IPSJ-JNL
|
|
- ありさ いいはた
- 5 years ago
- Views:
Transcription
1 Vol. 52 No (Dec. 2011) VocaListener 1 1 VocaListener VocaListener 2 VocaListener: A Singing Synthesis System by Mimicking Pitch and Dynamics of User s Singing Tomoyasu Nakano 1 and Masataka Goto 1 This paper presents a singing synthesis system, VocaListener, thatinterac- tively synthesizes a singing voice by mimicking pitch and dynamics of a user s singing voice. Although there is a method to estimate singing synthesis parameters of pitch (F 0 ) and dynamics (power) from a singing voice, it does not adapt to different singing synthesis conditions (e.g., different singing synthesis systems and their singer databases) or singing skill/style modifications. To deal with different conditions, VocaListener repeatedly updates singing synthesis parameters so that the synthesized singing can mimic the user s singing more closely. Moreover, VocaListener has functions to help modify the user s singing by correcting off-pitch phrases or changing vibrato. In an experimental evaluation under two different singing synthesis conditions, mean error values after the iteration were much smaller than the previous approach ) Web 2),3) 4) 7) 8) 10) HMM 11) text-to-speech TTS text-to-singing lyrics-to-singing 12) 13) 12),14) speech-to-singing 1 National Institute of Advanced Industrial Science and Technology (AIST) 3853 c 2011 Information Processing Society of Japan
2 3854 VocaListener 12) 15) VocaListener singing-to-singing Janer 16) VocaListener YAMAHA Vocaloid 10) 2 3 VocaListener YAMAHA Vocaloid 10) lyrics-to-singing 1 Fig. 1 Even if the same parameters are specified, the synthesized results always differ when we change the synthesis conditions. VOCALOID 10) 1 17) ) 2 2 VocaListener
3 3855 VocaListener 3. VocaListener VocaListener-core VocaListener-plus VocaListener-front-end 3 VocaListener 2 16) Fig. 2 Problems of a previous approach 16). VocaListener 2 VocaListener Janer Viterbi 16) 100% Viterbi 1 Viterbi 1 Vocaloid 10) A B VocaListener-front-end Viterbi C D E VocaListener-plus F VocaListener-core G Viterbi /tachidomaru/ H I J VocaListener-front-end K L M N O P VocaListener-front-end VocaListener-plus VocaListenercore 1
4 3856 VocaListener 3 VocaListener Fig. 3 System architecture of VocaListener. 3.1 VocaListener-front-end VocaListener-front-end 44.1 khz 10 msec F 0 [Hz] / Gross Error SWIPE 18) F 0 MIDI f Table 1 List of symbols. F 0 [Hz] f MIDI f d f t f (t) f(t) f n f(t) f (i) (t) i Δf (i) p (t) i PIT Δf (i) s (t) i PBS Δf (i) (t) i p(t) p (t) p(t) p(t) p (i) (t) p m(t) ˆp (i) (t) ɛ ɛ (i) f ɛ (i) p i DYN 64 i DYN i i F 0 f =12 log (1) 440 p(t) N x(t) h(t) N/2 1 ) p(t) = ( (x(t + τ) h(τ)) 2 (2) τ= N/2 N 2, ms h(t) Viterbi MeCab 19) Viterbi short pause
5 3857 VocaListener 2002 monophone HMM 20) MLLR Maximum Likelihood Linear Regression MAP Maximum A Posteriori Probability MLLR-MAP 21) Viterbi MLLR-MAP 16 khz HTK Speech Recognition Toolkit 22) Vocaloid2 10) CV01 CV02 1 VSTi Vocaloid Playback VST Instrument VocaListener-plus VocaListener-plus ) F 0 f(t) f d 127 { } (f(t) g i)2 f d =argmax exp (3) g 2σi 2 t i=0 σ =0.17 f(t) 5Hz 3 F ),25) 4 5Hz 8Hz 26),27) f d 0 F d < 1 { f(t) fd (0 f d < 0.5) f(t) = (4) f(t)+(1 f d ) (0.5 f d < 1) f t f(t) =f(t)+f t (5) f t msec VSTi 1msec 3 FIR 1.8 4
6 3858 VocaListener (6) (7) f(t) 3Hz 4 F 0 f (t) p(t) p (t) 5Hz 8Hz 26),27) r v r s f(t) =r {v s} f(t)+(1 r {v s} ) f (t) (6) p(t) =r {v s} p(t)+(1 r {v s} ) p (t) (7) r v 23) r s r v = r s =1 r v > 1 r s < 1 F 0 28) r s < VocaListener-plus F 0(t) Fig. 4 Examples of F 0(t) adjusted by VocaListener-plus. 3.3 VocaListener-core VocaListener-core 3 VocaListener-plus
7 3859 VocaListener Table 2 2 Singing synthesis parameters and those initial values PIT 8,192 8,191 0 PBS DYN Viterbi Vocaloid2 PIT PBS DYN MIDI DYN MIDI Expression PIT PBS DYN 2 PIT PBS PBS 1 ±1 16, DYN Viterbi Step 1) 1 Viterbi Step 2) 2 5 VocaListener-core Fig. 5 The lyrics alingment procedure of VocaListener-core. Step 3) Step 4) Step 2) Step 4) MFCC MFCC
8 3860 VocaListener 6 F 0 Fig. 6 F 0 of the target singing and estimated note numbers (1) F 0 PIT PBS ±2 PBS PBS F 0 f n 6 ( (n f n =argmax exp { }) f(t))2 (8) n 2σ 2 t 1 σ =0.33 t (2) f (i) (t) f(t) PIT PBS t i PIT PBS Δf (i) p Step 1) Step 2) f (i) (t) (t) Δf s (i) (t) Step 3) f(t) Δf (i) (t) 7 4 DYN Fig. 7 Power of the target singing and power of the singing synthesized with four different dynamics. Δf (i+1) (t) =Δf (i) (t)+ ( f(t) f (i) (t) ) (9) Δf (i) (t) PIT PBS MIDI 1 Δf (i) (t) = Step 4) Δf (i+1) (t) Δf (i+1) s (i) Δf p (t) Δf s (i) (t) (10) 8192 (t) Δf (i+1) p (t) Δf s (i+1) (t) (1) α 7 DYN DYN DYN = A 1 Δf (i) (t) F 0
9 3861 VocaListener 7 A p(t) DYN 64 p m(t) α ɛ 2 = (α p(t) p m(t)) 2 (11) t α (p(t) pm(t)) t α = (12) t p(t)2 Table 3 3 A B Dataset for experiment A and B and synthesis conditions. All of the song samples were sung by female singers. [sec] A No CV01 A No CV02 B No CV01,02 B No CV01,02 B No CV01,02 B No CV01,02 RWC-MDB-P (2) α DYN DYN DYN = (0, 32, 64, 96, 127) t i DYN ˆp (i) (t) DYN p (i) (t) Step 1) Step 2) p (i) (t) Step 3) ˆp (i) (t) ˆp (i+1) (t) =ˆp (i) (t)+ ( α p(t) p (i) (t) ) (13) Step 4) ˆp (i+1) (t) DYN DYN 4. VocaListener-core 4.1 VocaListener-core A B 2 RWC RWC-MDB-P ) Vocaloid2 0% CV01 CV02 A B 1 i ɛ (i) f ɛ (i) p ɛ (i) f = 1 f(t) f (i) (t) (14) T f ɛ (i) p t = 1 20 log (α p(t)) 20 log ( p (i) (t) ) (15) T p t 0 T f T p 0 B
10 3862 VocaListener 4 A Table 4 Number of boundary errors and number of repairs for correcting (pointing out) errors in experiment A. n n =0 n =1 n =2 n =3 No.07 CV No.16 CV VocaListener-core 2 A B A VocaListener-front-end Viterbi No.07 No.16 2 A 4 No /w/ /r/ /m/ /n/ B 5 No.07 VocaListener i = i =0 i =0 4 i = Janer 16) 4 No No No n [%] B No.07 Table 5 Mean error values after each iteration for song No.07 in experiment B. ɛ (i) [semitone] ɛ (i) f p [db] VocaListener i i =0 i =1 i =2 i =3 i =4 CV CV CV CV B Table 6 Minimum and maximum error values for all four songs in experiment B. VocaListener i i =0 i = HMM VocaListener 1 2
