Music Performer Recognition Using an Ensemble of Simple Classifiers

Size: px
Start display at page:

Download "Music Performer Recognition Using an Ensemble of Simple Classifiers"

Transcription

1 Musc Performer Recogto Usg a Esemble of Smple Classfers Efstathos Stamatatos 1 ad Gerhard Wdmer 2 Abstract. Ths. paper addresses the problem of detfyg the most lkely musc performer, gve a set of performaces of the same pece by a umber of sklled caddate pasts. We propose a set of features for represetg the stylstc characterstcs of a musc performer. A database of pao performaces of 22 pasts playg two peces by F. Chop s used the preseted expermets. Due to the lmtatos of the trag set sze ad the characterstcs of the put features we propose a esemble of smple classfers derved by both subsamplg the trag set ad subsamplg the put features. Prelmary expermets show that the resultg esemble s able to effcetly cope wth ths dffcult muscal task, dsplayg a level of accuracy ulkely to be matched by huma lsteers (uder smlar codtos). 1 INTRODUCTION The represetato of musc as gve the prted score s ot able to capture every muscal uace. Hece, a pece played exactly as otated the prted score would soud mechacal. Expressve musc performace s the terpretato of a pece of musc accordg to the artst s uderstadg of the structure (or meag ) of the pece. Every sklled performer cotuously modfes mportat parameters, such as tempo ad loudess, order to stress certa otes or shape certa passages. Expressve performace s what makes musc come alve ad what dstgushes oe performer from aother (ad what makes some performers famous). Because of ts cetral role our muscal culture, expressve performace s a cetral research topc cotemporary muscology. Oe ma drecto emprcal performace research ams at the developmet of rules or prcples of expressve performace ether wth the help of huma experts [6] or by processg large volumes of data usg mache learg techques [11]. Obvously, ths drecto attempts to explore the smlartes betwee sklled performers the same muscal cotext. O the other had, the dffereces betwee performers have ot bee studed thoroughly. Repp [10] preseted a exhaustve statstcal aalyss of temporal commoaltes ad dffereces amog dstgushed pasts' terpretatos of a well-kow pece ad demostrated the dvdualty of some famous pasts. However, the dffereces musc performace are stll expressed geerally wth aesthetc crtera rather tha quattatvely. 1 Austra Research Isttute for Artfcal Itellgece, Schottegasse 3, A Vea, Austra, emal: staths@a.uve.ac.at 2 Departmet of Medcal Cyberetcs ad Artfcal Itellgece, Uversty of Vea, ad Austra Research Isttute for Artfcal Itellgece, Schottegasse 3, A-1010 Vea, Austra, emal: gerhard@a.uve.ac.at I ths paper, we use AI (specfcally: mache learg) techques a attempt to express the dvdualty of musc performers (pasts) mache-terpretable terms by quatfyg the ma parameters of expressve performace. I order to avod ay subjectve evaluato of our approach, we apply t to a welldefed problem: the automatc detfcato of musc performers, gve a set of pao performaces of the same pece of musc by a umber of sklled caddate pasts. From ths perspectve, our task ca be vewed as a typcal classfcato problem, where the classes are the caddate pasts. A set of features that represet the stylstc propertes of a performer s proposed, troducg the orm performace as a referece pot, whle deas take from mache learg research are appled to the costructo of the classfer. The dmesos of expressve varato that wll be take to accout are the three ma expressve parameters avalable to a past: tmg (varatos tempo), dyamcs (varatos loudess), ad artculato (the use of overlaps ad pauses betwee successve otes). Frst expermetal results show that t s deed possble for a mache to dstgush musc performers (pasts) o the bass of ther performace style. From the pot of vew of mache learg, ths costtutes aother supportg case for the utlty of esemble learg methods (specfcally, the combato of a large umber of depedet smple experts [2]). The cotrbuto of ths work to muscology s the detfcato (va mache learg methodolog of a set of global characterstcs of performace style that seem to be relevat to dstgushg dfferet artsts. O the other had, t must be stressed that the curret results are stll very prelmary ad lmted because of the lmted emprcal data avalable for ths vestgato. Obtag precse measuremets, terms of tmg devatos, dyamcs, ad artculato, of performaces of hghly sklled artsts s a dffcult task. We are curretly vestg a large amout of effort to developg ew methods for extractg expressve detals from gve recordgs ad hope to be able to report o much more extesve expermets the ear future. 2 DATA AND TERMINOLOGY The data used ths study cossts of performaces played ad recorded o a Boesedorfer SE290 computer-motored cocert grad pao, whch s able to measure every key ad pedal movemet of the artst wth very hgh precso. 22 sklled performers, cludg professoal pasts, graduate studets ad professors of the Vea Musc Uversty, played two peces by F. Chop: the Etude op. 10/3 (frst 21 bars) ad the Ballade op. 38 (tal secto, bars 1 to 45). The dgtal recordgs were the

2 Score devato Norm devato #01 #02 #03 #04 # Fgure 1. Smoothed tmg devato of the pasts #01-#05 from the prted score (above) ad the orm of the pasts #06-#10 (below) for the soprao otes of Chop s Etude op. 10/3. trascrbed to symbolc form ad matched agast the prted score [3]. Thus, for each ote a pece we have precse formato about how t was otated the score, ad how t was actually played a performace. The parameters of terest are the exact tme whe a ote was played (vs. whe t should have bee played accordg to the score) ths relates to tempo ad tmg, the dyamc level or loudess of a played ote (dyamcs), ad the exact durato of played ote, ad how the ote s coected to the followg oe (artculato). All ths ca be readly computed from our data. I the followg, the term Iter-Oset Iterval (IOI) wll be used to deote the tme terval betwee the osets of two successve otes of the same voce. We defe Off-Tme Durato (OTD) as the tme terval betwee the offset tme of oe ote ad the oset tme of the ext ote of the same voce. The 22 pasts are referred by ther code ames (.e., #01, #02, etc.). 3 FEATURES FOR CHARACTERIZING PERFORMANCE STYLE If we defe (somewhat smplstcall expressve performace as teded devato from the score, the dfferet performaces dffer the way ad extet the artst devates from the score,.e., from a purely mechacal ( flat ) redto of the pece, terms of tmg, dyamcs, ad artculato. I order to be able to compare performaces of peces or sectos of dfferet legth, we eed to defe features that characterze ad quatfy these devatos at a global level,.e., wthout referece to dvdual otes ad how these were played. Fgure 1 (top) shows the tmg devato of fve pasts (#01- #05) from the prted score of Chop's Etude op. 10/3 (measured as the dfferece betwee performed IOIs ad the IOIs that would result from a mechacal performace of the pece at a pre-specfed fxed tempo). It s obvous that all the pasts ted to devate from the score a smlar way. That s ot surprsg. It s well kow that to a certa extet, expressve varato s correlated wth the structure of the pece of musc (e.g., phrase structure, harmoc structure, etc.); deed, expressve performace s a meas for the performer to commucate structural formato to the lsteer. The peaks ad dps of the resultg performace curves ted to correlate (more or less strogl wth phrase boudares ad phrase ceters. Thus, f we decde to rely o very global summarzatos of a past's tempo devatos etc. ad ot to ecode detaled aspects of the musc played (such as ts phrase structure, harmoc structure, etc.), these global features wll strogly deped o ad vary wth the trag set. Samplg the trag set from slghtly dfferet segmets of the same pece may affect the output of the classfer substatally. Ths problem ca be avoded by the use of what we call orm devato features. I addto to the comparso of the performace of a certa past wth the prted score, we propose the average performace of a dfferet set of performers as a referece pot. Fgure 1 (bottom) shows the tmg devato of pasts #01-#05 from the average performace (.e., orm) of the pasts #06-#10 for the same pece as above. As ca be see, the tmg devatos of the frst set of pasts from the orm of the secod set are more stable across the pece. Ths s a strog dcato that the orm devato features should ot be affected by slght chages to the trag set. Gve a set of referece performaces, the orm devato ca be easly calculated for tmg, dyamcs, ad artculato. Aother valuable source of formato comes from the explotato of the so-called melody lead pheomeo [7]. Notes that should be played smultaeously accordg to the prted score (.e., chords) are usually slghtly spread out over tme. A voce that s to be emphaszed precedes the other voces ad s played louder. Studes of ths pheomeo [9] showed that melody lead creases wth expressveess ad skll level. Therefore, devatos betwee the otes of the same chord terms of tmg ad dyamcs ca provde useful features that capture a aspect of the stylstc characterstcs of the musc performer. Specfcally, the, we propose the followg global features for represetg a musc performace, gve the prted score ad a performace orm derved from a gve set of dfferet performers: Score devato features: D(IOI s, IOI m ) D(IOI s, OTD m ) D(DL s, DL m ) Norm devato features: D(IOI, IOI m ) D(OTD, OTD m ) D(DL, DL m ) Melody lead features: D(ON xy, ON zy ) D(DL xy, DL zy ) tmg artculato dyamcs tmg artculato dyamcs tmg dyamcs where D(x, (a scalar) deotes the devato of a vector of umerc values x from a referece vector y, IOI s ad DL s are the omal ter-oset terval ad dyamc-level, respectvely, accordg to the prted score, IOI, OTD, ad DL are the ter-

