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Yoshiaki Bando

Researcher at National Institute of Advanced Industrial Science and Technology

Publications -  71
Citations -  814

Yoshiaki Bando is an academic researcher from National Institute of Advanced Industrial Science and Technology. The author has contributed to research in topics: Non-negative matrix factorization & Source separation. The author has an hindex of 14, co-authored 57 publications receiving 537 citations. Previous affiliations of Yoshiaki Bando include Kyoto University.

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Proceedings ArticleDOI

Statistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorization

TL;DR: This paper presents a statistical method of single-channel speech enhancement that uses a variational autoencoder (VAE) as a prior distribution on clean speech that outperformed the conventional DNN-based method in unseen noisy environments.
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Design of UAV-Embedded Microphone Array System for Sound Source Localization in Outdoor Environments

TL;DR: The design and implementation of a UAV-embedded microphone array system for sound source localization in outdoor environments and results confirmed that the SMAS provides highly accurate localization, water resistance, prompt assembly, stable wireless communication, and intuitive information for observers and operators.
Proceedings ArticleDOI

Bayesian Multichannel Speech Enhancement with a Deep Speech Prior

TL;DR: A semi-supervised method based on an extension of MNMF that consists of a deep generative model for speech spectra and a standard low-rank model for noise spectra, and the experimental results showed the potential of the proposed method.
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Development of microphone-array-embedded UAV for search and rescue task

TL;DR: This paper addresses online outdoor sound source localization using a microphone array embedded in an unmanned aerial vehicle (UAV) to cope with trade-off between latency and noise robustness, and develops data compression based on free lossless audio codec extended to support a 16 ch audio data stream via UDP and a water-resistant microphone array.
Journal ArticleDOI

Fast Multichannel Nonnegative Matrix Factorization With Directivity-Aware Jointly-Diagonalizable Spatial Covariance Matrices for Blind Source Separation

TL;DR: This article describes a computationally-efficient blind source separation method based on the independence, low-rankness, and directivity of the sources and proposes rank-constrained FastMNMF that enables us to individually specify the ranks of SCMs.