Kazunobu Kondo — accepted AI-conference 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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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…

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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…

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