11 3863 VocaListener C++ GUI Visual Studio 2005 GUI A F 0 9 B 9 C wav 8 Fig. 8 The estimated parameters and synthesized results. Web CV01 CV02 5. VocaListener 3 VocaListener D Vocaloid/Vocaloid F 0 A C E F 0 1
12 3864 VocaListener 5.2 Vocaloid2 Score Editor 10) 2 i) F 0 ii) 9 VocaListener Fig. 9 An example VocaListener screen. A B VocaListener VocaListener 1 VocaListener
13 3865 VocaListener 1 30),31) VocaListener 32) VocaListener-plus VocaListener-plus HMM singing-to-singing 1 CrestMuse CV01 CV02 RWC RWC-MDB-P ) Cabinet Office, Government of Japan: Virtual Idol, Highlighting JAPAN through images, Vol.2, No.11, pp (2009), available from img/vol 0020et/24-25.pdf. 2) Vol.25, No.1, pp (2010). 3) 2009 pp (2009). 4) Depalle, P., Garcia, G. and Rodet, X.: A virtual castrato, Proc. International Computer Music Conference (ICMC 94 ), pp (1994). 5) Cook, P.R.: Identification of Control Parameters in An Articulatory Vocal Tract Model, with Applications to the Synthesis of Singing, Ph.D. Thesis, Stanford Univ. (1991). 6) Cook, P.R.: Singing Voice Synthesis: History, Current Work, and Future Directions, Computer Music Journal, Vol.20, No.3, pp (1996). 7) Sundberg, J.: The KTH Synthesis of Singing, Advances in Cognitive Psychology, Special issue on Music Performance, Vol.2, pp (2006). 8) CyberSingers 99-SLP-25-8 Vol.99, No.14, pp (1998). 9) Bonada, J. and Xavier, S.: Synthesis of the Singing Voice by Performance Sampling and Spectral Models, IEEE Signal Processing Magazine, Vol.24, No.2, pp (2007).
14 3866 VocaListener 10) Kenmochi, H. and Ohshita, H.: VOCALOID Commercial Singing Synthesizer based on Sample Concatenation, Proc. 8th Annual Conference of the International Speech Communication Association (INTERSPEECH 2007 ), pp (2007). 11) Vol.45, No.7, pp (2004). 12) Saitou, T., Goto, M., Unoki, M. and Akagi, M.: Speech-To-Singing Synthesis: Converting Speaking Voices to Singing Voices by Controlling Acoustic Features Unique to Singing Voices, Proc IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA2007 ), pp (2007). 13) Fukayama, S., Nakatsuma, K., Sako, S., Nishimoto, T. and Sagayama, S.: Automatic Song Composition from the Lyrics Exploiting Prosody of the Japanese Language, Proc. 7th Sound and Music Computing Conference (SMC2010 ), pp (2010). 14) 2008-MUS-74-6 Vol.2008, No.12, pp (2008). 15) STRAIGHT Vol.43, No.2, pp (2002). 16) Janer, J., Bonada, J. and Blaauw, M.: Performance-driven Control for Sample- Based Singing Voice Synthesis, Proc. 9th Int. Conference on Digital Audio Effects (DAFx-06 ), pp (2006). 17) VOCALOID 2008-MUS-74-9 Vol.2008, No.12, pp (2008). 18) Camacho, A.: SWIPE: A Sawtooth Waveform Inspired Pitch Estimator for Speech And Music, Ph.D. Thesis, University of Florida (2007). 19) MeCab: Yet Another Part-of-Speech and Morphological Analyzer 20) SLP-48-1 Vol.2003, No.48, pp.1 6 (2003). 21) Digalakis, V. and Neumeyer, L.: Speaker Adaptation Using Combined Transformation and Bayesian Methods, IEEE Trans. Speech and Audio Processing, Vol.4, No.4, pp (1996). 22) Young, S., Evermann, G., Hain, T., Kershaw, D., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V. and Woodland, P.: The HTK Book (2002). 23) Vol.48, No.1, pp (2007). 24) Saitou, T., Unoki, M. and Akagi, M.: Development of an F0 Control Model Based on F0 Dynamic Characteristics for Singing-Voice Synthesis, Speech Communication, Vol.46, pp (2005). 25) Mori, H., Odagiri, W. and Kasuya, H.: F 0 Dynamics in Singing: Evidence from the Data of a Baritone Singer, IEICE Trans. Inf. & Syst., Vol.E87-D, No.5, pp (2004). 26) Seashore, C.E.: A Musical Ornament, the Vibrato, Psychology of Music, pp.33 52, McGraw-Hill (1938). 27) STRAIGHT P-15 pp (2005). 28) H , pp (2006). 29) RWC Vol.45, No.3, pp (2004). 30) Toda, T., Black, A. and Tokuda, K.: Voice Conversion Based on Maximum Likelihood Estimation of Spectral Parameter Trajectory, IEEE Trans. Audio, Speech and Language Processing, Vol.15, No.8, pp (2007). 31) STRAIGHT Vol.J91-D, No.4, pp (2008). 32) Nakano, T., Ogata, J., Goto, M. and Hiraga, Y.: Analysis and Automatic Detection of Breath Sounds in Unaccompanied Singing Voice, Proc. 10th International Conference of Music Perception and Cognition (ICMPC 10 ), pp (2008). ( ) ( )
15 3867 VocaListener IPA IT 25
7) 8) 9),10) 11) 18) 11),16) 18) 19) 20) Vocaloid 6) Vocaloid 1 VocaListener1 2 VocaListener1 3 VocaListener VocaListener1 VocaListener1 Voca
VocaListener2: 1 1 VocaListener2 VocaListener VocaListener2 VocaListener2 VocaListener VocaListener2 VocaListener2: A Singing Synthesis System Mimicking Voice Timbre Changes in Addition to Pitch and Dynamics
More informationlog F0 意識 しゃべり 葉の log F0 Fig. 1 1 An example of classification of substyles of rap. ' & 2. 4) m.o.v.e 5) motsu motsu (1) (2) (3) (4) (1) (2) mot
1. 1 2 1 3 2 HMM Rap-style Singing Voice Synthesis Keijiro Saino, 1 Keiichiro Oura, 2 Makoto Tachibana, 1 Hieki Kenmochi 3 an Keiichi Tokua 2 This paper aresses rap-style singing voice synthesis. Since
More informationIPSJ SIG Technical Report Vol.2012-MUS-96 No /8/10 MIDI Modeling Performance Indeterminacies for Polyphonic Midi Score Following and
MIDI 1 2 3 2 1 Modeling Performance Indeterminacies for Polyphonic Midi Score Following and Its Application to Automatic Accompaniment Nakamura Eita 1 Yamamoto Ryuichi 2 Saito Yasuyuki 3 Sako Shinji 2
More informationThe copyright of this material is retained by the Information Processing Society of Japan (IPSJ). The material has been made available on the website
The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). The material has been made available on the website by the author(s) under the agreement with the IPSJ.
