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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| CAS, CS | 2024-03-14 15:55 |
Okinawa | Residual Noise Removal in of Sound Source Separation Signal by Spectral Replacement Taiga Saito, Kenji Suyama (Tokyo Denki Univ.)  CAS2023-122 CS2023-115 |
Although sound source separation method based on a multiplication of multiple weighted sum circuits has high suppression... [more] | CAS2023-122 CS2023-115 pp.64-69 |
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| SIP, SP, EA, IPSJ-SLP [detail] | 2024-03-01 16:35 |
Okinawa | (Primary: On-site, Secondary: Online) |
Evaluations of Multi-channel Blind Source Separation for Speech Recognition in Car Environments Yutsuki Takeuchi, Natsuki Ueno, Nobutaka Ono (Tokyo Metropolitan Univ.), Takashi Takazawa, Shuhei Shimanoe, Tomoki Tanemura (MIRISE Technologies) EA2023-127 SIP2023-174 SP2023-109 |
In car environments, speech recognition is difficult due to various types of noise. For this issue, speech enhancement b... [more] | EA2023-127 SIP2023-174 SP2023-109 pp.388-393 |
| SP, IPSJ-MUS, IPSJ-SLP [detail] | 2022-06-18 15:00 |
Online | Online | Unsupervised Training of Sequential Neural Beamformer Using Blindly-separated and Non-separated Signals Kohei Saijo, Tetsuji Ogawa (Waseda Univ.) SP2022-25 |
We present an unsupervised training method of the sequential neural beamformer (Seq-NBF) using the separated signals fro... [more] | SP2022-25 pp.110-115 |
| 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 |
| PRMU, IPSJ-CVIM | 2021-03-05 09:45 |
Online | Online | Improved Speech Separation Performance from Monaural Mixed Speech Based on Deep Embedding Network Shaoxiang Dang, Tetsuya Matsumoto, Hiroaki Kudo (Nagoya Univ.), Yoshinori Takeuchi (Daido Univ.) PRMU2020-85 |
Speech separation refers to the separation of utterances in which multiple people are speaking simultaneously. The idea ... [more] | PRMU2020-85 pp.91-96 |
| EA, US, SP, SIP, IPSJ-SLP [detail] | 2021-03-03 13:05 |
Online | Online | [Invited Talk]
* Masahito Togami (LINE) EA2020-64 SIP2020-95 SP2020-29 |
Recently, deep learning based speech source separation has been evolved rapidly. A neural network (NN) is usually learne... [more] | EA2020-64 SIP2020-95 SP2020-29 pp.27-32 |
| EA, US, SP, SIP, IPSJ-SLP [detail] | 2021-03-03 14:05 |
Online | Online | [Poster Presentation]
Noise-robust time-domain speech separation with basis signals for noise Kohei Ozamoto (Tokyo Tech), Koji Iwano (TCU), Kuniaki Uto, Koichi Shinoda (Tokyo Tech) EA2020-70 SIP2020-101 SP2020-35 |
Recently, speech separation using deep learning has been extensively studied. TasNet, a time-domain method that directly... [more] | EA2020-70 SIP2020-101 SP2020-35 pp.63-67 |
| EA, SIP, SP | 2019-03-14 10:25 |
Nagasaki | i+Land nagasaki (Nagasaki-shi) | Blind speech separation based on approximate joint diagonalization utilizing correlation between neighboring frequency bins Taiki Asamizu, Toshihiro Furukawa (TUS) EA2018-100 SIP2018-106 SP2018-62 |
In this paper, we propose a new method that extends the approximate joint diagonalization blind speech separation (BSS).... [more] | EA2018-100 SIP2018-106 SP2018-62 pp.7-12 |
| EA, SIP, SP | 2019-03-15 13:30 |
Nagasaki | i+Land nagasaki (Nagasaki-shi) | [Poster Presentation]
Design and Evaluation of Ladder Denoising Autoencoder for Auditory Speech Feature Extraction of Overlapped Speech Separation Hiroshi Sekiguchi, Yoshiaki Narusue, Hiroyuki Morikawa (Univ. of Tokyo) EA2018-155 SIP2018-161 SP2018-117 |
Primates and mammalian distinguish overlapped speech sounds from one another by recognizing a single sound source whethe... [more] | EA2018-155 SIP2018-161 SP2018-117 pp.329-333 |
| EA, ASJ-H | 2018-08-23 12:55 |
Miyagi | Tohoku Gakuin Univ. | Self-produced speech enhancement and suppression method with wearable air- and body-conductive microphones Moe Takada, Shogo Seki, Tomoki Toda (Nagoya Univ.) EA2018-29 |
This paper presents a self-produced speech enhancement and suppression method for multichannel signals recorded with bot... [more] | EA2018-29 pp.7-12 |
| SP, IPSJ-SLP (Joint) |
2018-07-26 16:15 |
