30 citations · 46 across the 22 of their papers we have counts for
6 papers · 1 filter
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…
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…
Prior Distribution Design for Music Bleeding-Sound Reduction Based on Nonnegative Matrix Factorization
Yusaku Mizobuchi, Daichi Kitamura, Tomohiko Nakamura +3
When we place microphones close to a sound source near other sources in audio recording, the obtained audio signal includes undesired sound from the other sources, which is often c…
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…
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…
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…