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Technical Committee on Signal Processing (SIP) (Searched in: 2020)
Search Results: Keywords 'from:2020-08-27 to:2020-08-27'
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SIP | 2020-08-27 10:00 |
Online | Online | Signal Processing of Intelligence
-- Hypotheses for Memorization and Networking in the Brain at Protein Level -- Kumon Tokumaru (Writer)  SIP2020-27 |
Digital signal evolution from syllables, a character set to electronic data, enables human beings to network freely with... [more] | SIP2020-27 pp.1-6 |
| SIP | 2020-08-27 10:25 |
Online | Online | [Invited Talk]
An informatics approach to neuromusicology Toshihisa Tanaka (TUAT) SIP2020-28 |
[more] | SIP2020-28 p.7 |
| SIP | 2020-08-27 11:10 |
Online | Online | [Invited Talk]
Recent advances in conversational speech recognition
-- source separation, diarizatoin, and end-to-end speech recognition -- Keisuke Kinoshita, Marc Delcroix (NTT), Thilo von Neumann (PUB), Tomohiro Nakatani, Shoko Araki (NTT) SIP2020-29 |
[more] | SIP2020-29 pp.9-10 |
| SIP | 2020-08-27 13:30 |
Online | Online | [Invited Talk]
Recent Advance of Deep-Unfolded Algorithms for Signal Processing and Wireless Communications Satoshi Takabe (NITech) SIP2020-30 |
In this talk, I will briefly review recent progress of deep unfolding as a promising deep learning technique. A network ... [more] | SIP2020-30 p.11 |
| SIP | 2020-08-27 14:15 |
Online | Online | [Invited Talk]
Wirtinger derivative and complex signal processing Kazunori Hayashi (Kyoto Univ.) SIP2020-31 |
[more] | SIP2020-31 p.13 |
| SIP | 2020-08-27 15:15 |
Online | Online | SIP2020-32 |
[more] | SIP2020-32 p.15 |
| SIP | 2020-08-27 16:00 |
Online | Online | [Invited Talk]
Bringing out Computer Performance on Image Processing Programming Kenjiro Sugimoto (Waseda Univ.) SIP2020-33 |
Along with the complication of computer architecture, more advanced and delicate programming techniques have been requir... [more] | SIP2020-33 p.17 |
| SIP | 2020-08-28 10:30 |
Online | Online | Improvement Convergence Rate of the Sign Algorithm by Natural Gradient Method Taiyo Mineo, Hayaru Shouno (UEC) SIP2020-34 |
In lossless audio compression, it is essential to predictive residuals to be sparse, since we apply entropy codings to r... [more] | SIP2020-34 pp.19-24 |
| SIP | 2020-08-28 10:55 |
Online | Online | Theoretical Analysis on Convergence Acceleration of Deep-Unfolded Gradient Descent Satoshi Takabe, Tadashi Wadayama (NITech) SIP2020-35 |
Deep unfolding is a promising deep learning technique whose network architecture is based on existing iterative algorith... [more] | SIP2020-35 pp.25-30 |
| SIP | 2020-08-28 11:20 |
Online | Online | [Invited Talk]
Blind source separation based on proximal splitting algorithm Kohei Yatabe (Waseda Univ.) SIP2020-36 |
This talk presents blind source separation (BSS) from multi-channel audio signals. BSS is methodology of estimating sepa... [more] | SIP2020-36 p.31 |
| SIP | 2020-08-28 13:30 |
Online | Online | [Invited Talk]
Image smoothing based on L0 gradient regularization and its applications Ryo Matsuoka (Univ. of Kitakyushu) SIP2020-37 |
This talk outlines research on image processing based on L0 gradient regularization that promotes sparseness in the grad... [more] | SIP2020-37 p.33 |
| SIP | 2020-08-28 14:15 |
Online | Online | [Invited Talk]
Matrix rank minimization and maximum likelihood estimation for matrix completion problems Katsumi Konishi (Hosei Univ.) SIP2020-38 |
[more] | SIP2020-38 p.35 |
| SIP | 2020-08-28 15:15 |
Online | Online | [Invited Talk]
Spline Basics and Its Application to Quantile Regression Daichi Kitahara (Ritsumeikan Univ.) SIP2020-39 |
A spline function is a piecewise polynomial which possesses certain-times continuous differentiability at the places whe... [more] | SIP2020-39 pp.37-42 |
| SIP | 2020-08-28 16:00 |
Online | Online | [Invited Talk]
Sampling signals on graphs Yuichi Tanaka (TUAT) SIP2020-40 |
[more] | SIP2020-40 p.43 |
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