activity
20172022
most citedJoint-Diagonalizability-Constrained Multichannel Nonnegative Matrix Factorization Based on Multivariate Complex Sub-Gaussian Distribution

4 citations · 7 across the 11 of their papers we have counts for

collaborators

16 papers

eess.AS2022

Hyperbolic Timbre Embedding for Musical Instrument Sound Synthesis Based on Variational Autoencoders

Futa Nakashima, Tomohiko Nakamura, Norihiro Takamune +2

In this paper, we propose a musical instrument sound synthesis (MISS) method based on a variational autoencoder (VAE) that has a hierarchy-inducing latent space for timbre. VAE-bas…

cs.SD2021

Speech Enhancement by Noise Self-Supervised Rank-Constrained Spatial Covariance Matrix Estimation via Independent Deeply Learned Matrix Analysis

Sota Misawa, Norihiro Takamune, Tomohiko Nakamura +4

Rank-constrained spatial covariance matrix estimation (RCSCME) is a method for the situation that the directional target speech and the diffuse noise are mixed. In conventional RCS…

cs.SD2021

Multichannel Audio Source Separation with Independent Deeply Learned Matrix Analysis Using Product of Source Models

Takuya Hasumi, Tomohiko Nakamura, Norihiro Takamune +4

Independent deeply learned matrix analysis (IDLMA) is one of the state-of-the-art multichannel audio source separation methods using the source power estimation based on deep neura…

cs.SD2021

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…

cs.SD2021

Empirical Bayesian Independent Deeply Learned Matrix Analysis For Multichannel Audio Source Separation

Takuya Hasumi, Tomohiko Nakamura, Norihiro Takamune +4

Independent deeply learned matrix analysis (IDLMA) is one of the state-of-the-art supervised multichannel audio source separation methods. It blindly estimates the demixing filters…

cs.SD2021

Deficient Basis Estimation of Noise Spatial Covariance Matrix for Rank-Constrained Spatial Covariance Matrix Estimation Method in Blind Speech Extraction

Yuto Kondo, Yuki Kubo, Norihiro Takamune +2

Rank-constrained spatial covariance matrix estimation (RCSCME) is a state-of-the-art blind speech extraction method applied to cases where one directional target speech and diffuse…