3 oset terval, the off-tme durato, ad the dyamc-level, respectvely, of the performace orm, IOI m, OTD m, ad DL m are the ter-oset terval, the off-tme durato, ad the dyamclevel, respectvely, of the actual performace, ad ON xy, ad DL xy are the o-tme ad the dyamc-level, respectvely, of a ote of the x-th voce wth the chord y. For measurg the devato each of the above features, dfferet types of dstace could be appled. We decded to choose the approprate type of dstace for each feature category accordg to ts statstcal sgfcace the trag set. I the followg expermets, Chop's Ballade op. 38 wll be used as the trag materal, ad the Etude op.10/3 as the test pece. Pasts #01-#12 wll be used as the set of referece pasts to compute the orm performace, ad the task wll be to lear to dstgush pasts #13-#22. For determg the best type of dstace measure for each type of feature, the trag pece (the Ballade) was dvded to four o-overlappg segmets, each cludg 40 soprao otes. For each segmet of the performace of the pece by the pasts #13-#22, the values of the proposed features for the followg dfferet types of dstace were calculated: Smple: Ds ( x, ( ( x y )) Relatve: D Smple absolute: 1 ( x y ) r ( x, ( ) 1 x Dsa ( x, ( x y ) 1 x y Relatve absolute: Dra ( x, ( ) x The, aalyss of varace (aka ANOVA) was appled to these values for extractg coclusos about the statstcal sgfcace of the dfferet types of dstace ad features. The most sgfcat features proved to be the devato from the orm terms of tmg ad artculato, the tmg devato betwee the frst ad the thrd voce as well as betwee the frst ad the fourth voce (the bass le), ad the devato from the score terms of tmg ad artculato. As regards the dfferet types of dstaces, D r gave the best results for the score devato features. Ths type of dstace has bee used prevously for comparg dfferet performaces. D s seems to be the approprate selecto for the orm devato features. Fally, D sa fts better the melody lead features, whch dcates that formato o whether a voce precedes or follows the frst voce a chord s ot that mportat as the degree to whch devates from t. 1 4 THE CLASSIFICATION MODEL 4.1 Problem characterstcs Sce oly two peces were avalable (oe of whch should serve as depedet test pece), the trag examples of the musc performer classfer should cosst of pece segmets rather tha etre muscal peces. To determe the best mode of segmetato (equal legth segmets or segmets based o the pece's phrase structure), a smple expermet was performed. A umber of smple classfers, based o dfferet types of features ad dstace deftos, were traed (va dscrmat aalyss see below) usg the Accuracy (%) Table 1. Comparso of score ad orm devato measures for dfferet types of dstace ad dfferet methods of formg trag examples. Accuracy (%) Dstace Equal-legth Phrase-based D s D r D sa D ra D s D r D sa D ra Score Norm Trag example legth Fgure 2. Classfcato accuracy vs. trag example legth ( soprao otes). performaces of the pasts #13-#22 of Ballade op. 38, wth dfferet methods of segmetg the pece to trag examples: oe case, the pece was segmeted to four parts of equal legth (40 soprao otes each), the other, t was cut to four parts accordg to phrase boudares that were detfed maually by a huma expert. Table 1 shows the classfcato accuracy results (leave-oe-out evaluato o the orgal data). As ca be see, all the cases the classfers based o trag examples of equal legth gave better or equal accuracy results comparso wth the phrase-based classfers. The orm devato features geerally outperformed the score devato features. Fgure 2 shows the relato of the legth of the trag examples (umber of soprao otes) wth the classfcato accuracy usg Ballade op. 38 as testg groud ad the orm devato features. The loger the segmets that costtute the trag examples, the more accurate the classfer. Ths meas that for costructg relable classfers t s ecessary to have trag examples as log as possble, whch makes for a rather small umber of examples ad aga meas that the umber of put features per example (segmet) should be rather small ( order to avod overfttg of the trag data). 4.2 The proposed esemble All the above characterstcs of the problem suggest the use of a esemble of classfers rather tha a uque classfer. Recet research mache learg [1, 4] has studed thoroughly the costructo of meta-classfers. I ths study, we take advatage of these techques, costructg a esemble of classfers derved

4 Table 2. Descrpto of the proposed smple classfers. The thrd colum dcates the umber of trag examples (ad ther legth soprao otes) per class. Code Iput features Tr. examples Acc. (%) C 11 D s (IOI, IOI m ), D s (OTD, OTD m ), D s (DL, DL m ) 4x C 21 D r (IOI s, IOI m ), D r (IOI s, OTD m ), D r (DL s, DL m ) 12x C 22 D r (IOI s, IOI m ), D r (IOI s, OTD m ), D r (DL s, DL m ) 12x C 23 D r (IOI s, IOI m ), D r (IOI s, OTD m ), D r (DL s, DL m ) 12x C 24 D r (IOI s, IOI m ), D r (IOI s, OTD m ), D r (DL s, DL m ) 12x C 31 D sa (ON 1m, ON 2m ), D sa (ON 1m, ON 3m ), D sa (ON 1m, ON 4m ) 4x C 32 D sa (DL 1m, DL 2m ), D sa (DL 1m, DL 3m ), D sa (DL 1m, DL 4m ) 4x C 33 D sa (ON 1m, ON 2m ), D sa (DL 1m, DL 2m ) 4x C 34 D sa (ON 1m, ON 3m ), D sa (DL 1m, DL 3m ) 4x C 35 D sa (ON 1m, ON 4m ), D sa (DL 1m, DL 4m ) 4x Table 3. Predctos of the dvdual smple classfers o performaces of the usee test set (Etude op. 10/3). The frst colum dcates the code of the actual performer. Correct predctos are boldface. Last row summarzes correct guesses. Actual C 11 C 21 C 22 C 23 C 24 C 31 C 32 C 33 C 34 C 35 #13 #13 #13 #16 #13 #18 #13 #13 #13 #13 #13 #14 #14 #21 #14 #22 #22 #21 #21 #13 #21 #15 #15 #21 #21 #14 #21 #14 #15 #13 #15 #17 #13 #16 #18 #18 #16 #18 #18 #16 #16 #19 #16 #16 #17 #17 #17 #17 #17 #17 #15 #17 #16 #16 #21 #18 #13 #13 #16 #18 #18 #17 #17 #22 #18 #14 #19 #13 #19 #19 #13 #13 #16 #19 #19 #16 #19 #20 #14 #21 #14 #14 #14 #20 #20 #14 #14 #20 #21 #14 #14 #14 #14 #14 #17 #17 #13 #21 #14 #22 #22 #17 #19 #19 #22 #16 #16 #15 #16 #16 Correct: from subsamplg the put features ad subsamplg the trag data set. The former techque s usually appled whe multple redudat features are avalable. I our case, the put features caot be used cocurretly due to the lmted sze of the trag set (.e., oly a few trag examples per class are avalable) ad the cosequet dager of overfttg. The latter techque s usually appled whe ustable learg algorthms are used for costructg the base classfers. I our case, a subset of the put features (.e., the score devato measures) s ustable ther values ca chage drastcally gve a slght chage the selected trag segmets. Gve the scarcty of trag data ad the multtude of possble features, we propose the use of a relatvely large umber of rather smple dvdual base classfers (or experts, the termology of [2]). Each expert s traed usg a dfferet set of features ad/or parts of the trag data. The features ad sectos of the trag performaces used for the dvdual experts are lsted table 2. C 11 s based o the devato of the performer from the orm. C 21, C 22, C 23, ad C 24 are based o the devato of the performer from the score ad are traed usg slghtly chaged trag sets (because the orm features are kow to be ustable relatve to chages the data). The trag set was dvded to four dsjot subsets ad the four dfferet overlappg trag sets were costructed by droppg oe of these four subsets (.e., crossvaldated commttees). Fally, C 31, C 32, C 33, C 34, ad C 35 are based o melody lead features. The learg algorthm used to costruct the dvdual experts s dscrmat aalyss, a stadard techque of multvarate statstcs, whch costructs a set of lear fuctos of the put varables by maxmzg the betwee-group varace whle mmzg the wth-group varace [5]. The last colum table 2 shows the accuracy of each dvdual expert o the trag data (estmated va leave-oe-out crossvaldato). As ca be see, the classfer based o orm devato features s by far the most accurate. The combato of the resultg smple classfers or experts s realzed va a weghted majorty scheme. The predcto of each dvdual classfer s weghted accordg to ts accuracy o the trag set [8]. Both the frst ad the secod choce of a classfer are take to accout. Specfcally, the weght w of the classfer C s as follows: w a a where a s the accuracy of the classfer C o the trag set (see table 2). a /2 s used to compute the weght for the secod choce of a classfer. The class recevg the hghest votes s the fal class predcto. Specfcally, f c (x) s the predcto of the classfer C for the case x ad P s the set of possble classes (.e., pasts) the the fal predcto s extracted as follows: xy c ˆ( x) arg max w c ( x) p pp where a=b s 1 f a s equal to b ad 0 otherwse. 4.3 Expermetal results The dvdual base classfers as defed above were traed o the performaces of the Ballade op.38 by pasts #13-#22; pasts #01-#11 were used to defe the performace orm. Both the dvdual base classfers ad the combed esemble classfer were the tested o a depedet test pece, the Etude op.10/3. Table 3 shows the classfcato results for the dvdual base classfers. The classfcato accuracy of each dvdual classfer rages betwee 30% ad 50%. The errors of orm devato ad score devato classfers are partally correlated (.e., commo msclassfcatos: #16-#18, #19-#13, #20-#14, #21-#14). O the xy

