4 citations · 15 across the 24 of their papers we have counts for
19 papers · 1 filter
Online Algorithms for Independent Low-Rank Matrix Analysis and Rank-Constrained Spatial Covariance Matrix Estimation Based on Maximum Weighted Likelihood Estimation
Yuto Ishikawa, Norihiro Takamune, Tomohiko Nakamura +4
Real-time multichannel speech extraction (MSE) under diffuse noise conditions is an important task with a wide range of applications, such as speech recognition and hearing aids. I…
Local Equivariance Error-Based Metrics for Evaluating Sampling-Frequency-Independent Property of Neural Network
Kanami Imamura, Tomohiko Nakamura, Norihiro Takamune +2
Audio signal processing methods based on deep neural networks (DNNs) are typically trained only at a single sampling frequency (SF) and therefore require signal resampling to handl…
Spatial Voice Conversion: Voice Conversion Preserving Spatial Information and Non-target Signals
Kentaro Seki, Shinnosuke Takamichi, Norihiro Takamune +3
This paper proposes a new task called spatial voice conversion, which aims to convert a target voice while preserving spatial information and non-target signals. Traditional voice…
Real-time Speech Extraction Using Spatially Regularized Independent Low-rank Matrix Analysis and Rank-constrained Spatial Covariance Matrix Estimation
Yuto Ishikawa, Kohei Konaka, Tomohiko Nakamura +2
Real-time speech extraction is an important challenge with various applications such as speech recognition in a human-like avatar/robot. In this paper, we propose the real-time ext…
NoisyILRMA: Diffuse-Noise-Aware Independent Low-Rank Matrix Analysis for Fast Blind Source Extraction
Koki Nishida, Norihiro Takamune, Rintaro Ikeshita +3
In this paper, we address the multichannel blind source extraction (BSE) of a single source in diffuse noise environments. To solve this problem even faster than by fast multichann…
Algorithms of Sampling-Frequency-Independent Layers for Non-integer Strides
Kanami Imamura, Tomohiko Nakamura, Norihiro Takamune +2
In this paper, we propose algorithms for handling non-integer strides in sampling-frequency-independent (SFI) convolutional and transposed convolutional layers. The SFI layers have…
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…
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…
Joint-Diagonalizability-Constrained Multichannel Nonnegative Matrix Factorization Based on Multivariate Complex Sub-Gaussian Distribution
Keigo Kamo, Yuki Kubo, Norihiro Takamune +4
In this paper, we address a statistical model extension of multichannel nonnegative matrix factorization (MNMF) for blind source separation, and we propose a new parameter update a…
Convergence-guaranteed Independent Positive Semidefinite Tensor Analysis Based on Student's t Distribution
Tatsuki Kondo, Kanta Fukushige, Norihiro Takamune +4
In this paper, we address a blind source separation (BSS) problem and propose a new extended framework of independent positive semidefinite tensor analysis (IPSDTA). IPSDTA is a st…
Regularized Fast Multichannel Nonnegative Matrix Factorization with ILRMA-based Prior Distribution of Joint-Diagonalization Process
Keigo Kamo, Yuki Kubo, Norihiro Takamune +4
In this paper, we address a convolutive blind source separation (BSS) problem and propose a new extended framework of FastMNMF by introducing prior information for joint diagonaliz…
Acceleration of rank-constrained spatial covariance matrix estimation for blind speech extraction
Yuki Kubo, Norihiro Takamune, Daichi Kitamura +1
In this paper, we propose new accelerated update rules for rank-constrained spatial covariance model estimation, which efficiently extracts a directional target source in diffuse b…
Efficient Full-Rank Spatial Covariance Estimation Using Independent Low-Rank Matrix Analysis for Blind Source Separation
Yuki Kubo, Norihiro Takamune, Daichi Kitamura +1
In this paper, we propose a new algorithm that efficiently separates a directional source and diffuse background noise based on independent low-rank matrix analysis (ILRMA). ILRMA…
Phase reconstruction from amplitude spectrograms based on von-Mises-distribution deep neural network
Shinnosuke Takamichi, Yuki Saito, Norihiro Takamune +2
This paper presents a deep neural network (DNN)-based phase reconstruction from amplitude spectrograms. In audio signal and speech processing, the amplitude spectrogram is often us…
Independent Low-Rank Matrix Analysis Based on Parametric Majorization-Equalization Algorithm
Yoshiki Mitsui, Daichi Kitamura, Norihiro Takamune +3
In this paper, we propose a new optimization method for independent low-rank matrix analysis (ILRMA) based on a parametric majorization-equalization algorithm. ILRMA is an efficien…
Independent Low-Rank Matrix Analysis Based on Complex Student's -Distribution for Blind Audio Source Separation
Shinichi Mogami, Daichi Kitamura, Yoshiki Mitsui +3
In this paper, we generalize a source generative model in a state-of-the-art blind source separation (BSS), independent low-rank matrix analysis (ILRMA). ILRMA is a unified method…