More information1911 F0 5) SingBySpeaking F0 F0 F0 4 F0 2. F0 4) 5) rate extent 6) rate 5.6 [Hz] extent 87 [cent] F0 5.2 [%] F0 SingBySpeaking 7) F0 Fig. 1 1 F0 F0 co
Vol. 52 No. 5 1910 1922 (May 2011) This paper describes the details of singing database for analyzing the differences of musical expressions ( and portamento) among professional singers and the effective
More informationVol. 43 No. 2 Feb. 2002,, MIDI A Probabilistic-model-based Quantization Method for Estimating the Position of Onset Time in a Score Masatoshi Hamanaka
Vol. 43 No. 2 Feb. 2002,, MIDI A Probabilistic-model-based Quantization Method for Estimating the Position of Onset Time in a Score Masatoshi Hamanaka, Masataka Goto,, Hideki Asoh and Nobuyuki Otsu, This
More information2). 3) 4) 1.2 NICTNICT DCRA Dihedral Corner Reflector micro-arraysdcra DCRA DCRA DCRA 3D DCRA PC USB PC PC ON / OFF Velleman K8055 K8055 K8055
1 1 1 2 DCRA 1. 1.1 1) 1 Tactile Interface with Air Jets for Floating Images Aya Higuchi, 1 Nomin, 1 Sandor Markon 1 and Satoshi Maekawa 2 The new optical device DCRA can display floating images in free
More informationIPSJ-SLP
F0 MFCC 1 2 3 1 1 1 1 MFCCF0 1 86.7% 90.2% A System for Automatic Discrimination between Singing and Speaking Voices on the Basis of Peak Interval of Spectral Change, F0, and MFCC Shimpei Aso, 1 Takeshi
More information2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server
a) Change Detection Using Joint Intensity Histogram Yasuyo KITA a) 2 (0 255) (I 1 (x),i 2 (x)) I 2 = CI 1 (C>0) (I 1,I 2 ) (I 1,I 2 ) 2 1. [1] 2 [2] [3] [5] [6] [8] Intelligent Systems Research Institute,
More information10_08.dvi
476 67 10 2011 pp. 476 481 * 43.72.+q 1. MOS Mean Opinion Score ITU-T P.835 [1] [2] [3] Subjective and objective quality evaluation of noisereduced speech. Takeshi Yamada, Shoji Makino and Nobuhiko Kitawaki
More informationIPSJ SIG Technical Report Vol.2011-MUS-91 No /7/ , 3 1 Design and Implementation on a System for Learning Songs by Presenting Musical St
1 2 1, 3 1 Design and Implementation on a System for Learning Songs by Presenting Musical Structures based on Phrase Similarity Yuma Ito, 1 Yoshinari Takegawa, 2 Tsutomu Terada 1, 3 and Masahiko Tsukamoto
More information& Vol.5 No (Oct. 2015) TV 1,2,a) , Augmented TV TV AR Augmented Reality 3DCG TV Estimation of TV Screen Position and Ro
TV 1,2,a) 1 2 2015 1 26, 2015 5 21 Augmented TV TV AR Augmented Reality 3DCG TV Estimation of TV Screen Position and Rotation Using Mobile Device Hiroyuki Kawakita 1,2,a) Toshio Nakagawa 1 Makoto Sato
More information1 Fig. 1 Extraction of motion,.,,, 4,,, 3., 1, 2. 2.,. CHLAC,. 2.1,. (256 ).,., CHLAC. CHLAC, HLAC. 2.3 (HLAC ) r,.,. HLAC. N. 2 HLAC Fig. 2
CHLAC 1 2 3 3,. (CHLAC), 1).,.,, CHLAC,.,. Suspicious Behavior Detection based on CHLAC Method Hideaki Imanishi, 1 Toyohiro Hayashi, 2 Shuichi Enokida 3 and Toshiaki Ejima 3 We have proposed a method for
More information11 22 33 12 23 1 2 3, 1 2, U2 3 U 1 U b 1 (o t ) b 2 (o t ) b 3 (o t ), 3 b (o t ) MULTI-SPEAKER SPEECH DATABASE Training Speech Analysis Mel-Cepstrum, logf0 /context1/ /context2/... Context Dependent
More information動画コンテンツ 動画 1 動画 2 動画 3 生成中の映像 入力音楽 選択された素片 テンポによる伸縮 音楽的構造 A B B B B B A C C : 4) 6) Web Web 2 2 c 2009 Information Processing S
1 2 2 1 Web An Automatic Music Video Creation System by Reusing Dance Video Content Sora Murofushi, 1 Tomoyasu Nakano, 2 Masataka Goto 2 and Shigeo Morishima 1 This paper presents a system that automatically
More informationIPSJ SIG Technical Report Vol.2010-NL-199 No /11/ treebank ( ) KWIC /MeCab / Morphological and Dependency Structure Annotated Corp
1. 1 1 1 2 treebank ( ) KWIC /MeCab / Morphological and Dependency Structure Annotated Corpus Management Tool: ChaKi Yuji Matsumoto, 1 Masayuki Asahara, 1 Masakazu Iwatate 1 and Toshio Morita 2 This paper
More information音響モデル triphone 入力音声 音声分析 デコーダ 言語モデル N-gram bigram HMM の状態確率として利用 出力層 triphone: 3003 ノード リスコア trigram 隠れ層 2048 ノード X7 層 1 Structure of recognition syst
1,a) 1 1 1 deep neural netowrk(dnn) (HMM) () GMM-HMM 2 3 (CSJ) 1. DNN [6]. GPGPU HMM DNN HMM () [7]. [8] [1][2][3] GMM-HMM Gaussian mixture HMM(GMM- HMM) MAP MLLR [4] [3] DNN 1 1 triphone bigram [5]. 2
More information[2] OCR [3], [4] [5] [6] [4], [7] [8], [9] 1 [10] Fig. 1 Current arrangement and size of ruby. 2 Fig. 2 Typography combined with printing
1,a) 1,b) 1,c) 2012 11 8 2012 12 18, 2013 1 27 WEB Ruby Removal Filters Using Genetic Programming for Early-modern Japanese Printed Books Taeka Awazu 1,a) Masami Takata 1,b) Kazuki Joe 1,c) Received: November
More information( ) [1] [4] ( ) 2. [5] [6] Piano Tutor[7] [1], [2], [8], [9] Radiobaton[10] Two Finger Piano[11] Coloring-in Piano[12] ism[13] MIDI MIDI 1 Fig. 1 Syst
情報処理学会インタラクション 2015 IPSJ Interaction 2015 15INT014 2015/3/7 1,a) 1,b) 1,c) Design and Implementation of a Piano Learning Support System Considering Motivation Fukuya Yuto 1,a) Takegawa Yoshinari 1,b) Yanagi
More information258 5) GPS 1 GPS 6) GPS DP 7) 8) 10) GPS GPS 2 3 4 5 2. 2.1 3 1) GPS Global Positioning System
Vol. 52 No. 1 257 268 (Jan. 2011) 1 2, 1 1 measurement. In this paper, a dynamic road map making system is proposed. The proposition system uses probe-cars which has an in-vehicle camera and a GPS receiver.
