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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 12 of 12  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
EA, SIP, SP, IPSJ-SLP [detail] 2022-03-01
14:20
Okinawa
(Primary: On-site, Secondary: Online)
Fast Distortion Pedal Modeling with Fine-Tuning
Haruki Shoji, Kento Yoshimoto, Daiki Saka, Hiroki Kuroda, Daichi Kitahara, Kenichiro Tanaka, Akira Hirabayashi (Ritsumeikan Univ.) EA2021-75 SIP2021-102 SP2021-60
We propose a fast modeling method for distortion pedals based on deep learning. For modeling many times with different p... [more] EA2021-75 SIP2021-102 SP2021-60
pp.70-75
EA, US
(Joint)
2021-12-22
13:30
Kumamoto Sojo University [Poster Presentation] Improved voice quality due to multi-speaker learning with WaveNet vocoder
Satoshi Yoshida, Shingo Uenohara, Ken'ichi Furuya (Oita Univ.) EA2021-57
In recent years, speech synthesis and voice quality conversion techniques using neural networks have attracted much atte... [more] EA2021-57
pp.1-6
SP, EA, SIP 2020-03-02
09:20
Okinawa Okinawa Industry Support Center
(Cancelled but technical report was issued)
Investigation of neural speech rate conversion with multi-speaker WaveNet vocoder
Takuma Okamoto (NICT), Keisuke Matsubara (Kobe Univ./NICT), Tomoki Toda (Nagoya Univ./NICT), Yoshinori Shiga, Hisashi Kawai (NICT) EA2019-101 SIP2019-103 SP2019-50
Speech rate conversion technology, which can expand or compress speech waveforms without changing pitch of sound, is con... [more] EA2019-101 SIP2019-103 SP2019-50
pp.1-6
SP, EA, SIP 2020-03-02
13:00
Okinawa Okinawa Industry Support Center
(Cancelled but technical report was issued)
[Poster Presentation] High-precision modeling of distortion stomp box by deep learning using spectral features
Kento Yoshimoto, Daichi Kitahara, Akira Hirabayashi (Ritsumeikan Univ.) EA2019-124 SIP2019-126 SP2019-73
We propose a method for modeling distortion stomp box with high accuracy using a deep neural network, WaveNet. The conve... [more] EA2019-124 SIP2019-126 SP2019-73
pp.135-140
NLC, IPSJ-NL, SP, IPSJ-SLP
(Joint) [detail]
2019-12-06
16:00
Tokyo NHK Science & Technology Research Labs. A comparison of neural vocoders in singing voice synthesis
Sota Wada, Yukiya Hono, Shinji Takaki, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda (Nagoya Inst. of Tech.) SP2019-42
In this study, we compare five types of vocoders based on neural networks (neural vocoders) for singing voice synthesis.... [more] SP2019-42
pp.85-90
SP 2019-08-28
14:40
Kyoto Kyoto Univ. [Poster Presentation] An investigation on training of WaveNet vocoder in end-to-end text-to-speech
Kazuki Yasuhara, Tomoki Hayashi, Tomoki Toda (Nagoya Univ.) SP2019-14
In this paper, we investigate the training of WaveNet vocoder in end-to-end text-to-speech. Tacotron 2, which is an end-... [more] SP2019-14
pp.31-36
SIP, EA, SP, MI
(Joint) [detail]
2018-03-20
09:00
Okinawa   [Poster Presentation] Development of NU Voice Conversion System 2018
Patrick Lumban Tobing, Yi-Chiao Wu, Tomoki Hayashi, Kazuhiro Kobayashi (Nagoya Univ.), Tomoki Toda (Nagoya Univ./JST PRESTO) EA2017-138 SIP2017-147 SP2017-121
This paper presents NU (Nagoya University) voice conversion (VC) system for the HUB task of Voice
Conversion Challenge ... [more]
EA2017-138 SIP2017-147 SP2017-121
pp.203-208
SIP, EA, SP, MI
(Joint) [detail]
2018-03-20
09:00
Okinawa   [Poster Presentation] Do prosodic manual annotations matter for Japanese speech synthesis systems with WaveNet vocoder?
Hieu-Thi Luong, Xin Wang, Junichi Yamagishi (NII), Nobuyuki Nishizawa (KDDI Research) EA2017-140 SIP2017-149 SP2017-123
We investigated the impact of noisy linguistics features on the performance of a Japanese neural net- work based speech ... [more] EA2017-140 SIP2017-149 SP2017-123
pp.215-220
SP, ASJ-H 2018-01-21
14:45
Tokyo The University of Tokyo An investigation of multi-speaker WaveNet vocoder
Tomoki Hayashi, Kazuhiro Kobayashi, Akira Tamamori, Kazuya Takeda, Tomoki Toda (Nagoya Univ.) SP2017-81
In this paper, we investigate a multi-speaker WaveNet vocoder. In our previous work, we have demonstrated that our propo... [more] SP2017-81
pp.81-86
SP, ASJ-H 2018-01-21
15:35
Tokyo The University of Tokyo Mel-cepstrum based quantization noise shaping applied to speech synthesis based on WaveNet
Takenori Yoshimura, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda (Nagoya Inst. of Tech.) SP2017-83
This paper proposes a mel-cepstrum based quantization noise shaping for improving the quality of synthetic speech genera... [more] SP2017-83
pp.93-98
SP, ASJ-H 2018-01-21
16:00
Tokyo The University of Tokyo A study on voice conversion based on WaveNet
Jumpei Niwa, Takenori Yoshimura, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda (NIT) SP2017-84
This paper proposes a voice conversion technique based on WaveNet to directly generate target audio waveforms from acous... [more] SP2017-84
pp.99-104
SP, SIP, EA 2017-03-01
09:20
Okinawa Okinawa Industry Support Center Speech waveform synthesis based on WaveNet considering speech generation process
Akira Tamamori, Tomoki Hayashi, Tomoki Toda, Kazuya Takeda (Nagoya Univ.) EA2016-82 SIP2016-137 SP2016-77
Our aim is to realize a new vocoder, which can resolve various constraints imposed on source-filter model and deal with ... [more] EA2016-82 SIP2016-137 SP2016-77
pp.1-6
 Results 1 - 12 of 12  /   
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