5 Table 4. Predctos (frst ad secod choce) of the esemble of the smple classfers o performaces of the usee test set (Etude op. 10/3). The frst colum dcates the code of the actual performer. Correct predctos are boldface. Last row summarzes correct guesses. Actual 1st choce Score 2d choce Score #13 # # #14 # # #15 # # #16 # # #17 # # #18 # # #19 # # #20 # # #21 # # #22 # # Correct: 7 1 other had, the errors of the melody lead classfers are hghly ucorrelated comparso to the others. Note that ucorrelated errors are very crucal for costructg esembles of classfers [4]. Table 4 shows the classfcato results of the esemble classfer. The esemble correctly detfed the past 7 out of 10 cases, whch gves a accuracy of 70%. The esemble thus performs substatally better tha ay of the costtuet classfers. The score assged to each predcto ca be used as a dcato of the classfer's certaty. Thus, the classfcato of the performaces by pasts #14, #18, ad #22 are the most dffcult cases sce the dstace of the frst choce from the secod choce s less tha Note that 70% s a hgh success rate a 10-class task. Note also that ths would be a very dffcult task for a huma: mage you frst hear 10 dfferet pasts performg oe partcular pece (ad that s all you kow about the pasts), ad the you have to detfy each of the 10 pasts a recordg of aother (ad qute dfferet) pece. We are plag a classfcato expermet wth huma lsteers to measure the level of huma performace ths type of task; we expect t to be substatally lower. 5 CONCLUSIONS We have preseted a computatoal approach to the problem of dscrmatg betwee musc performers playg the same pece of musc, ad troduced a set of features that capture some aspects of the dvdual style of each performer. I order to cope effcetly wth ths problem, we proposed a classfcato model that takes advatage of varous techques of costructg meta-classfers. The results show that the dffereces betwee musc performers ca be quatfed. Whle huma experts use mostly aesthetc crtera for recogzg dfferet performers, t s demostrated that the dvdualty of each performer ca be objectvely captured usg mache-terpretable features. Ths research s performed the cotext of a large research project whose goal s to study fudametal prcples of expressve musc performace wth AI methods. The curret study ca be see as aother attempt at dscoverg ad quatfyg features that are crucal to uderstadg ad modelg ths complex pheomeo. The proposed features ca be easly computed ad do ot make use of ay pece-specfc formato (e.g., extracted by structural or harmoc aalyss). However, the results caot be easly terpreted terms of the tradtoal musc theory. Thus, the proposed features are ot lkely to help the explaato of the dffereces betwee the performers. Such a task would requre features assocated wth partcular local muscal cotexts ad pecespecfc formato. The relablty of our curret results s stll severely compromsed by the very small set of emprcal data that were avalable. It s plaed to vest substatal effort the future to collectg ad precsely measurg a larger ad more dverse set of performaces by a set of dfferet pasts (o a computercotrolled pao). Studyg famous cocert pasts wth ths approach would requre us to be able to precsely measure tmg, dyamcs, ad artculato from soud recordgs, whch ufortuately stll s a usolved sgal-processg problem. ACKNOWLEDGEMENTS Ths work was supported by the EU project HPRN-CT (MOSART) ad the START program of the Austra Federal Mstry for Educato, Scece, ad Culture (Grat o. Y99-INF). The Austra Research Isttute for Artfcal Itellgece ackowledges basc facal support from the Austra Federal Mstry for Educato, Scece, ad Culture. REFERENCES [1] E. Bauer ad R. Kohav, A Emprcal Comparso of Votg Classfcato Algorthms: Baggg, Boostg, ad Varats, Mache Learg, 39 (1/2), pp , (1999). [2] A. Blum, Emprcal Support for Wow ad Weghted-Majorty Based Algorthms: Results o a Caledar Schedulg Doma, Mache Learg, 26 (1), pp. 5-23, (1997). [3] E. Cambouropoulos, From MIDI to Tradtoal Musc Notato, I Proc. of the AAAI 2000 Workshop o Artfcal Itellgece ad Musc, 17th Natoal Cof. O Artfcal Itellgece, pp (2000). [4] T. Detterch, Esemble Methods Mache Learg, Frst It. Workshop o Multple Classfer Systems, pp. 1-15, (2000). [5] R. Esebes ad R. Avery, Dscrmat Aalyss ad Classfcato Procedures: Theory ad Applcatos, Lexgto, Mass.: D.C. Health ad Co., 1972 [6] A. Frberg, Geeratve Rules for Musc Performace: A Formal Descrpto of a Rule System Computer Musc Joural, 15 (2), pp , (1991). [7] W. Goebl, Sklled Pao Performace: Melody Lead Caused by Dyamc Dfferetato, I Proc. of the 6th It. Cof. o Musc Percepto ad Cogto, (2000). [8] D. Optz ad J. Shavlk, Geeratg Accurate ad Dverse Members of a Neural-Network Esemble, I D. Touretzky, M. Mozer, ad M. Hasselmo (Eds.) Advaces Neural Iformato Processg Systems, 8, pp , (1996). [9] C. Palmer, O the Assgmet of Structure Musc Performace, Musc Percepto, 14, pp , (1996). [10] B. Repp, Dversty ad Commoalty Musc Performace: A Aalyss of Tmg Mcrostructure Schuma s Träumere. Joural of the Acoustcal Socety of Amerca, 92 (5), pp , (1992). [11] G. Wdmer, Usg AI ad Mache Learg to Study Expressve Musc Performace: Project Survey ad Frst Report, AI Commucatos, 14, pp (2001).

Comparative Study of Word Alignment Heuristics and Phrase-Based SMT

Comparative Study of Word Alignment Heuristics and Phrase-Based SMT Comparatve Study of Word Algmet Heurstcs ad Phrase-Based SMT Hua Wu ad Hafeg Wag Toshba (Cha) Research ad Developmet Ceter 5/F., Tower W2, Oretal Plaza No., East Chag A Ave., Dog Cheg Dstrct Bejg, 00738,

More information

Handout #5. Introduction to the Design of Experiments (DOX) (Reading: FCDAE, Chapter 1~3)

Handout #5. Introduction to the Design of Experiments (DOX) (Reading: FCDAE, Chapter 1~3) Hadout #5 Ttle: FAE Course: Eco 368/01 Spr/015 Istructor: Dr. I-M Chu Itroducto to the Des of Expermets (DOX) (Read: FCDAE, Chapter 1~3) I hadout oe, we leared that data ca be ether observatoal or expermetal.

