called fast multichannel nonnegative matrix factorization (FastMNMF) fro…
Self-supervised Neural Audio-Visual Sound Source Localization via Probabilistic Spatial Modeling
Yoshiki Masuyama, Yoshiaki Bando, Kohei Yatabe +3
Detecting sound source objects within visual observation is important for autonomous robots to comprehend surrounding environments. Since sounding objects have a large variety with…
Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model
Yoshiaki Bando, Yoko Sasaki, Kazuyoshi Yoshii
This paper presents an unsupervised method that trains neural source separation by using only multichannel mixture signals. Conventional neural separation methods require a lot of…
Unsupervised Speech Enhancement Based on Multichannel NMF-Informed Beamforming for Noise-Robust Automatic Speech Recognition
Kazuki Shimada, Yoshiaki Bando, Masato Mimura +3
This paper describes multichannel speech enhancement for improving automatic speech recognition (ASR) in noisy environments. Recently, the minimum variance distortionless response…
Fast Multichannel Source Separation Based on Jointly Diagonalizable Spatial Covariance Matrices
Kouhei Sekiguchi, Aditya Arie Nugraha, Yoshiaki Bando +1
This paper describes a versatile method that accelerates multichannel source separation methods based on full-rank spatial modeling. A popular approach to multichannel source separ…
Statistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorization
Yoshiaki Bando, Masato Mimura, Katsutoshi Itoyama +2
This paper presents a statistical method of single-channel speech enhancement that uses a variational autoencoder (VAE) as a prior distribution on clean speech. A standard approach…