no code implementations • 1 Jun 2023 • Joonyong Park, Shinnosuke Takamichi, Tomohiko Nakamura, Kentaro Seki, Detai Xin, Hiroshi Saruwatari
We examine the speech modeling potential of generative spoken language modeling (GSLM), which involves using learned symbols derived from data rather than phonemes for speech analysis and synthesis.
1 code implementation • 29 Nov 2022 • Tomohiko Nakamura, Shinnosuke Takamichi, Naoko Tanji, Satoru Fukayama, Hiroshi Saruwatari
These songs were arranged from out-of-copyright Japanese children's songs and have six voice parts (lead vocal, soprano, alto, tenor, bass, and vocal percussion).
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Vocal ensemble separation
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no code implementations • 27 Sep 2022 • Futa Nakashima, Tomohiko Nakamura, Norihiro Takamune, Satoru Fukayama, Hiroshi Saruwatari
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.
no code implementations • 1 Feb 2022 • Masaya Kawamura, Tomohiko Nakamura, Daichi Kitamura, Hiroshi Saruwatari, Yu Takahashi, Kazunobu Kondo
A differentiable digital signal processing (DDSP) autoencoder is a musical sound synthesizer that combines a deep neural network (DNN) and spectral modeling synthesis.
1 code implementation • 10 May 2021 • Koichi Saito, Tomohiko Nakamura, Kohei Yatabe, Yuma Koizumi, Hiroshi Saruwatari
Audio source separation is often used as preprocessing of various applications, and one of its ultimate goals is to construct a single versatile model capable of dealing with the varieties of audio signals.
1 code implementation • 28 Jan 2020 • Tomohiko Nakamura, Hiroshi Saruwatari
With this belief, focusing on the fact that the DWT has an anti-aliasing filter and the perfect reconstruction property, we design the proposed layers.
1 code implementation • 24 Dec 2015 • Tomohiko Nakamura, Eita Nakamura, Shigeki Sagayama
We confirmed real-time operation of the algorithms with music scores of practical length (around 10000 notes) on a modern laptop and their tracking ability to the input performance within 0. 7 s on average after repeats/skips in clarinet performance data.
1 code implementation • 8 Apr 2014 • Eita Nakamura, Tomohiko Nakamura, Yasuyuki Saito, Nobutaka Ono, Shigeki Sagayama
We present a polyphonic MIDI score-following algorithm capable of following performances with arbitrary repeats and skips, based on a probabilistic model of musical performances.