More information

A Genetic Programming Framework for Error Recovery in Robotic Assembly Systems

A Genetic Programming Framework for Error Recovery in Robotic Assembly Systems A Geetc Programmg Framework for Error Recovery Robotc Assembly Systems Cem M. Baydar cbaydar@umch.edu Kazuhro Satou kazu@umch.edu Departmet of Mechacal Egeerg ad Appled Mechacs Uversty of Mchga A Arbor,

More information

Real-time Scheduling of Flexible Manufacturing Systems using Support Vector Machines and Neural Networks

Real-time Scheduling of Flexible Manufacturing Systems using Support Vector Machines and Neural Networks Real-tme Schedulg of Flexble Maufacturg Systems usg Support Vector Maches ad Neural Networks P. Prore, R. Po, J. Parreño, J. Lozao ad M. Moterrey EPI de Gó, Campus de Vesques, 33203 Gó, Asturas, Spa Abstract

More information

Exploiting the Marginal Profits of Constraints with Evolutionary Multi-objective Optimization Techniques

Exploiting the Marginal Profits of Constraints with Evolutionary Multi-objective Optimization Techniques Eplotg the Margal Profts of Costrats wth Evolutoary Mult-objectve Optmzato Techques Ya Zheyu Zh We Kag Lsha Laboratory of Software Egeerg Departmet of Computer Scece Laboratory of Software Egeerg Wuha

More information

Use the template below as a guide for organizing the text of your story.

Use the template below as a guide for organizing the text of your story. Plot Ptch Template Use the template below as a gude for orgazg the text of your story. Ths template s a suggesto of how the text of a 14-page chldre s pcture storybook could be orgazed. It does ot clude

More information

Recognizing Names in Biomedical Texts using Hidden Markov Model and SVM plus Sigmoid

Recognizing Names in Biomedical Texts using Hidden Markov Model and SVM plus Sigmoid Recogzg Names Bomedcal Texts usg Hdde Markov Model ad SVM plus Sgmod ZHOU GuoDog Isttute for Ifocomm Research 2 Heg Mu Keg Terrace Sgapore 963 Emal: zhougd@2r.a-star.edu.sg ABSTRACT I ths paper, we preset

More information

A Realistic E-Learning System based on Mixed Reality

A Realistic E-Learning System based on Mixed Reality A Realstc E-Learg System based o Mxed Realty Kyusug Cho 1, Juho Lee 1, Jaem Soh 1, Juseok Lee 2, Hyu S. Yag 1 1 Departmet of Computer Scece, Korea Advaced Isttute of Scece ad Techology, 373-1 Guseog-dog,

More information

Cost Control of the Transmission Congestion Management in Electricity Systems Based on Ant Colony Algorithm

Cost Control of the Transmission Congestion Management in Electricity Systems Based on Ant Colony Algorithm Eergy ad Power Egeerg, 2011, 3, 17-23 do:10.4236/epe.2011.31003 Publshed Ole February 2011 (http://www.scrp.org/joural/epe) Cost Cotrol of the Trasmsso Cogesto Maagemet Electrcty Systems Based o At Coloy

More information

Rank Inclusion in Criteria Hierarchies

Rank Inclusion in Criteria Hierarchies Mat-.08 deedet Research Project Aled Mathematcs Rak cluso Crtera Herarches Jue 00 Helsk Uversty of Techology ystems Aalyss Laboratory Att Pukka Deartmet of Egeerg Physcs ad Mathematcs 48484T Cotets. NTRODUCTON...3.

More information

Object Modeling for Multicamera Correspondence Using Fuzzy Region Color Adjacency Graphs

Object Modeling for Multicamera Correspondence Using Fuzzy Region Color Adjacency Graphs Obect Modelg for Mltcamera Correspodece Usg Fzzy Rego Color Adacecy Graphs Amr Hosse Khall 1 ad Shohreh Kasae 2 1 Sharf Uersty of Techology, Tehra, Ira a_khall@ce.sharf.ed 2 Sharf Uersty of Techology,

More information

11 Hybrid Cables. n f Hz. kva i P. Hybrid Cables Description INFORMATION Description

11 Hybrid Cables. n f Hz. kva i P. Hybrid Cables Description INFORMATION Description Hybrd Cables Descrpto Hybrd Cables INFORMATION Ths secto shows the techcal data o the hybrd cables avalable at SEW- EURODRIVE. For assgmet o hybrd cables to the products, reer to the respectve sectos..

More information

Scheme For Finding The Next Term Of A Sequence Based On Evolution {File Closing Version 4}. ISSN

Scheme For Finding The Next Term Of A Sequence Based On Evolution {File Closing Version 4}. ISSN Bagad, R. (207). Scheme For Fdg The ext Term Of A Sequece Based O voluto {Fle Closg Verso 3}. ISS 75-3030. PHILICA.COM Artcle umber 40. htt://www.hlca.com/dsla_artcle.h?artcle_d=40 Scheme For Fdg The ext

More information

A BROADCASTING PROTOCOL FOR COMPRESSED VIDEO

A BROADCASTING PROTOCOL FOR COMPRESSED VIDEO Proceegs of the EUROMEDIA 99 Coferece (Much, Aprl 6-8, 1999), pp78-84 A BROADCASTING PROTOCOL FOR COMPRESSED VIDEO Jeha-Fraços Pârs 1 Departmet of Computer Scece Uversty of Housto Housto, TX 7704-475 pars@csuheu

More information

RIAM Local Centre Woodwind, Brass & Percussion Syllabus

RIAM Local Centre Woodwind, Brass & Percussion Syllabus 8 RIAM Local Centre Woodwnd, Brass & Percusson Syllabus 2015-2018 AURAL REQUIREMENTS AND THEORETICAL QUESTIONS REVISED FOR ALL PRACTICAL SUBJECTS AURAL TESTS From Elementary to Grade V ths area s worth

More information

Logistics We are here. If you cannot login to MarkUs, me your UTORID and name.

Logistics We are here. If you cannot login to MarkUs,  me your UTORID and name. Logistics We are here 8 Week If you caot logi to arkus, email me your UTORID ad ame. heck lab marks o arkus, if it s recorded wrog, cotact Larry withi a week after the lab. Quiz average: 8% Assembly Laguage

More information

EE260: Digital Design, Spring /3/18. n Combinational Logic: n Output depends only on current input. n Require cascading of many structures

EE260: Digital Design, Spring /3/18. n Combinational Logic: n Output depends only on current input. n Require cascading of many structures EE260: igital esig, prig 208 4/3/8 EE 260: Itroductio to igital esig equetial Logic Elemets ao Zheg epartmet of Electrical Egieerig Uiversity of Hawaiʻi at Māoa equetial ircuits ombiatioal Logic: Output

More information

Minimum Penalized Hellinger Distance for Model Selection in Small Samples

Minimum Penalized Hellinger Distance for Model Selection in Small Samples Ope Joural of Statstcs,,, 369-38 ttp://dxdoorg/436/os445 ublsed Ole October (ttp://wwwscrorg/oural/os) Mu ealzed ellger Dstace for Model Selecto Sall Saples apa Ngo *, Bertrad Ntep Laboratore de Mateatques

More information

A Computational Model for Discriminating Music Performers

A Computational Model for Discriminating Music Performers A Computational Model for Discriminating Music Performers Efstathios Stamatatos Austrian Research Institute for Artificial Intelligence Schottengasse 3, A-1010 Vienna stathis@ai.univie.ac.at Abstract In

More information

Following a musical performance from a partially specified score.