More informationsigmus201007_fujihara.dvi
1 1 1) W-PST W-PST W-PST W-PST Singing voice conversion method by using spectral envelope of singing voice estimated from polyphonic music Hiromasa Fujihara 1 and Masataka Goto 1 This paper describes a
More informationDPA,, ShareLog 3) 4) 2.2 Strino Strino STRain-based user Interface with tacticle of elastic Natural ObjectsStrino 1 Strino ) PC Log-Log (2007 6)
1 2 1 3 Experimental Evaluation of Convenient Strain Measurement Using a Magnet for Digital Public Art Junghyun Kim, 1 Makoto Iida, 2 Takeshi Naemura 1 and Hiroyuki Ota 3 We present a basic technology
More informationIPSJ SIG Technical Report 1, Instrument Separation in Reverberant Environments Using Crystal Microphone Arrays Nobutaka ITO, 1, 2 Yu KITANO, 1
1, 2 1 1 1 Instrument Separation in Reverberant Environments Using Crystal Microphone Arrays Nobutaka ITO, 1, 2 Yu KITANO, 1 Nobutaka ONO 1 and Shigeki SAGAYAMA 1 This paper deals with instrument separation
More informationID 3) 9 4) 5) ID 2 ID 2 ID 2 Bluetooth ID 2 SRCid1 DSTid2 2 id1 id2 ID SRC DST SRC 2 2 ID 2 2 QR 6) 8) 6) QR QR QR QR
Vol. 51 No. 11 2081 2088 (Nov. 2010) 2 1 1 1 which appended specific characters to the information such as identification to avoid parity check errors, before QR Code encoding with the structured append
More information1 UD Fig. 1 Concept of UD tourist information system. 1 ()KDDI UD 7) ) UD c 2010 Information Processing S
UD 1 2 3 4 1 UD UD UD 2008 2009 Development and Evaluation of UD Tourist Information System Using Mobile Phone to Heritage Park HISASHI ICHIKAWA, 1 HIROYUKI FUKUOKA, 2 YASUNORI OSHIDA, 3 TORU KANO 4 and
More information4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q
x-means 1 2 2 x-means, x-means k-means Bayesian Information Criterion BIC Watershed x-means Moving Object Extraction Using the Number of Clusters Determined by X-means Clustering Naoki Kubo, 1 Kousuke
More informationTHE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. E-mail: {ytamura,takai,tkato,tm}@vision.kuee.kyoto-u.ac.jp Abstract Current Wave Pattern Analysis for Anomaly
More information1 1 CodeDrummer CodeMusician CodeDrummer Fig. 1 Overview of proposal system c
CodeDrummer: 1 2 3 1 CodeDrummer: Sonification Methods of Function Calls in Program Execution Kazuya Sato, 1 Shigeyuki Hirai, 2 Kazutaka Maruyama 3 and Minoru Terada 1 We propose a program sonification
More informationFig. 3 Flow diagram of image processing. Black rectangle in the photo indicates the processing area (128 x 32 pixels).
Fig. 1 The scheme of glottal area as a function of time Fig. 3 Flow diagram of image processing. Black rectangle in the photo indicates the processing area (128 x 32 pixels). Fig, 4 Parametric representation
More information28 Horizontal angle correction using straight line detection in an equirectangular image
28 Horizontal angle correction using straight line detection in an equirectangular image 1170283 2017 3 1 2 i Abstract Horizontal angle correction using straight line detection in an equirectangular image
More informationIPSJ SIG Technical Report Vol.2014-MUS-104 No /8/27 F0 1,a) 1,b) 1,c) 2,d) (F0) F0 F0 Graphical User Interface (GUI) F0 1. [1] CD MIDI [2] [3,
F,a),b),c) 2,d) (F) F F Graphical User Interface (GUI) F. [] CD MIDI [2] [3, 4] [5] 2 a) ikemiya@kuis.kyoto-u.ac.jp b) itoyama@kuis.kyoto-u.ac.jp c) yoshii@kuis.kyoto-u.ac.jp d) okuno@aoni.waseda.jp TANDEM-STRAIGHT
More information2007-Kanai-paper.dvi
19 Estimation of Sound Source Zone using The Arrival Time Interval 1080351 2008 3 7 S/N 2 2 2 i Abstract Estimation of Sound Source Zone using The Arrival Time Interval Koichiro Kanai The microphone array
More informationgengo.dvi
4 97.52% tri-gram 92.76% 98.49% : Japanese word segmentation by Adaboost using the decision list as the weak learner Hiroyuki Shinnou In this paper, we propose the new method of Japanese word segmentation
More informationStudies of Foot Form for Footwear Design (Part 9) : Characteristics of the Foot Form of Young and Elder Women Based on their Sizes of Ball Joint Girth
Studies of Foot Form for Footwear Design (Part 9) : Characteristics of the Foot Form of Young and Elder Women Based on their Sizes of Ball Joint Girth and Foot Breadth Akiko Yamamoto Fukuoka Women's University,
More informationIPSJ SIG Technical Report Vol.2017-MUS-116 No /8/24 MachineDancing: 1,a) 1,b) 3 MachineDancing MachineDancing MachineDancing 1 MachineDan
MachineDancing: 1,a) 1,b) 3 MachineDancing 2 1. 3 MachineDancing MachineDancing 1 MachineDancing MachineDancing [1] 1 305 0058 1-1-1 a) s.fukayama@aist.go.jp b) m.goto@aist.go.jp 1 MachineDancing 3 CG
More informationVol.53 No (Mar. 2012) 1, 1,a) 1, 2 1 1, , Musical Interaction System Based on Stage Metaphor Seiko Myojin 1, 1,a
1, 1,a) 1, 2 1 1, 3 2 1 2011 6 17, 2011 12 16 Musical Interaction System Based on Stage Metaphor Seiko Myojin 1, 1,a) Kazuki Kanamori 1, 2 Mie Nakatani 1 Hirokazu Kato 1, 3 Sanae H. Wake 2 Shogo Nishida
