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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| EA, SIP, SP, IPSJ-SLP [detail] | 2022-03-01 14:45 |
Okinawa | (Primary: On-site, Secondary: Online) |
Target speaker extraction based on conditional variational autoencoder and directional information in underdetermined condition Rui Wang, Li Li, Tomoki Toda (Nagoya Univ)  EA2021-76 SIP2021-103 SP2021-61 |
This paper deals with a dual-channel target speaker extraction problem in underdetermined conditions. A blind source sep... [more] | EA2021-76 SIP2021-103 SP2021-61 pp.76-81 |
| SR | 2019-01-25 16:40 |
Fukushima | Corasse, Fukushima city (Fukushima prefecture) | A study on widely acceptable model for spectrum usage Kento Yamada, Kenta Umebayashi (TUAT) SR2018-119 |
We investigate a flexible and scalable spectrum usage model in time domain for an enhanced dynamic spectrum access (DSA)... [more] | SR2018-119 pp.141-147 |
| RCS, SR, SRW (Joint) |
2018-03-02 10:50 |
Kanagawa | YRP | Spectrum usage model for Smart Spectrum Access Kento Yamada, Kenta Umebayashi (TUAT), Janne Lehtomaki, Shashika Manosha Kapuruhamy Badalge (Univ. of Oulu) SR2017-133 |
In a smart spectrum access, the statistical information in terms of spectrum usage can enhance a spectrum sharing dramat... [more] | SR2017-133 pp.109-116 |
| PRMU, IE, MI, SIP | 2017-05-26 12:00 |
Aichi | Background Modeling based on Gaussian Mixture Model using Spatial Features Kan Zheng, Toshio Kondo, Yuki Fukazawa, Takahiro Sasaki (Mie Univ.) SIP2017-24 IE2017-24 PRMU2017-24 MI2017-24 |
Many methods for detecting a moving object from surveillance video using a background model have been proposed. Mixed Ga... [more] | SIP2017-24 IE2017-24 PRMU2017-24 MI2017-24 pp.125-130 |
|
| AI | 2017-02-18 15:00 |
Okayama | Prediction Model for Electric Power Demand in Building on Field Data with Extended Goal Graph and Heterogeneous Mixture Modeling Noriyuki Kushiro, Ami Fukuda (KIT), Toshihiro Mega (MELTEC), Takuro Shimizu (KIT) AI2016-31 |
For realizing energy management and demand response in buildings, standardized interfaces, e.g. BACnet and OpenADR, are ... [more] | AI2016-31 pp.39-44 |
|
| EA, SP, SIP | 2016-03-29 10:45 |
Oita | Beppu International Convention Center B-ConPlaza | Tensor-based Speech Representation and its Application to Identification of Languages and Speakers So Suzuki, Daisuke Saito, Nobuaki Minematsu (UTokyo) EA2015-127 SIP2015-176 SP2015-155 |
This paper proposes a novel approach to speech representation for automatic identification of languages and speakers by ... [more] | EA2015-127 SIP2015-176 SP2015-155 pp.341-346 |
| IBISML | 2015-11-26 15:00 |
Ibaraki | Epochal Tsukuba | [Poster Presentation]
Non-parametric e-mixture Estimation Ken Takano (Waseda Univ.), Hideitsu Hino (Univ. of Tsukuba), Shotaro Akaho (AIST), Noboru Murata (Waseda Univ.) IBISML2015-59 |
Mixture modeling is one of the simplest ways to represent complicated probability density functions, and to integrate in... [more] | IBISML2015-59 pp.45-52 |
| SP | 2015-10-15 13:25 |
Hyogo | Kobe Univ. | Statistical singing voice conversion based on direct waveform modification and its parameter generation algorithms Kazuhiro Kobayashi, Tomoki Toda, Satoshi Nakamura (NAIST) SP2015-60 |
This report presents a novel statistical singing voice conversion (SVC) technique with direct waveform modification base... [more] | SP2015-60 pp.7-12 |
| SP | 2014-11-14 10:55 |
Fukuoka | Kyushu Univ. Chikushi Campus | Design of control parameters for voice quality control based on multiple-regression Gaussian mixture model Kazutaka Kubo, Kazuhiro Kobayashi, Tomoki Toda, Graham Neubig, Sakriani Sakti, Satoshi Nakamura (NAIST) SP2014-101 |
This report presents a method for designing control parameters in statistical voice quality control. As a method for int... [more] | SP2014-101 pp.65-70 |
| SP, IPSJ-SLP | 2013-12-20 10:45 |
Tokyo | [Invited Talk]
Acoustic Modeling Using Restricted Boltzmann Machines and Deep Belief Networks for Statistical Parametric Speech Synthesis and Voice Conversion Zhen-Hua Ling, Ling-Hui Chen, Li-Rong Dai (USTC) SP2013-90 |
This paper summarizes our previous work on spectral modeling using restricted Boltzmann machines (RBM) and deep belief n... [more] | SP2013-90 pp.103-108 |
|
| PRMU | 2011-02-18 14:30 |
Saitama | Probabilistic Background Texture Modeling for Object Detection Satoshi Yoshinaga, Atsushi Shimada, Hajime Nagahara, Rin-ichiro Taniguchi (Kyushu Univ.) PRMU2010-232 |
We propose a new method of background modeling, which is considering the background texture, to detect objects. Many bac... [more] | PRMU2010-232 pp.153-158 |
|
| NC, MBE (Joint) |
2010-03-10 13:20 |
Tokyo | Tamagawa University | ARMA Model Based Time Series Clustering Using Dirichlet Process Mixture Models Yuki Washizu, Nobuo Suematsu, Akira Hayashi, Kazunori Iwata (Hiroshima City Univ) NC2009-135 |
Dirichlet Process Mixture (DPM) models allow nonparametric mixture modeling in which the number of mixture components is... [more] | NC2009-135 pp.279-284 |
| SP, NLC | 2009-12-22 15:50 |
Tokyo | Univ. of Tokyo | A study on speech synthesis by modeling harmonics structure with Multi Beta Mixture Model Toru Nakashika (Kobe Univ.), Ryuki Tachibana, Masafumi Nishimura (IBM Japan), Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) NLC2009-26 SP2009-90 |
There are currently some researches related to speech synthesis, but here we present a new framework for speech synthes... [more] |
NLC2009-26 SP2009-90 pp.165-170 |
| SP | 2008-07-17 - 2008-07-19 |
Iwate | Iwate Prefectural Univ. | An Investigation on the speaker Vector-based Speaker Verification Naoki Tadokoro, Masaharu Katoh, Tetsuo Kosaka (Yamagata Univ.) SP2008-46 |
This paper describes the improvement in performance of a text-independent speaker verification based on a speaker vector... [more] | SP2008-46 pp.19-24 |
| PRMU, MI | 2005-05-19 15:00 |
Aichi | Nagoya Institute of Technology | Vehicle Detection using Gaussian Mixture Model from IR Image Nami Hirata, Makito Seki, Haruhisa Okuda, Manabu Hashimoto (Mitsubishi Electric Corp.) |
This paper describes the vehicle detection method using infrared images. To avoid the influences of sunshine changes and... [more] | PRMU2005-7 MI2005-7 pp.37-42 |
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