Zobrazeno 1 - 10
of 77
pro vyhledávání: '"Daichi Kitamura"'
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2022, Iss 1, Pp 1-24 (2022)
Abstract Rank-constrained spatial covariance matrix estimation (RCSCME) is a blind speech extraction method utilized under the condition that one-directional target speech and diffuse background noise are mixed. In this paper, we propose a new model
Externí odkaz:
https://doaj.org/article/0ec8159c1d2b4faeb38af926e54b8df2
Autor:
Daichi Kitamura, Kohei Yatabe
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2020, Iss 1, Pp 1-35 (2020)
Abstract Independent low-rank matrix analysis (ILRMA) is the state-of-the-art algorithm for blind source separation (BSS) in the determined situation (the number of microphones is greater than or equal to that of source signals). ILRMA achieves a gre
Externí odkaz:
https://doaj.org/article/77314d40d0a6460781fcdbc03a930ba6
Autor:
Daichi Kitamura, Shinichi Mogami, Yoshiki Mitsui, Norihiro Takamune, Hiroshi Saruwatari, Nobutaka Ono, Yu Takahashi, Kazunobu Kondo
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2018, Iss 1, Pp 1-25 (2018)
Abstract In this paper, statistical-model generalizations of independent low-rank matrix analysis (ILRMA) are proposed for achieving high-quality blind source separation (BSS). BSS is a crucial problem in realizing many audio applications, where the
Externí odkaz:
https://doaj.org/article/176ce768efe64862b94e6f5bff39ab22
Autor:
Shoya Kawaguchi, Daichi Kitamura
Publikováno v:
Journal of Signal Processing (1342-6230); Nov2023, Vol. 27 Issue 6, p207-211, 5p
Publikováno v:
Advanced Synthesis & Catalysis.
Publikováno v:
2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC).
Publikováno v:
2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC).
Publikováno v:
Acoustical Science and Technology. 42:222-225
Autor:
Masaya Kawamura, Tomohiko Nakamura, Daichi Kitamura, Hiroshi Saruwatari, Yu Takahashi, Kazunobu Kondo
Publikováno v:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
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
Publikováno v:
IEICE Transactions on Information and Systems. :441-449