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Daichi Kitamura

11 accepted papers

2022

Differentiable Digital Signal Processing Mixture Model for Synthesis Parameter Extraction from Mixture of Harmonic Sounds

Masaya Kawamura, Tomohiko Nakamura, Daichi Kitamura, Hiroshi Saruwatari, Yu Takahashi, Kazunobu Kondo
ICASSP 2022accepted

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 to flexibly edit sounds by changing the fundamental frequency, timbre feature, and loudness (synthesis parameters) extr…

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2021

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, Daichi Kitamura, Hiroshi Saruwatari
ICASSP 2021accepted

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 noise are mixed. In this paper, we proposed a new algorithmic extension of RCSCME. RCSCME complements a deficient one r…

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2021

Linear Multichannel Blind Source Separation based on Time-Frequency Mask Obtained by Harmonic/Percussive Sound Separation

Soichiro Oyabu, Daichi Kitamura, Kohei Yatabe
ICASSP 2021accepted

Determined blind source separation (BSS) extracts the source signals by linear multichannel filtering. Its performance depends on the accuracy of source modeling, and hence existing BSS methods have proposed several source models. Recently, a new determined BSS algorithm that incorporates a time-fre…

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2020

Convergence-Guaranteed Independent Positive Semidefinite Tensor Analysis Based on Student's T Distribution

Tatsuki Kondo, Kanta Fukushige, Norihiro Takamune, Daichi Kitamura, Hiroshi Saruwatari, Rintaro Ikeshita +1
ICASSP 2020accepted

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 state-of-the-art BSS method that enables us to take interfrequency correlations into account, but the generative model is…

Cited by 0Source ↗Scholar ↗
2020

Regularized Fast Multichannel Nonnegative Matrix Factorization with ILRMA-Based Prior Distribution of Joint-Diagonalization Process

Keigo Kamo, Yuki Kubo, Norihiro Takamune, Daichi Kitamura, Hiroshi Saruwatari, Yu Takahashi +1
ICASSP 2020accepted

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 diagonalization of the spatial covariance matrix model. Recently, FastMNMF has been proposed as a fast version of multichannel non…

Cited by 0Source ↗Scholar ↗
2018

Vectorwise Coordinate Descent Algorithm for Spatially Regularized Independent Low-Rank Matrix Analysis

Yoshiki Mitsui, Norihiro Takamune, Daichi Kitamura, Hiroshi Saruwatari, Yu Takahashi, Kazunobu Kondo
ICASSP 2018accepted

Audio source separation is an important problem for many audio applications. Independent low-rank matrix analysis (ILRMA) is a recently proposed algorithm that employs the statistical independence between sources and the low-rankness of the time-frequency structure in each source. As reported in thi…

Cited by 38Source ↗Scholar ↗
2017

Blind source separation based on independent low-rank matrix analysis with sparse regularization for time-series activity

Yoshiki Mitsui, Daichi Kitamura, Shinnosuke Takamichi, Nobutaka Ono, Hiroshi Saruwatari
ICASSP 2017accepted

In this paper, we propose a new blind source separation (BSS) method based on independent low-rank matrix analysis (ILRMA) with novel sparse regularization. ILRMA is a recently proposed BSS algorithm that simultaneously estimates a demixing matrix and source spectrogram models based on nonnegative m…

Cited by 25Source ↗Scholar ↗
2015

Efficient multichannel nonnegative matrix factorization exploiting rank-1 spatial model

Daichi Kitamura, Nobutaka Ono, Hiroshi Sawada, Hirokazu Kameoka, Hiroshi Saruwatari
ICASSP 2015accepted

This paper proposes a new efficient multichannel nonnegative matrix factorization (NMF) method. Recently, multichannel NMF (MNMF) has been proposed as a means of solving the blind source separation problem. This method estimates a mixing system of sources and attempts to separate them in a blind fas…

Cited by 0Source ↗Scholar ↗
2015

Statistical modeling of binaural signal and its application to binaural source separation

Yuki Murota, Daichi Kitamura, Shoichi Koyama, Hiroshi Saruwatari, Satoshi Nakamura
ICASSP 2015accepted

This paper addresses a new statistical model of binaural signals and its application to efficient binaural source separation. Binaural source separation is always required to retain a spatial cue of the separated sound, such as a head-related transfer function (HRTF). However, the direct use of an H…

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