Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Kitamura, Daichi"'
Spotforming is a target-speaker extraction technique that uses multiple microphone arrays. This method applies beamforming (BF) to each microphone array, and the common components among the BF outputs are estimated as the target source. This study pr
Externí odkaz:
http://arxiv.org/abs/2407.08951
Autor:
Nishida, Koki, Takamune, Norihiro, Ikeshita, Rintaro, Kitamura, Daichi, Saruwatari, Hiroshi, Nakatani, Tomohiro
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 multichannel nonnegative matrix factorization (FastMNMF) and its variant, we pro
Externí odkaz:
http://arxiv.org/abs/2306.12820
Autor:
Kawamura, Masaya, Nakamura, Tomohiko, Kitamura, Daichi, Saruwatari, Hiroshi, Takahashi, Yu, Kondo, Kazunobu
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
Externí odkaz:
http://arxiv.org/abs/2202.00200
Autor:
Misawa, Sota, Takamune, Norihiro, Nakamura, Tomohiko, Kitamura, Daichi, Saruwatari, Hiroshi, Une, Masakazu, Makino, Shoji
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 RCSCME, independent low-rank matrix analysis (ILRMA) is used as the prepr
Externí odkaz:
http://arxiv.org/abs/2109.04658
Autor:
Hasumi, Takuya, Nakamura, Tomohiko, Takamune, Norihiro, Saruwatari, Hiroshi, Kitamura, Daichi, Takahashi, Yu, Kondo, Kazunobu
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 neural networks (DNNs). The DNN-based power estimation works well for sound
Externí odkaz:
http://arxiv.org/abs/2109.00704
Autor:
Mizobuchi, Yusaku, Kitamura, Daichi, Nakamura, Tomohiko, Saruwatari, Hiroshi, Takahashi, Yu, Kondo, Kazunobu
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 called cross-talk or bleeding sound. For many audio applications includ
Externí odkaz:
http://arxiv.org/abs/2109.00237
Autor:
Narisawa, Naoki, Ikeshita, Rintaro, Takamune, Norihiro, Kitamura, Daichi, Nakamura, Tomohiko, Saruwatari, Hiroshi, Nakatani, Tomohiro
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-frequency correlation of each source spectrum is zero, which is inappropriate
Externí odkaz:
http://arxiv.org/abs/2106.05529
Autor:
Hasumi, Takuya, Nakamura, Tomohiko, Takamune, Norihiro, Saruwatari, Hiroshi, Kitamura, Daichi, Takahashi, Yu, Kondo, Kazunobu
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 on the basis of source independence, using the source model estimated
Externí odkaz:
http://arxiv.org/abs/2106.03492
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 extensi
Externí odkaz:
http://arxiv.org/abs/2105.02491
Autor:
Kitamura, Daichi, Yatabe, Kohei
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 great separa
Externí odkaz:
http://arxiv.org/abs/2007.00274