Shizuoka | Sago-Royal-Hotel (Hamamatsu) | Ladder Network Driven from Auditory Computational Model for Multi-talker Speech Separation Hiroshi Sekiguchi, Yoshiaki Narusue, Hiroyuki Morikawa (Univ. of Tokyo) SP2018-18 |
This paper introduces ladder network implementation induced by auditory computational model for multi-talker speech sepa... [more] | SP2018-18 pp.9-13 |
| SIP, EA, SP, MI (Joint) [detail] |
2018-03-19 09:25 |
Okinawa | Stable Estimation Method of Spatial Correlation Matrices for Multi-channel NMF Yuuki Tachioka (Denso IT Lab) EA2017-103 SIP2017-112 SP2017-86 |
Multi-channel non-negative matrix factorization (MNMF) achieves a high sound source separation performance but its initi... [more] | EA2017-103 SIP2017-112 SP2017-86 pp.7-12 |
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| EA | 2018-02-16 13:10 |
Hiroshima | Pref. Univ. Hiroshima | The effect of increasing the number of channels with multi-channel non-negative matrix factorization for noisy speech recognition Takanobu Uramoto (Oita Univ.), Youhei Okato, Toshiyuki Hanazawa (Mitsubishi Electric), Iori Miura, Shingo Uenohara, Ken'ich Furuya (Oita Univ.) EA2017-99 |
Nonnegative Matrix Factorization (NMF) factorizes a non-negative matrix into two non-negative matrices. In the field of ... [more] | EA2017-99 pp.33-38 |
| NLC, IPSJ-NL, SP, IPSJ-SLP (Joint) [detail] |
2017-12-22 11:20 |
Tokyo | Waseda Univ. Green Computing Systems Research Organization | A Sound Source Separation Method for Multiple Person Speech Recognition using Wavelet Analysis Based on Sound Source Position Obtained by Depth Sensor Nobuhiro Uehara, Kazuo Ikeshiro, Hiroki Imamura (Soka Univ.) SP2017-63 |
Recently, voice information guidance systems are used for only one person in operating at a city hall. To realize operat... [more] | SP2017-63 pp.79-83 |
| SIS | 2017-12-14 10:50 |
Tottori | Tottori Prefectural Center for Lifelong Learning | Harmonic Structure Detection in Speech Separation Using Modified DFT Pair Based on ASA Motohiro Ichikawa, Isao Nakanishi (Tottori Univ) SIS2017-34 |
Humans have the ability of cocktail party effect to be able to recognized the target voice from the various conversation... [more] | SIS2017-34 pp.5-9 |
| WIT, SP | 2017-10-19 13:20 |
Fukuoka | Tobata Library of Kyutech (Kitakyushu) | Speech enhancement of utterance while playing with werewolf game "JINRO" based on NMF Shunsuke Kawano, Toru Takahashi (OSU) SP2017-35 WIT2017-31 |
We describe that speech enhancement for natural and multi speaker dialognue. To record natural and multi speaker dialogn... [more] | SP2017-35 WIT2017-31 pp.7-12 |
| SP | 2017-08-30 11:00 |
Kyoto | Kyoto Univ. | [Poster Presentation]
Semi-blind speech separation and enhancement using recurrent neural network Masaya Wake, Yoshiaki Bando, Masato Mimura, Katsutoshi Itoyama, Kazuyoshi Yoshii, Tatsuya Kawahara (Kyoto Univ.) SP2017-22 |
This paper describes a semi-blind speech enhancement method using a neural network. In a human-robot speech interaction... [more] |
SP2017-22 pp.13-18 |
| PRMU, SP | 2017-06-22 14:45 |
Miyagi | Postfiltering of STFT Spectrograms Based on Generative Adversarial Networks Takuhiro Kaneko (NTT), Shinji Takaki (NII), Hirokazu Kameoka (NTT), Junichi Yamagishi (NII) PRMU2017-28 SP2017-4 |
This paper presents postfiltering of short-term Fourier transform (STFT) spectrograms based on Generative Adversarial Ne... [more] | PRMU2017-28 SP2017-4 pp.17-22 |
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| CAS, ICTSSL | 2017-01-26 09:00 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Target Sound Enhancement by Post Processing of Sound Source Separation Naoki Shinohara, Kenji Suyama (Tokyo Denki Univ.) CAS2016-77 ICTSSL2016-31 |
Although several methods have been proposed for sound source separation, a suppression ability of interference sound is ... [more] | CAS2016-77 ICTSSL2016-31 pp.1-6 |
| EA, EMM | 2015-11-12 17:00 |
Kumamoto | Kumamoto Univ. | Noise suppression method for body-conducted soft speech based on external noise monitoring Yusuke Tajiri (NAIST), Tomoki Toda (Nagoya Univ.), Satoshi Nakamura (NAIST) EA2015-31 EMM2015-52 |
As one of the silent speech interfaces, nonaudible murmur (NAM) microphone has been developed for detecting an extremely... [more] | EA2015-31 EMM2015-52 pp.41-46 |
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