Following a musical performance from a partially specified score. Followng a muscal performance from a partally specfed score. Bryan Pardo and Wllam P. Brmngham Artfcal Intellgence Laboratory Electrcal Engneerng and Computer Scence Dept. and School of Musc The Unversty

More information

Positive-living skills for children aged 3 to 6

Positive-living skills for children aged 3 to 6 Table of Cotets Gudeles Postve-lvg sklls for chldre aged 3 to 6 A troducto to M MdMasters Suggestos o how to use the M MdMasters actvtes Tps o usg M MdMasters wth chldre who have a physcal dsablty Stages

More information

Part II: Derivation of the rules of voice-leading. The Goal. Some Abbreviations

Part II: Derivation of the rules of voice-leading. The Goal. Some Abbreviations Presetatio by Aaro Yag Huro: Toe ad Voice Part II Toe ad Voice: A Derivatio of the Rules of Voice-Leadig from Perceptual Priciples Part II: Derivatio of the rules of voice-leadig By David Huro Presetatio

More information

Mullard INDUCTOR POT CORE EQUIVALENTS LIST. Mullard Limited, Mullard House, Torrington Place, London Wel 7HD. Telephone:

Mullard INDUCTOR POT CORE EQUIVALENTS LIST. Mullard Limited, Mullard House, Torrington Place, London Wel 7HD. Telephone: Mullard INDUCTOR POT CORE EQUIVALENTS LIST Mullard Limited, Mullard House, Torrigto Place, Lodo Wel 7HD. Telephoe: 01-580 6633 INDUCTOR POT CORE EQUIVALENTS LIST Mullard Limited have bee maufacturig ferrite

More information

Technical Information

Technical Information CHEMCUT Techncal Informaton CORPORATION Introducton The Chemcut CC8000 etcher has many new features desgned to reduce the cost of manufacturng and, just as mportantly, the cost of ownershp. Keepng the

More information

Statistics AGAIN? Descriptives

Statistics AGAIN? Descriptives Cal State Northrdge Ψ427 Andrew Answorth PhD Statstcs AGAIN? What do we want to do wth statstcs? Organze and Descrbe patterns n data Takng ncomprehensble data and convertng t to: Tables that summarze the

More information

Chapter 7 Registers and Register Transfers

Chapter 7 Registers and Register Transfers Logic ad Computer Desig Fudametals Chapter 7 Registers ad Register Trasfers Part 2 Couters, Register Cells, Buses, & Serial Operatios Charles Kime & Thomas Kamiski 28 Pearso Educatio, Ic (Hyperliks are

More information

THE IMPORTANCE OF ARM-SWING DURING FORWARD DIVE AND REVERSE DIVE ON SPRINGBOARD

THE IMPORTANCE OF ARM-SWING DURING FORWARD DIVE AND REVERSE DIVE ON SPRINGBOARD THE MPORTANCE OF ARM-SWNG DURNG FORWARD DVE AND REVERSE DVE ON SPRNGBOARD Ken Yokoyama Laboratory of Bomechancs Faculty ofeducaton Kanazawa Unversty Kanazawa, Japan J unjro Nagano Department of Physcal

More information

Line numbering and synchronization in digital HDTV systems

Line numbering and synchronization in digital HDTV systems Lie umberig ad sychroizatio i digital HDTV systems D. (VURT) I cotrast to aalogue televisio systems where lie umberig is covetioally liked to the vertical sychroizatio, digital televisio offers the possibility

More information

Instructions for Contributors to the International Journal of Microwave and Wireless Technologies

Instructions for Contributors to the International Journal of Microwave and Wireless Technologies Instructons for Contrbutors to the Internatonal Journal of Mcrowave and Wreless Technologes Frst A. Author 1, Second Author 1,2, Thrd Author 2 1 Cambrdge Unversty Press, Ednburgh Buldng, Shaftesbury Road,

More information

Heterogeneous Talent and Optimal Emigration 1

Heterogeneous Talent and Optimal Emigration 1 Heterogeeous Talet a Optmal mgrato A Cotrbuto to te New coomcs of te Bra Dra Nguye Duc Ta Natoal Grauate Isttute for Polcy Stues GRIPS, Tokyo Vetam Developmet orum VD Tokyo mal: 3@stu.grps.ac.jp September

More information

Motivation. Analysis-and-manipulation approach to pitch and duration of musical instrument sounds without distorting timbral characteristics

Motivation. Analysis-and-manipulation approach to pitch and duration of musical instrument sounds without distorting timbral characteristics Aalysis-ad-maipulatio approach to pitch ad duratio of musical istrumet souds without distortig timbral characteristics Takehiro Abe Katsutoshi Itoyama Kazuyoshi Yoshii Kazuori Komatai Tetsuya Ogata Hiroshi

More information

QUICK START GUIDE v0.98

QUICK START GUIDE v0.98 QUICK START GUIDE v0.98 QUICK HELP Q A 1 STEP BY STEP 3 GLOSSARY 2 A B C 1 INSTALLATION 1. Make sure that the hardware nstallaton s performed by a certfed vendor 2. Install OTOTRAK app from Apple s App

More information

Read Only Memory (ROM)

Read Only Memory (ROM) ECE 545 igital System esig with VHL Lecture A igital Logic Reresher Part A Combiatioal Logic Buildig Blocks Cot. Problem 2 What is a size o ROM with a 4-bit address iput ad a 8-bit data output? What is

More information

PROBABILITY AND STATISTICS Vol. I - Ergodic Properties of Stationary, Markov, and Regenerative Processes - Karl Grill

PROBABILITY AND STATISTICS Vol. I - Ergodic Properties of Stationary, Markov, and Regenerative Processes - Karl Grill PROBABILITY AND STATISTICS Vol. I Ergodic Properties of Statioary, Markov, ad Regeerative Processes Karl Grill ERGODIC PROPERTIES OF STATIONARY, MARKOV, AND REGENERATIVE PROCESSES Karl Grill Istitut für

More information

Polychrome Devices Reference Manual

Polychrome Devices Reference Manual Polychrome Devices Referece Maual Improvisio, Viscout Cetre II, Uiversity of Warwick Sciece Park, Millbur Hill Road, Covetry. CV4 7HS Tel: 0044 (0) 24 7669 2229 Fax: 0044 (0) 24 7669 0091 e-mail: admi@improvisio.com

More information

A STUDY OF TRUMPET ENVELOPES

A STUDY OF TRUMPET ENVELOPES A STUDY OF TRUMPET ENVELOPES Roger B. Dannenberg, Hank Pellern, and Istvan Dereny School of Computer Scence, Carnege Mellon Unversty Pttsburgh, PA 15213 USA rbd@cs.cmu.edu, hank.pellern@andrew.cmu.edu,

More information

Image Intensifier Reference Manual

Image Intensifier Reference Manual Image Itesifier Referece Maual Improvisio, Viscout Cetre II, Uiversity of Warwick Sciece Park, Millbur Hill Road, Covetry. CV4 7HS Tel: 0044 (0) 24 7669 2229 Fax: 0044 (0) 24 7669 0091 e-mail: admi@improvisio.com

More information

AMP-LATCH* Ultra Novo mm [.025 in.] Ribbon Cable 02 MAR 12 Rev C

AMP-LATCH* Ultra Novo mm [.025 in.] Ribbon Cable 02 MAR 12 Rev C AMP-LATCH* Ultra Novo Applcaton Specfcaton Receptacle Connectors for 114-40056 0.64 mm [.025 n.] Rbbon Cable 02 MAR 12 All numercal values are n metrc unts [wth U.S. customary unts n brackets]. Dmensons

More information

Appendix A. Quarter-Tone Note Names

Appendix A. Quarter-Tone Note Names 233 Appendx A Qurter-Tone Note Nmes The followng tble lsts ll possble note nmes, rngng from double flts to double shrps, for ech of the 24 possble ptch-clsses. Enhrmonclly equvlent note nmes pper on the

More information

Daniel R. Dehaan Three Études For Solo Voice Summer 2010, Chicago

Daniel R. Dehaan Three Études For Solo Voice Summer 2010, Chicago Daiel R. Dehaa Three Études For Solo Voice Summer 010 Chicago Daiel R. Dehaa Three Études For Solo Voice Summer 010 Chicago Copyright 010 by Daiel R. Dehaa All rights reserved icludig performig rights.

More information

Lost on the Web: Does Web Distribution Stimulate or Depress Television Viewing?

Lost on the Web: Does Web Distribution Stimulate or Depress Television Viewing? Lost on the Web: Does Web Dstrbuton Stmulate or Depress Televson Vewng? Joel Waldfogel The Wharton School Unversty of Pennsylvana August 10, 2007 Prelmnary comments welcome Abstract In the past few years,

More information

Small Area Co-Modeling of Point Estimates and Their Variances for Domains in the Current Employment Statistics Survey

Small Area Co-Modeling of Point Estimates and Their Variances for Domains in the Current Employment Statistics Survey Small Area Co-Modelng of Pont Estmates and Ther Varances for Domans n the Current Employment Statstcs Survey Jule Gershunskaya, Terrance D. Savtsky U.S. Bureau of Labor Statstcs FCSM, March 2018 Dsclamer:

More information

The UCD community has made this article openly available. Please share how this access benefits you. Your story matters!

The UCD community has made this article openly available. Please share how this access benefits you. Your story matters! Provded by the author(s) and Unversty College Dubln Lbrary n accordance wth publsher polces., Please cte the publshed verson when avalable. tle Dynamc Complexty Scalng for Real-me H.264/AVC Vdeo Encodng

More information

NIIT Logotype YOU MUST NEVER CREATE A NIIT LOGOTYPE THROUGH ANY SOFTWARE OR COMPUTER. THIS LOGO HAS BEEN DRAWN SPECIALLY.