More informationIPSJ SIG Technical Report Vol.2009-DPS-141 No.20 Vol.2009-GN-73 No.20 Vol.2009-EIP-46 No /11/27 1. MIERUKEN 1 2 MIERUKEN MIERUKEN MIERUKEN: Spe
1. MIERUKEN 1 2 MIERUKEN MIERUKEN MIERUKEN: Speech Visualization System Based on Augmented Reality Yuichiro Nagano 1 and Takashi Yoshino 2 As the spread of the Augmented Reality(AR) technology and service,
More informationVol.54 No (July 2013) [9] [10] [11] [12], [13] 1 Fig. 1 Flowchart of the proposed system. c 2013 Information
Vol.54 No.7 1937 1950 (July 2013) 1,a) 2012 11 1, 2013 4 5 1 Similar Sounds Sentences Generator Based on Morphological Analysis Manner and Low Class Words Masaaki Kanakubo 1,a) Received: November 1, 2012,
More informationIPSJ SIG Technical Report Vol.2017-MUS-115 No /6/17 1,a) 1 1 WORLD F0 Vocaloid F0 ipad 1. Vocaloid [1] UTAU *1 Vocaloid Vocaloid F0 VocaListene
1,a) 1 1 WORLD F0 Vocaloid F0 ipad 1. Vocaloid [1] UTAU *1 Vocaloid Vocaloid F0 VocaListener [2], [3] Vocaloid *2 VocaListener Vocaloid 1 University of Yamanashi a) g16tk018@yamanashi.ac.jp *1 http://utau2008.web.fc2.com/
More informationVol. 48 No. 3 Mar Evaluation of Music-noise Assimilation Playback for Portable Audio Players Akifumi Inoue, Shohei Bise, Satoshi Ichimura and
Vol. 48 No. 3 Mar. 2007 1 Evaluation of Music-noise Assimilation Playback for Portable Audio Players Akifumi Inoue, Shohei Bise, Satoshi Ichimura and Yutaka Matsushita Though the population of portable
More information第62巻 第1号 平成24年4月/石こうを用いた木材ペレット
Bulletin of Japan Association for Fire Science and Engineering Vol. 62. No. 1 (2012) Development of Two-Dimensional Simple Simulation Model and Evaluation of Discharge Ability for Water Discharge of Firefighting
More informationIPSJ SIG Technical Report Pitman-Yor 1 1 Pitman-Yor n-gram A proposal of the melody generation method using hierarchical pitman-yor language model Aki
Pitman-Yor Pitman-Yor n-gram A proposal of the melody generation method using hierarchical pitman-yor language model Akira Shirai and Tadahiro Taniguchi Although a lot of melody generation method has been
More information1., 1 COOKPAD 2, Web.,,,,,,.,, [1]., 5.,, [2].,,.,.,, 5, [3].,,,.,, [4], 33,.,,.,,.. 2.,, 3.., 4., 5., ,. 1.,,., 2.,. 1,,
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.,, 464 8601 470 0393 101 464 8601 E-mail: matsunagah@murase.m.is.nagoya-u.ac.jp, {ide,murase,hirayama}@is.nagoya-u.ac.jp,
More informationDT pdf
131 71 71 71 71 71 7 1 71 71 71 71 71 71 71 7 1 71 71 71 71 71 71 71 71 71 71 7 1 71 71 71 71 7 1 71 71 71 71 71 71 71 71 71 71 71 7 1 71 71 71 71 71 71 71 71 7 1 71 71 7 1 71 71 71 71 71 71 71 71 7 1
More informationTCP/IP IEEE Bluetooth LAN TCP TCP BEC FEC M T M R M T 2. 2 [5] AODV [4]DSR [3] 1 MS 100m 5 /100m 2 MD 2 c 2009 Information Processing Society of
IEEE802.11 [1]Bluetooth [2] 1 1 (1) [6] Ack (Ack) BEC FEC (BEC) BEC FEC 100 20 BEC FEC 6.19% 14.1% High Throughput and Highly Reliable Transmission in MANET Masaaki Kosugi 1 and Hiroaki Higaki 1 1. LAN
More information2006 [3] Scratch Squeak PEN [4] PenFlowchart 2 3 PenFlowchart 4 PenFlowchart PEN xdncl PEN [5] PEN xdncl DNCL 1 1 [6] 1 PEN Fig. 1 The PEN
PenFlowchart 1,a) 2,b) 3,c) 2015 3 4 2015 5 12, 2015 9 5 PEN & PenFlowchart PEN Evaluation of the Effectiveness of Programming Education with Flowcharts Using PenFlowchart Wataru Nakanishi 1,a) Takeo Tatsumi
More informationIPSJ SIG Technical Report Secret Tap Secret Tap Secret Flick 1 An Examination of Icon-based User Authentication Method Using Flick Input for
1 2 3 3 1 Secret Tap Secret Tap Secret Flick 1 An Examination of Icon-based User Authentication Method Using Flick Input for Mobile Terminals Kaoru Wasai 1 Fumio Sugai 2 Yosihiro Kita 3 Mi RangPark 3 Naonobu
More informationVol.55 No (Jan. 2014) saccess 6 saccess 7 saccess 2. [3] p.33 * B (A) (B) (C) (D) (E) (F) *1 [3], [4] Web PDF a m
Vol.55 No.1 2 15 (Jan. 2014) 1,a) 2,3,b) 4,3,c) 3,d) 2013 3 18, 2013 10 9 saccess 1 1 saccess saccess Design and Implementation of an Online Tool for Database Education Hiroyuki Nagataki 1,a) Yoshiaki
More informationInput image Initialize variables Loop for period of oscillation Update height map Make shade image Change property of image Output image Change time L
1,a) 1,b) 1/f β Generation Method of Animation from Pictures with Natural Flicker Abstract: Some methods to create animation automatically from one picture have been proposed. There is a method that gives
More information(255) Vol. 19 No. 4 July (completion) tcsh bash UNIX Emacs/Mule 2 ( ) [2] [9] [11] 2 (speech completion) 3 ( ) [7] 2 ( 7.1 )
10 (254) () 1 Speech Completion: Introducing New Modality into Speech Input Interface Masataka Goto, Katunobu Itou, Tomoyosi Akiba, Satoru Hayamizu, [ ], National Institute of Advanced Industrial Science
More informationTF-IDF TDF-IDF TDF-IDF Extracting Impression of Sightseeing Spots from Blogs for Supporting Selection of Spots to Visit in Travel Sat
