7 citations · 12 across the 10 of their papers we have counts for
11 papers
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…
Lead Instrument Detection from Multitrack Music
Longshen Ou, Yu Takahashi, Ye Wang
Prior approaches to lead instrument detection primarily analyze mixture audio, limited to coarse classifications and lacking generalization ability. This paper presents a novel app…
Differentiable Digital Signal Processing Mixture Model for Synthesis Parameter Extraction from Mixture of Harmonic Sounds
Masaya Kawamura, Tomohiko Nakamura, Daichi Kitamura +3
A differentiable digital signal processing (DDSP) autoencoder is a musical sound synthesizer that combines a deep neural network (DNN) and spectral modeling synthesis. It allows us…
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…
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…
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…
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…
Independent Low-Rank Matrix Analysis Based on Time-Variant Sub-Gaussian Source Model
Shinichi Mogami, Norihiro Takamune, Daichi Kitamura +5
Independent low-rank matrix analysis (ILRMA) is a fast and stable method for blind audio source separation. Conventional ILRMAs assume time-variant (super-)Gaussian source models,…
Monaural source enhancement maximizing source-to-distortion ratio via automatic differentiation
Hiroaki Nakajima, Yu Takahashi, Kazunobu Kondo +1
Recently, deep neural network (DNN) has made a breakthrough in monaural source enhancement. Through a training step by using a large amount of data, DNN estimates a mapping between…
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…