NIIT Logotype YOU MUST NEVER CREATE A NIIT LOGOTYPE THROUGH ANY SOFTWARE OR COMPUTER. THIS LOGO HAS BEEN DRAWN SPECIALLY. NIIT Logotype The NIIT logotype is always preseted i a fixed cofiguratio. The desig of the logotype is based o a typeface called Egyptia. The letters N I I T has bee specially desiged ad letter-spaced.

More information

Analysis of Subscription Demand for Pay-TV

Analysis of Subscription Demand for Pay-TV Analyss of Subscrpton Demand for Pay-TV Manabu Shshkura Researcher Insttute for Informaton and Communcatons Polcy 2-1-2 Kasumgasek, Chyoda-ku Tokyo 110-8926 Japan m-shshkura@soumu.go.jp Tel: 03-5253-5496

More information

References and quotations

References and quotations CHAPTER 1.8 Refereces ad quotatios Academic writig depeds o the research ad ideas of others, so it is vital to show which sources you have used i your work, i a acceptable maer. This uit explais the format

More information

Decision Support by Interval SMART/SWING Incorporating. Imprecision into SMART and SWING Methods

Decision Support by Interval SMART/SWING Incorporating. Imprecision into SMART and SWING Methods Decson Support by Interval SMART/SWING Incorporatng Imprecson nto SMART and SWING Methods Abstract: Interval judgments are a way of handlng preferental and nformatonal mprecson n multcrtera decson analyss.

More information

Correcting Image Placement Errors Using Registration Control (RegC ) Technology In The Photomask Periphery

Correcting Image Placement Errors Using Registration Control (RegC ) Technology In The Photomask Periphery Correctng Image Placement Errors Usng Regstraton Control (RegC ) Technology In The Photomask Perphery Av Cohen 1, Falk Lange 2 Guy Ben-Zv 1, Erez Gratzer 1, Dmtrev Vladmr 1 1. Carl Zess SMS Ltd., Karmel

More information

A. Flue Pipes. 2. Open Pipes. = n. Musical Instruments. Instruments. A. Flue Pipes B. Flutes C. Reeds D. References

A. Flue Pipes. 2. Open Pipes. = n. Musical Instruments. Instruments. A. Flue Pipes B. Flutes C. Reeds D. References Musical Istrumets 1 Wid Istrumets 2 Wid Istrumets A. Flue Pipes B. Flutes C. Reeds D. Refereces May 29. 2012 A. Flue Pipes 3 1. Vo Karma vortex street 4 1) Vortex Oscillatio 2) Ope-Ed Pipes 3) Closed-Ed

More information

Modeling Form for On-line Following of Musical Performances

Modeling Form for On-line Following of Musical Performances Modelng Form for On-lne Followng of Muscal Performances Bryan Pardo 1 and Wllam Brmngham 2 1 Computer Scence Department, Northwestern Unversty, 1890 Maple Ave, Evanston, IL 60201 2 Department of Math and

More information

current activity shows on the top right corner in green. The steps appear in yellow

current activity shows on the top right corner in green. The steps appear in yellow Browzwear Tutorals Tutoral ntroducton Ths tutoral leads you through the best practces of color ways operatons usng an llustrated step by step approach. Each slde shows the actual applcaton at the stage

More information

V (D) i (gm) Except for 56-7,63-8 Flute and Oboe are the same. Orchestration will only list Fl for space purposes

V (D) i (gm) Except for 56-7,63-8 Flute and Oboe are the same. Orchestration will only list Fl for space purposes Measure # 1 2 3 5 6 7 8 9 10 11 12 13 1 15 16 17 18 Intro Motve 1 A Motve 1 B - "Wakarathe" Dynamcs mp terraced dymancs to mm.7 p Moderately fast and lght (quarter=96) 3 G Mnor motve 1 con't mp melody

More information

US B2. ( *) Notice: Subject to any disclaimer, the term of this patent is extended or adjusted under 35 U.S.c. 154(b) by 0 days.

US B2. ( *) Notice: Subject to any disclaimer, the term of this patent is extended or adjusted under 35 U.S.c. 154(b) by 0 days. 111111 1111111111111111111111111111111111111111111111111111111111111 US006981576B2 (12) United States Patent Amo et ai. (10) Patent o.: US 6,981,576 B2 (45) Date of Patent: *Jan. 3, 2006 (54) FORMATO DSPLAY

More information

tj tj D... '4,... ::=~--lj c;;j _ ASPA: Automatic speech-pause analyzer* t> ,. "",. : : :::: :1'NTmAC' I

tj tj D... '4,... ::=~--lj c;;j _ ASPA: Automatic speech-pause analyzer* t> ,. ,. : : :::: :1'NTmAC' I ASPA: Automatc speech-pause analyzer* D. GERVERt and G. DNELEY Unversty of Durham, Durham, England ASPA: The Programs Snce the actual detals of nterface samplng, dsk storage routnes, etc., wll depend upon

More information

A Comparative Analysis of Disk Scheduling Policies

A Comparative Analysis of Disk Scheduling Policies A Comparatve Analyss of Dsk Schedulng Polces Toby J. Teorey and Tad B. Pnkerton Unversty of Wsconsn* Fve well-known schedulng polces for movable head dsks are compared usng the performance crtera of expected

More information

T-25e, T-39 & T-66. G657 fibres and how to splice them. TA036DO th June 2011

T-25e, T-39 & T-66. G657 fibres and how to splice them. TA036DO th June 2011 T-25e, T-39 & T-66 G657 fibres ad how to splice them TA036DO0018-03 10 th Jue 2011 What is G657 fibre? G657 is a ew class of sigle mode fibre which ca be bet more severely the ormal G652 sigle mode without

More information

A Model of Metric Coherence

A Model of Metric Coherence A Model of Metric Coherece Ier Metric Aalysis u Metric weight, Metric Coherece Aja Volk Uiversity of Souther Califoria Itegrated Media Systems Ceter Ier Metric Aalysis Ier Metric Aalysis u Metric weight,

More information

the who Produced by Alfred Music P.O. Box Van Nuys, CA alfred.com Printed in USA. ISBN-10: ISBN-13:

the who Produced by Alfred Music P.O. Box Van Nuys, CA alfred.com Printed in USA. ISBN-10: ISBN-13: the who he Who, November 197 L to R: Pete owshed, Keith Moo, Roger altrey ad oh Etwistle Photo: riifold Maagemet Produced by lfred Music P.O. ox 1 Va Nuys, C 9141- alfred.com Prited i US. No part of this

More information

PIANO SYLLABUS SPECIFICATION. Also suitable for Keyboards Edition

PIANO SYLLABUS SPECIFICATION. Also suitable for Keyboards Edition PIANO SYLLABUS SPECIFICATION Also suitable for Keyboards 2016 Editio Piao Syllabus Specificatio 2016 Editio Rockschool Performace Arts Awards Vocatioal Qualificatios Ackowledgemets Syllabus Syllabus writte

More information

LOW-COMPLEXITY VIDEO ENCODER FOR SMART EYES BASED ON UNDERDETERMINED BLIND SIGNAL SEPARATION

LOW-COMPLEXITY VIDEO ENCODER FOR SMART EYES BASED ON UNDERDETERMINED BLIND SIGNAL SEPARATION LOW-COMPLEXITY VIDEO ENCODER FOR SMART EYES BASED ON UNDERDETERMINED BLIND SIGNAL SEPARATION Jng Lu, Fe Qao *, Zhjan Ou and Huazhong Yang Department of Electronc Engneerng, Tsnghua Unversty ABSTRACT Ths

More information

Simon Sheu Computer Science National Tsing Hua Universtity Taiwan, ROC

Simon Sheu Computer Science National Tsing Hua Universtity Taiwan, ROC Mounr A. Tantaou School of Electrcal Engneerng and Computer Scence Unversty of Central Florda Orlando, FL 3286-407-823-393 tantaou@cs.ucf.edu Interacton wth Broadcast Vdeo Ken A. Hua School of Electrcal

More information

Practice Guide Sonata in F Minor, Op. 2, No. 1, I. Allegro Ludwig van Beethoven

Practice Guide Sonata in F Minor, Op. 2, No. 1, I. Allegro Ludwig van Beethoven Practice Guide Soata i F Mior, O 2, No 1, I Allegro Ludwig va Beethove Comosed i 1795, the Soata i F Mior, O 2, No 1 was dedicated to Hayd, whom Beethove admired ad had briefly studied with three years

More information

Detecting Errors in Blood-Gas Measurement by Analysiswith Two Instruments

Detecting Errors in Blood-Gas Measurement by Analysiswith Two Instruments CLIN. CHEM. 33/4, 512-517 (1987) Detectng Errors n Blood-Gas Measurement by Analysswth Two Instruments LouIs F. Metzger, Wllam B. Stauffer, Ann V. Kruplnskl, Rchard P. MIIlman,3 George S. Cembrowskl,2

More information

Integration of Internet of Thing Technology in Digital Energy Network with Dispersed Generation

Integration of Internet of Thing Technology in Digital Energy Network with Dispersed Generation Amercan Scentfc Research Journal for Engneerng, Technology, and Scences (ASRJETS) ISS (Prnt) 2313-4410, ISS (Onlne) 2313-4402 Global Socety of Scentfc Research and Researchers http://asrjetsjournal.org/

More information

Expressive Musical Timing

Expressive Musical Timing Axel Berndt, Tlo Hähnel Department of Smulaton and Graphcs Otto-von-Guercke Unversty of Magdeburg {aberndt tlo}@sg.cs.un-magdeburg.de Abstract. Tmng s crucal for the qualty of expressve musc performances.