1 1 2 1. TF-IDF TDF-IDF TDF-IDF. 3 18 6 Extracting Impression of Sightseeing Spots from Blogs for Supporting Selection of Spots to Visit in Travel Satoshi Date, 1 Teruaki Kitasuka, 1 Tsuyoshi Itokawa 2
More informationpaper.dvi
59 6 2003 pp. 1 11 1 43.72.Kb * 1 2 3 1. 2 2 1 1 1 [1] Person Recognition for News Videos through Multimodal Interaction, by Masakiyo Fujimoto, Yasuo Ariki and Shuji Doshita. 1 ATR 2 3 masakiyo.fujimoto@atr.jp
More informationA Feasibility Study of Direct-Mapping-Type Parallel Processing Method to Solve Linear Equations in Load Flow Calculations Hiroaki Inayoshi, Non-member
A Feasibility Study of Direct-Mapping-Type Parallel Processing Method to Solve Linear Equations in Load Flow Calculations Hiroaki Inayoshi, Non-member (University of Tsukuba), Yasuharu Ohsawa, Member (Kobe
More informationMOTIF XF 取扱説明書
MUSIC PRODUCTION SYNTHESIZER JA 2 (7)-1 1/3 3 (7)-1 2/3 4 (7)-1 3/3 5 http://www.adobe.com/jp/products/reader/ 6 NOTE http://japan.steinberg.net/ http://japan.steinberg.net/ 7 8 9 A-1 B-1 C0 D0 E0 F0 G0
More informationThe Japanese Journal of Experimental Social Psychology. 2002, Vol. 41, No. 2, 155-164 V. 1986 An introduction to human memory. Routledge & Kegan Paul.) Hay, D. C., & Young, A. W. 1982 The human
More information1 Web [2] Web [3] [4] [5], [6] [7] [8] S.W. [9] 3. MeetingShelf Web MeetingShelf MeetingShelf (1) (2) (3) (4) (5) Web MeetingShelf
1,a) 2,b) 4,c) 3,d) 4,e) Web A Review Supporting System for Whiteboard Logging Movies Based on Notes Timeline Taniguchi Yoshihide 1,a) Horiguchi Satoshi 2,b) Inoue Akifumi 4,c) Igaki Hiroshi 3,d) Hoshi
More informationVisual Evaluation of Polka-dot Patterns Yoojin LEE and Nobuko NARUSE * Granduate School of Bunka Women's University, and * Faculty of Fashion Science,
Visual Evaluation of Polka-dot Patterns Yoojin LEE and Nobuko NARUSE * Granduate School of Bunka Women's University, and * Faculty of Fashion Science, Bunka Women's University, Shibuya-ku, Tokyo 151-8523
More information, (GPS: Global Positioning Systemg),.,, (LBS: Local Based Services).. GPS,.,. RFID LAN,.,.,.,,,.,..,.,.,,, i
25 Estimation scheme of indoor positioning using difference of times which chirp signals arrive 114348 214 3 6 , (GPS: Global Positioning Systemg),.,, (LBS: Local Based Services).. GPS,.,. RFID LAN,.,.,.,,,.,..,.,.,,,
More information1 4 4 [3] SNS 5 SNS , ,000 [2] c 2013 Information Processing Society of Japan
SNS 1,a) 2 3 3 2012 3 30, 2012 10 10 SNS SNS Development of Firefighting Knowledge Succession Support SNS in Tokyo Fire Department Koutarou Ohno 1,a) Yuki Ogawa 2 Hirohiko Suwa 3 Toshizumi Ohta 3 Received:
More informationsigmusdemo.dvi
V IT Demonstrations: Introduction of Research by Young Researchers V Masatoshi Hamanaka Akira Nishimura Hiroshi Takaesu Shigeyuki Hirai Katsutoshi Itoyama Akiyuki Yoshino Shohei Kajiwara Nozomi Kigimoto
More informationa) Extraction of Similarities and Differences in Human Behavior Using Singular Value Decomposition Kenichi MISHIMA, Sayaka KANATA, Hiroaki NAKANISHI a
a) Extraction of Similarities and Differences in Human Behavior Using Singular Value Decomposition Kenichi MISHIMA, Sayaka KANATA, Hiroaki NAKANISHI a), Tetsuo SAWARAGI, and Yukio HORIGUCHI 1. Johansson
More informationOngaCREST [10] A 3. Latent Dirichlet Allocation: LDA [11] Songle [12] Pitman-Yor (VPYLM) [13] [14,15] n n n 3.1 [16 18] PreFEst [19] F
1,a) 2,b) 1,c) LPMCC MFCC Fluctuation Pattern (LDA) Songle Pitman-Yor (VPYLM) 3278 1. (MIR: Music Information Retrieval) [1 5] [6 8] 1 National Institute of Advanced Industrial Science and Technology (AIST)
More informationTable 1. Assumed performance of a water electrol ysis plant. Fig. 1. Structure of a proposed power generation system utilizing waste heat from factori
Proposal and Characteristics Evaluation of a Power Generation System Utilizing Waste Heat from Factories for Load Leveling Pyong Sik Pak, Member, Takashi Arima, Non-member (Osaka University) In this paper,
More information1 7.35% 74.0% linefeed point c 200 Information Processing Society of Japan
1 2 3 Incremental Linefeed Insertion into Lecture Transcription for Automatic Captioning Masaki Murata, 1 Tomohiro Ohno 2 and Shigeki Matsubara 3 The development of a captioning system that supports the
More informationIPSJ SIG Technical Report Vol.2009-DPS-141 No.23 Vol.2009-GN-73 No.23 Vol.2009-EIP-46 No /11/27 t-room t-room 2 Development of
t-room 1 2 2 2 2 1 1 2 t-room 2 Development of Assistant System for Ensemble in t-room Yosuke Irie, 1 Shigemi Aoyagi, 2 Toshihiro Takada, 2 Keiji Hirata, 2 Katsuhiko Kaji, 2 Shigeru Katagiri 1 and Miho
More informationA Japanese Word Dependency Corpus ÆüËܸì¤Îñ¸ì·¸¤ê¼õ¤±¥³¡¼¥Ñ¥¹
A Japanese Word Dependency Corpus 2015 3 18 Special thanks to NTT CS, 1 /27 Bunsetsu? What is it? ( ) Cf. CoNLL Multilingual Dependency Parsing [Buchholz+ 2006] (, Penn Treebank [Marcus 93]) 2 /27 1. 2.
