Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Takamune, Norihiro"'
Autor:
Seki, Kentaro, Takamichi, Shinnosuke, Takamune, Norihiro, Saito, Yuki, Imamura, Kanami, Saruwatari, Hiroshi
This paper proposes a new task called spatial voice conversion, which aims to convert a target voice while preserving spatial information and non-target signals. Traditional voice conversion methods focus on single-channel waveforms, ignoring the ste
Externí odkaz:
http://arxiv.org/abs/2406.17722
Real-time speech extraction is an important challenge with various applications such as speech recognition in a human-like avatar/robot. In this paper, we propose the real-time extension of a speech extraction method based on independent low-rank mat
Externí odkaz:
http://arxiv.org/abs/2403.12477
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
We propose {\it HumanDiffusion,} a diffusion model trained from humans' perceptual gradients to learn an acceptable range of data for humans (i.e., human-acceptable distribution). Conventional HumanGAN aims to model the human-acceptable distribution
Externí odkaz:
http://arxiv.org/abs/2306.12169
Publikováno v:
European Signal Processing Conference, Sep. 2023, pp. 326--330
In this paper, we propose algorithms for handling non-integer strides in sampling-frequency-independent (SFI) convolutional and transposed convolutional layers. The SFI layers have been developed for handling various sampling frequencies (SFs) by a s
Externí odkaz:
http://arxiv.org/abs/2306.10718
Hyperbolic Timbre Embedding for Musical Instrument Sound Synthesis Based on Variational Autoencoders
Autor:
Nakashima, Futa, Nakamura, Tomohiko, Takamune, Norihiro, Fukayama, Satoru, Saruwatari, Hiroshi
Publikováno v:
2022 Asia Pacific Signal and Information Processing Association Annual Summit and Conference
In this paper, we propose a musical instrument sound synthesis (MISS) method based on a variational autoencoder (VAE) that has a hierarchy-inducing latent space for timbre. VAE-based MISS methods embed an input signal into a low-dimensional latent re
Externí odkaz:
http://arxiv.org/abs/2209.13211
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:
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