More information

Background Manuscript Music Data Results... sort of Acknowledgments. Suite, Suite Phylogenetics. Michael Charleston and Zoltán Szabó

Background Manuscript Music Data Results... sort of Acknowledgments. Suite, Suite Phylogenetics. Michael Charleston and Zoltán Szabó Suite, Suite Phylogeetics /5 Suite, Suite Phylogeetics Michael Charlesto ad Zoltá Szabó michael.charlesto@sydey.edu.au November 5th, 20 Suite, Suite Phylogeetics 2/5 S Bach oha Sebastia Bach was bor i

More information

Energy-Efficient FPGA-Based Parallel Quasi-Stochastic Computing

Energy-Efficient FPGA-Based Parallel Quasi-Stochastic Computing Article Eergy-Efficiet FPGA-Based Parallel Quasi-Stochastic Computig Ramu Seva, Prashathi Metku * ad Misu Choi Departmet of Computer Egieerig, Missouri Uiversity of Sciece & Techology, 4 Emerso Electric

More information

INSTRUCTION MANUAL FOR THE INSTALLATION, USE AND MAINTENANCE OF THE REGULATOR GENIUS POWER COMBI

INSTRUCTION MANUAL FOR THE INSTALLATION, USE AND MAINTENANCE OF THE REGULATOR GENIUS POWER COMBI NSTRUCTON MANUAL FOR THE NSTALLATON, USE AND MANTENANCE OF THE REGULATOR GENUS POWER COMB (TRANSLATON OF THE ORGNAL NSTRUCTON MANUAL N TALAN) PRELMNARY VERSON WARRANTY The devce s guaranteed 24 months

More information

Product Information. Universal swivel units SRU-plus

Product Information. Universal swivel units SRU-plus Product Informaton Unversal swvel unts SRU-plus SRU-plus Unversal swvel unts Robust. Fast. Hgh Performance. SRU-plus unversal rotary actuator Unversal unt for pneumatc swvel and turnng movements. Feld

More information

AREA (SQ. FT.) BREAKDOWN: 1. SALES AREA: 2. ENTRY VESTIBULE (EXT.): 3. SERVICE: 4. TOILET ROOM: 5. OFFICE: 6. STAIRWAY/REAR EXIT: 7.

AREA (SQ. FT.) BREAKDOWN: 1. SALES AREA: 2. ENTRY VESTIBULE (EXT.): 3. SERVICE: 4. TOILET ROOM: 5. OFFICE: 6. STAIRWAY/REAR EXIT: 7. Exhibit 6 35 ORTH SATA CRUZ AVEUE LOS GATOS, CA 953 PURVEYORS of SWEETESS 245 Ford Parkay, Suite 3 65.69.5525.finndaniels.com LEVEL: OF VCTY MAP MALL KEY PLA PROJECT TEAM BREAKDOW:. SALES AREA: 2. ETRY

More information

arxiv: v1 [cs.cl] 12 Sep 2018

arxiv: v1 [cs.cl] 12 Sep 2018 Powered by TCPDF (www.tcpdf.org) Neural Melody Composton from Lyrcs Hangbo Bao, Shaohan Huang 2, Furu We 2, Le Cu 2, Yu Wu 3, Chuanq Tan 3, Songhao Pao, Mng Zhou 2 School of Computer Scence, Harbn Insttute

More information

Accepted Manuscript. An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time

Accepted Manuscript. An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time Accepted Manuscrpt An mproved artfcal bee colony algorthm for flexble ob-shop schedulng problem wth fuzzy processng tme Ka Zhou Gao, Ponnuthura Nagaratnam Suganthan, Quan Ke Pan, Tay Jn Chua, Chn Soon

More information

Product Bulletin 40C 40C-10R 40C-20R 40C-114R. Product Description For Solvent, Eco-Solvent, UV and Latex Inkjet and Screen Printing 3-mil vinyl films

Product Bulletin 40C 40C-10R 40C-20R 40C-114R. Product Description For Solvent, Eco-Solvent, UV and Latex Inkjet and Screen Printing 3-mil vinyl films Product Bulletn 40C Revson D, Effectve February 2016 (Replaces C, Apr. 15) 40C-10R 40C-20R 40C-114R Product Descrpton For Solvent, Eco-Solvent, UV and Latex Inkjet and Screen Prntng 3-ml vnyl flms Quck

More information

Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms

Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms Complex Intell. Syst. (2018) 4:55 65 https://do.org/10.1007/s40747-017-0052-x ORIGINAL ARTICLE Automated composer recognton for mult-voce pano compostons usng rhythmc features, n-grams and modfed cortcal

More information

Loewe bild 7.65 OLED. Set-up options. Loewe bild 7 cover Incl. Back cover. Loewe bild 7 cover kit Incl. Back cover and Speaker cover

Loewe bild 7.65 OLED. Set-up options. Loewe bild 7 cover Incl. Back cover. Loewe bild 7 cover kit Incl. Back cover and Speaker cover Product nformaton Loewe bld 7.65 Page of March 07 Loewe bld 7.65 OLED EU energy effcency class: B Screen dagonal (n cm) / Screen dagonal (n nch): 64 / 65 Power consumpton ON (n W): 80 Annual energy consumpton

More information

Color Monitor. L200p. English. User s Guide

Color Monitor. L200p. English. User s Guide Color Montor L200p User s Gude Englsh Frst Edton (February / 2003) Note : For mportant nformaton, refer to the Montor Safety and Warranty manual that comes wth ths montor. Contents ENGLISH Safety (Read

More information

THE Internet of Things (IoT) is likely to be incorporated

THE Internet of Things (IoT) is likely to be incorporated This is the author's versio of a article that has bee published i this oural. Chages were made to this versio by the publisher prior to publicatio. IEEE INTERNET OF THINGS JOURNAL, VOL. 5, NO. 1, FEBRUARY

More information

Hybrid Transcoding for QoS Adaptive Video-on-Demand Services

Hybrid Transcoding for QoS Adaptive Video-on-Demand Services 732 IEEE Transactons on Consumer Electroncs, Vol. 50, No. 2, MAY 2004 Hybrd Transcodng for QoS Adaptve Vdeo-on-Demand Servces Ilhoon Shn and Kern Koh Abstract Transcodng s a core technque that s used n

More information

COMMITTEE ON THE HISTORY OF THE FEDERAL RESERVE SYSTEM. Register of Papers CHARLES SUMNER HAMLIM ( )

COMMITTEE ON THE HISTORY OF THE FEDERAL RESERVE SYSTEM. Register of Papers CHARLES SUMNER HAMLIM ( ) COMMITTEE ON THE HISTORY OF THE FEDERAL RESERVE SYSTEM Register of Papers Processed: M^ Date: 1/23/56 AC. 4886 AC. 4886 ads 1-3 CHARLES SUMNER HAMLIM (1861-1938) The papers of Charles Hamli, lawyer, Assistat

More information

S Micro--Strip Tool in. S Combination Strip Tool ( ) S Cable Holder Assembly (Used only

S Micro--Strip Tool in. S Combination Strip Tool ( ) S Cable Holder Assembly (Used only Instructon Sheet LghtCrmp* Plus LC 408-10103 (for Jacketed Cable) Connectors 18 AUG 09 Rear Protectve Cap Termnaton CoverG Boot Connector Assembly Crmp Eyelet Duplex Clp G Connector kt s shpped wth these

More information

Cost-Aware Fronthaul Rate Allocation to Maximize Benefit of Multi-User Reception in C-RAN

Cost-Aware Fronthaul Rate Allocation to Maximize Benefit of Multi-User Reception in C-RAN Cost-Aware Fronthaul Rate Allocaton to Maxmze Beneft of Mult-User Recepton n C-RAN Dora Bovz, Chung Shue Chen, Sheng Yang To cte ths verson: Dora Bovz, Chung Shue Chen, Sheng Yang. Cost-Aware Fronthaul

More information

However, in studies of expressive timing, the aim is to investigate production rather than perception of timing, that is, independently of the listene

However, in studies of expressive timing, the aim is to investigate production rather than perception of timing, that is, independently of the listene Beat Extraction from Expressive Musical Performances Simon Dixon, Werner Goebl and Emilios Cambouropoulos Austrian Research Institute for Artificial Intelligence, Schottengasse 3, A-1010 Vienna, Austria.