More informationVol. 43 No. 7 July 2002 ATR-MATRIX,,, ATR ITL ATR-MATRIX ATR-MATRIX 90% ATR-MATRIX Development and Evaluation of ATR-MATRIX Speech Translation System
Vol. 43 No. 7 July 2002 ATR-MATRIX,,, ATR ITL ATR-MATRIX ATR-MATRIX 90% ATR-MATRIX Development and Evaluation of ATR-MATRIX Speech Translation System Fumiaki Sugaya,,, Toshiyuki Takezawa, Eiichiro Sumita,
More information(3.6 ) (4.6 ) 2. [3], [6], [12] [7] [2], [5], [11] [14] [9] [8] [10] (1) Voodoo 3 : 3 Voodoo[1] 3 ( 3D ) (2) : Voodoo 3D (3) : 3D (Welc
1,a) 1,b) Obstacle Detection from Monocular On-Vehicle Camera in units of Delaunay Triangles Abstract: An algorithm to detect obstacles by using a monocular on-vehicle video camera is developed. Since
More information1_26.dvi
C3PV 1,a) 2,b) 2,c) 3,d) 1,e) 2012 4 20, 2012 10 10 C3PV C3PV C3PV 1 Java C3PV 45 38 84% Programming Process Visualization for Supporting Students in Programming Exercise Hiroshi Igaki 1,a) Shun Saito
More information第 55 回自動制御連合講演会 2012 年 11 月 17 日,18 日京都大学 1K403 ( ) Interpolation for the Gas Source Detection using the Parameter Estimation in a Sensor Network S. T
第 55 回自動制御連合講演会 212 年 11 月 日, 日京都大学 1K43 () Interpolation for the Gas Source Detection using the Parameter Estimation in a Sensor Network S. Tokumoto, T. Namerikawa (Keio Univ. ) Abstract The purpose of
More informationIPSJ SIG Technical Report Vol.2015-MUS-107 No /5/23 HARK-Binaural Raspberry Pi 2 1,a) ( ) HARK 2 HARK-Binaural A/D Raspberry Pi 2 1.
HARK-Binaural Raspberry Pi 2 1,a) 1 1 1 2 3 () HARK 2 HARK-Binaural A/D Raspberry Pi 2 1. [1,2] [2 5] () HARK (Honda Research Institute Japan audition for robots with Kyoto University) *1 GUI ( 1) Python
More informationIPSJ SIG Technical Report Vol.2009-BIO-17 No /5/26 DNA 1 1 DNA DNA DNA DNA Correcting read errors on DNA sequences determined by Pyrosequencing
DNA 1 1 DNA DNA DNA DNA Correcting read errors on DNA sequences determined by Pyrosequencing Youhei Namiki 1 and Yutaka Akiyama 1 Pyrosequencing, one of the DNA sequencing technologies, allows us to determine
More information(MIRU2008) HOG Histograms of Oriented Gradients (HOG)
(MIRU2008) 2008 7 HOG - - E-mail: katsu0920@me.cs.scitec.kobe-u.ac.jp, {takigu,ariki}@kobe-u.ac.jp Histograms of Oriented Gradients (HOG) HOG Shape Contexts HOG 5.5 Histograms of Oriented Gradients D Human
More information3 3) 6) 1) MPEG-7 2) MPEG-7 (A) (B) 2 9) Zils 10) (1) (2) 2.1 2
yoshii@kuis.kyoto-u.ac.jp m.goto@aist.go.jp okuno@i.kyoto-u.ac.jp 48% 82% Identification of Hihat Cymbals for Musical Audio Signals Using the Single Template Adaptation Method KAZUYOSHI YOSHII,MASATAKA
More information1: A/B/C/D Fig. 1 Modeling Based on Difference in Agitation Method artisoc[7] A D 2017 Information Processing
1,a) 2,b) 3 Modeling of Agitation Method in Automatic Mahjong Table using Multi-Agent Simulation Hiroyasu Ide 1,a) Takashi Okuda 2,b) Abstract: Automatic mahjong table refers to mahjong table which automatically
More information60 90% ICT ICT [7] [8] [9] 2. SNS [5] URL 1 A., B., C., D. Fig. 1 An interaction using Channel-Oriented Interface. SNS SNS SNS SNS [6] 3. Processing S
1,a) 1 1,b) 1,c) 1,d) Interaction Design for Communication Between Older Adults and Their Families Using Channel-Oriented Interface Takeda Keigo 1,a) Ishiwata Norihiro 1 Nakano Teppei 1,b) Akabane Makoto
More informationIPSJ SIG Technical Report An Evaluation Method for the Degree of Strain of an Action Scene Mao Kuroda, 1 Takeshi Takai 1 and Takashi Matsuyama 1
1 1 1 An Evaluation Method for the Degree of of an Action Scene Mao Kuroda, 1 Takeshi Takai 1 and Takashi Matsuyama 1 The purpose of our research is to investigate structure of an action scene scientifically.
More informationComputational Semantics 1 category specificity Warrington (1975); Warrington & Shallice (1979, 1984) 2 basic level superiority 3 super-ordinate catego
Computational Semantics 1 category specificity Warrington (1975); Warrington & Shallice (1979, 1984) 2 basic level superiority 3 super-ordinate category preservation 1 / 13 analogy by vector space Figure
More informationIPSJ SIG Technical Report Vol.2012-CG-148 No /8/29 3DCG 1,a) On rigid body animation taking into account the 3D computer graphics came
3DCG 1,a) 2 2 2 2 3 On rigid body animation taking into account the 3D computer graphics camera viewpoint Abstract: In using computer graphics for making games or motion pictures, physics simulation is
More information_念3)医療2009_夏.indd
Evaluation of the Social Benefits of the Regional Medical System Based on Land Price Information -A Hedonic Valuation of the Sense of Relief Provided by Health Care Facilities- Takuma Sugahara Ph.D. Abstract
More information1. HNS [1] HNS HNS HNS [2] HNS [3] [4] [5] HNS 16ch SNR [6] 1 16ch 1 3 SNR [4] [5] 2. 2 HNS API HNS CS27-HNS [1] (SOA) [7] API Web 2
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. 657 8531 1 1 E-mail: {soda,matsubara}@ws.cs.kobe-u.ac.jp, {masa-n,shinsuke,shin,yosimoto}@cs.kobe-u.ac.jp,
More information<4D6963726F736F667420576F7264202D203033918190EC959F90B781758BA38B5A82A982E982BD82CC897282DD82C982A882AF82E983748348838B837D839383678EFC9467909482CC93C192A5817630322033372D34392E646F63>
(Research in Experimental Phonetics and Linguistics) 4: 37-49 (2012) * 1 2 2 1 2 1 3 1. 1 500 1 1 1 2 3 1 * 2010 2 1 2 1 7 1 1 2 3 3 2 37 1 3 1 4-3-1-5 4 1 1 4 5 2 1 4-3-1-5 2 6 2 7 SUGI Speech Analyzer
More informationBull. of Nippon Sport Sci. Univ. 47 (1) Devising musical expression in teaching methods for elementary music An attempt at shared teaching
Bull. of Nippon Sport Sci. Univ. 47 (1) 45 70 2017 Devising musical expression in teaching methods for elementary music An attempt at shared teaching materials for singing and arrangements for piano accompaniment