More information

Craig Webre, Sheriff Personnel Division/Law Enforcement Complex 1300 Lynn Street Thibodaux, Louisiana 70301

Craig Webre, Sheriff Personnel Division/Law Enforcement Complex 1300 Lynn Street Thibodaux, Louisiana 70301 DATE OF APPLCATON: Craig Webre, Sheriff Personnel Division/Law Enforcement Complex 1300 Lynn Street Thibodaux, Louisiana 70301 N GENERAL EMAL ADDRESS: For Local Calls - (985) 532-4380 (985) 446-2255 (985)

More information

The Communication Method of Distance Education System and Sound Control Characteristics

The Communication Method of Distance Education System and Sound Control Characteristics 36 IJCSNS Iteratioal Joural of Computer Sciece ad Network Security, VO.6 No.7A, July 006 The Commuicatio ethod of Distace Educatio System ad Soud Cotrol Characteristics aabu Ishihara, Departmet of Electrical

More information

Five Rounds. by Peter Billam. Peter J Billam, 1986

Five Rounds. by Peter Billam. Peter J Billam, 1986 Fve Rounds by Per Bllam Per J Bllam, 986 Ths score s offered under the Creatve Commons Attrbuton.0 Inrnatonal lcence; see creatvecommons.org The copyrght owner remans the composer, Per Bllam. Ths edton

More information

Guide to condition reports for domestic electrical installations

Guide to condition reports for domestic electrical installations Coditio rpt leaflet A5:codiio rpt leaflet A5 15022012 11:48 Page 1 Guide to coditio reports for domestic electrical istallatios Coditio rpt leaflet A5:codiio rpt leaflet A5 15022012 11:48 Page 2 Courtesy

More information

Product Information. Manual change system HWS

Product Information. Manual change system HWS Product Informaton HWS HWS Flexble. Compact. Productve. HWS manual change system Manual tool change system wth ntegrated ar feed-through and optonal electrc feed-through Feld of applcaton Excellently sutable

More information

BOUND FOR SOUTH AUSTRALIA

BOUND FOR SOUTH AUSTRALIA FULL SCOE $3.50 BOUND FO SOUTH AUSTALIA or 3-5 oct. hadbells Traditioal sea shaty arr. Alex Guebert (2015) 2015 Alex Guebert. Distributed by The Golde Dace..HadbellMusic.com Permissio is grated or licesed

More information

Video Cassette Recorder

Video Cassette Recorder 3-865-427-12(1) Video Cassette Recorder Operatig Istructios SLV-L49 MX SLV-L52 PA/PC SLV-L59 CL/CS/PR/VZ SLV-X55 MX SLV-L69HF MX SLV-L72HF PA/PC SLV-L79HF CL/CS/VZ SLV-L89HF CL/CS/MX/VZ SLV-X66HF MX 1999

More information

Supply Quantitative Model à la Leontief *

Supply Quantitative Model à la Leontief * Moer Ecoomy, 2011, 2, 642-653 o:10.4236/me.2011.24072 Pubhe Oe September 2011 (http://www.scrp.org/joura/me) Suppy Quattatve Moe à a Leotef * Abtract Ezra Davar Iepeet Reearcher, Amo Vtamar, Netaya, Irae

More information

RHYTHM TRANSCRIPTION OF POLYPHONIC MIDI PERFORMANCES BASED ON A MERGED-OUTPUT HMM FOR MULTIPLE VOICES

RHYTHM TRANSCRIPTION OF POLYPHONIC MIDI PERFORMANCES BASED ON A MERGED-OUTPUT HMM FOR MULTIPLE VOICES Proceedigs SMC 6.8. -.9.6, Hamburg, Germay RHYTHM TRANSCRIPTION OF POLYPHONIC MIDI PERFORMANCES BASED ON A MERGED-OUTPUT HMM FOR MULTIPLE VOICES Eita Nakamura Kyoto Uiversity eakamura@sap.ist.i.kyoto-u.ac.p

More information

BesTrans AOC (Active Optical Cable) Spec and Manual

BesTrans AOC (Active Optical Cable) Spec and Manual BesTras AOC (Active Optical Cable) Spec ad Maual A. Techology: BesTras Active Optical Cable (AOC) is a easy-to-use, secure coectio for home video distributio, coferece room presetatio systems, classroom

More information

Product Information. Manual change system HWS

Product Information. Manual change system HWS Product Informaton HWS HWS Flexble. Compact. Productve. HWS manual change system Manual tool change system wth ntegrated ar feed-through and optonal electrc feed-through Feld of applcaton Excellently sutable

More information

Discussion Paper Series

Discussion Paper Series Doshsha Unversty Center for the Study of the Creatve Economy Dscusson Paper Seres No. 2013-04 Nonlnear Effects of Superstar Collaboraton: Why the Beatles Succeeded but Broke Up Tadash Yag Dscusson Paper

More information

MODELLING PERCEPTION OF SPEED IN MUSIC AUDIO

MODELLING PERCEPTION OF SPEED IN MUSIC AUDIO MODELLING PERCEPTION OF SPEED IN MUSIC AUDIO Aders Elowsso KTH Royal Istitute of Techology CSC, Dept. of Speech, Music ad Hearig elov@kth.se Aders Friberg KTH Royal Istitute of Techology CSC, Dept. of

More information

RELIABILITY EVALUATION OF REPAIRABLE COMPLEX SYSTEMS AN ANALYZING FAILURE DATA

RELIABILITY EVALUATION OF REPAIRABLE COMPLEX SYSTEMS AN ANALYZING FAILURE DATA It. J. Mech. Eg. & Rob. Res. 2013 G Gurumahesh et al., 2013 Research Paper ISSN 2278 0149 www.ijmerr.com Vol. 2, No. 1, Jauary 2013 2013 IJMERR. All Rights Reserved RELIABILITY EVALUATION OF REPAIRABLE

More information

2 Specialty Application Photoelectric Sensors

2 Specialty Application Photoelectric Sensors SMARTEYE X-PRO XP10 XP10 -- Extremely High Speed Sesor 2 Specialty Applicatio Photoelectric Sesors 2-119 Specialty Applicatio Photoelectric Sesors 2 SMARTEYE X-PRO XP10 Extremely High Speed (10µs) Photoelectric

More information

T541 Flat Panel Monitor User Guide ENGLISH

T541 Flat Panel Monitor User Guide ENGLISH T541 Flat Panel Montor User Gude ENGLISH Frst Edton (June / 2002) Note : For mportant nformaton, refer to the Montor Safety and Warranty manual that comes wth ths montor. Ths publcaton could contan techncal

More information

A Quantization-Friendly Separable Convolution for MobileNets

A Quantization-Friendly Separable Convolution for MobileNets arxv:1803.08607v1 [cs.cv] 22 Mar 2018 A Quantzaton-Frendly Separable for MobleNets Abstract Tao Sheng tsheng@qt.qualcomm.com Xaopeng Zhang parker.zhang@gmal.com As deep learnng (DL) s beng rapdly pushed

More information

System of Automatic Chinese Webpage Summarization Based on The Random Walk Algorithm of Dynamic Programming

System of Automatic Chinese Webpage Summarization Based on The Random Walk Algorithm of Dynamic Programming Send Orders for Reprnts to reprnts@benthamscence.ae The Open Cybernetcs & Systemcs Journal, 205, 9, 35-322 35 Open Access System of Automatc Chnese Webpage Summarzaton Based on The Random Walk Algorthm

More information