More information24 Depth scaling of binocular stereopsis by observer s own movements
24 Depth scaling of binocular stereopsis by observer s own movements 1130313 2013 3 1 3D 3D 3D 2 2 i Abstract Depth scaling of binocular stereopsis by observer s own movements It will become more usual
More informationIPSJ SIG Technical Report Vol.2011-EC-19 No /3/ ,.,., Peg-Scope Viewer,,.,,,,. Utilization of Watching Logs for Support of Multi-
1 3 5 4 1 2 1,.,., Peg-Scope Viewer,,.,,,,. Utilization of Watching Logs for Support of Multi-View Video Contents Kosuke Niwa, 1 Shogo Tokai, 3 Tetsuya Kawamoto, 5 Toshiaki Fujii, 4 Marutani Takafumi,
More information,,.,,.,..,.,,,.,, Aldous,.,,.,,.,,, NPO,,.,,,,,,.,,,,.,,,,..,,,,.,
J. of Population Problems. pp.,.,,,.,,..,,..,,,,.,.,,...,.,,..,.,,,. ,,.,,.,..,.,,,.,, Aldous,.,,.,,.,,, NPO,,.,,,,,,.,,,,.,,,,..,,,,., ,,.,,..,,.,.,.,,,,,.,.,.,,,. European Labour Force Survey,,.,,,,,,,
More informationIPSJ SIG Technical Report Vol.2011-DBS-153 No /11/3 Wikipedia Wikipedia Wikipedia Extracting Difference Information from Multilingual Wiki
Wikipedia 1 2 3 Wikipedia Wikipedia Extracting Difference Information from Multilingual Wikipedia Yuya Fujiwara, 1 Yu Suzuki 2 and Akiyo Nadamoto 3 There are multilingual articles on the Wikipedia. The
More information2) TA Hercules CAA 5 [6], [7] CAA BOSS [8] 2. C II C. ( 1 ) C. ( 2 ). ( 3 ) 100. ( 4 ) () HTML NFS Hercules ( )
1,a) 2 4 WC C WC C Grading Student programs for visualizing progress in classroom Naito Hiroshi 1,a) Saito Takashi 2 Abstract: To grade student programs in Computer-Aided Assessment system, we propose
More informationit-ken_open.key
深層学習技術の進展 ImageNet Classification 画像認識 音声認識 自然言語処理 機械翻訳 深層学習技術は これらの分野において 特に圧倒的な強みを見せている Figure (Left) Eight ILSVRC-2010 test Deep images and the cited4: from: ``ImageNet Classification with Networks et
More informationsoturon.dvi
12 Exploration Method of Various Routes with Genetic Algorithm 1010369 2001 2 5 ( Genetic Algorithm: GA ) GA 2 3 Dijkstra Dijkstra i Abstract Exploration Method of Various Routes with Genetic Algorithm
More informationJOURNAL OF THE JAPANESE ASSOCIATION FOR PETROLEUM TECHNOLOGY VOL. 66, NO. 6 (Nov., 2001) (Received August 10, 2001; accepted November 9, 2001) Alterna
JOURNAL OF THE JAPANESE ASSOCIATION FOR PETROLEUM TECHNOLOGY VOL. 66, NO. 6 (Nov., 2001) (Received August 10, 2001; accepted November 9, 2001) Alternative approach using the Monte Carlo simulation to evaluate
More informationJAIST Reposi Title 既存曲に合わせて口す さまれる即興歌唱を利用した 音楽創作支援手法に関する研究 Author(s) 柳, 卓知 Citation Issue Date Type Thesis or Dissertation Te
JAIST Reposi https://dspace.j Title 既存曲に合わせて口す さまれる即興歌唱を利用した 音楽創作支援手法に関する研究 Author(s) 柳, 卓知 Citation Issue Date 2017-03 Type Thesis or Dissertation Text version author URL http://hdl.handle.net/10119/14119
More information[2] 2. [3 5] 3D [6 8] Morishima [9] N n 24 24FPS k k = 1, 2,..., N i i = 1, 2,..., n Algorithm 1 N io user-specified number of inbetween omis
1,a) 2 2 2 1 2 3 24 Motion Frame Omission for Cartoon-like Effects Abstract: Limited animation is a hand-drawn animation style that holds each drawing for two or three successive frames to make up 24 frames
More informationEQUIVALENT TRANSFORMATION TECHNIQUE FOR ISLANDING DETECTION METHODS OF SYNCHRONOUS GENERATOR -REACTIVE POWER PERTURBATION METHODS USING AVR OR SVC- Ju
EQUIVALENT TRANSFORMATION TECHNIQUE FOR ISLANDING DETECTION METHODS OF SYNCHRONOUS GENERATOR -REACTIVE POWER PERTURBATION METHODS USING AVR OR SVC- Jun Motohashi, Member, Takashi Ichinose, Member (Tokyo
More informationWikipedia YahooQA MAD 4)5) MAD Web 6) 3. YAMAHA 7) 8) 2 3 4 5 6 2. Vocaloid2 2006 1 PV 2009 1 1100 200 YouTube 1 minato minato ussy 3D MAD F EDis ussy
1, 2 3 1, 2 Web Fischer Social Creativity 1) Social Creativity CG Network Analysis of an Emergent Massively Collaborative Creation Community Masahiro Hamasaki, 1, 2 Hideaki Takeda 3 and Takuichi Nishimura
More information( ) fnirs ( ) An analysis of the brain activity during playing video games: comparing master with not master Shingo Hattahara, 1 Nobuto Fuji
1 1 2 3 4 ( ) fnirs () An analysis of the brain activity during playing video games: comparing master with not master Shingo Hattahara, 1 Nobuto Fujii, 1 Shinpei Nagae, 2 Koji Kazai 3 and Haruhiro Katayose
More informationSICE東北支部研究集会資料(2012年)
77 (..3) 77- A study on disturbance compensation control of a wheeled inverted pendulum robot during arm manipulation using Extended State Observer Luis Canete Takuma Sato, Kenta Nagano,Luis Canete,Takayuki
More informationIPSJ SIG Technical Report Vol.2017-SLP-115 No /2/18 1,a) 1 1,2 Sakriani Sakti [1][2] [3][4] [5][6][7] [8] [9] 1 Nara Institute of Scie
1,a) 1 1,2 Sakriani Sakti 1 1 1 1. [1][2] [3][4] [5][6][7] [8] [9] 1 Nara Institute of Science and Technology 2 Japan Science and Technology Agency a) ishikawa.yoko.io5@is.naist.jp 2. 1 Belief-Desire theory
More informationkubostat2018d p.2 :? bod size x and fertilization f change seed number? : a statistical model for this example? i response variable seed number : { i
kubostat2018d p.1 I 2018 (d) model selection and kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2018 06 25 : 2018 06 21 17:45 1 2 3 4 :? AIC : deviance model selection misunderstanding kubostat2018d (http://goo.gl/76c4i)
More information[2][3][4][5] 4 ( 1 ) ( 2 ) ( 3 ) ( 4 ) 2. Shiratori [2] Shiratori [3] [4] GP [5] [6] [7] [8][9] Kinect Choi [10] 3. 1 c 2016 Information Processing So
1,a) 2 2 1 2,b) 3,c) A choreographic authoring system reflecting a user s preference Ryo Kakitsuka 1,a) Kosetsu Tsukuda 2 Satoru Fukayama 2 Naoya Iwamoto 1 Masataka Goto 2,b) Shigeo Morishima 3,c) Abstract:
More information