J our nal ofCommuni cat i on and Comput er 9( 2012)234— 241 … N BandNavi :Band. . M em ber Backtr acker Based on M em ber Hi story I nform ati on M asat oshi Hamanaka and Mi ki t o Yoshi ya De pa r t me n t o fl nt e l l i ge nt I nt e r ac t i o n Te c h nol o gi e s .Un i v er s i t y o fT s uk u ba 30 5— 8 57 3 1- I ・ l T e nn ou dai T s u k u ba l b ar a ki , J a pan Recei ved:Sept ember 05,20 1 1/Accept ed:November 07,20 1 1/Publ i shed:Febr uar y 29,20 l 2 Abst ract:A band. member . backt r acki ng appl i ca t i on cal l ed Ba nd Navi has been de vel oped t ha t enabl es a user t o di s cover new songs and bands by t r aci ng musi ci ns a who ha ve pl ayed i n di f f er ent ba nds.Pr evi ous si mi l ar i t y— bas ed s ong r ec ommendat i on s ys t ems onl y r et r i eve si mi l ar s ongs ,but many r et r i eved r esul t s ar e not s ongs by mus i ci ans t he us er l i kes .By us i ng a web- mi ni ng t echni que,dat a was col l ect ed on me m ber hi s t or y i nf or mmi on of 3000 bands .Ba n dNa vi enabl es a us er t o di s cover ot her bands i n whi c h a m usi ci a n ha s p l a y e d. Ba nd Na v i wa s i mpl e me n t e d o n t he i Pho ne .a n d e x pe r i me nt a l r e s u l t s s ho w t h a t 70 p e r c e n t o f s u b j e c t s u nd e r s t oo d ho w t o us e Ba ndNavi wi t hi n f i ve mi nut es 70 per cent enj oyed t r aci ng t he ba nds n d a t hei r me mber s’connect i ons,a n d 52 per cent di s cover ed a pr evi ous l y unknown band t hat t hey l i ked. Key words:M usi c r ecomme nda t i on,ba n d- member ,web mi ni ng t echni que,ba n d net wor k,i Phone appl i cat i on. I.IntrOductj0n buy songs di scover ed by usi ng t he navi gat i on f unct i on. W e can now easi l y l i st en t o and st ore enor m ous numbers of songs on M P3 pl ayers and smar tphones. BandN avi di spl ays bands and t hei r members by usi ng band-l i neup hi st ori es. W e can al so sel ect and buy songs ver y easi l y t hrough A musi ci an wi l l of ten have pl ayed i n or wi t h more onl i ne musi c st or es.However,users m ay not be abl e to di scover many songs t hey may l i ke because t hey spend ver y l i t tl e ti me choosi ng songs t o buy even though t hey spend use a l ot ofti me l i s teni ng t o musi c. Ther ef ore, we have devel oped a band—member backt r acki ng appl i cat i on cal l ed BandNavi t hat enabl es a user t o di scover previ ousl y unknow n songs or ar t i st s whi l e l i s t eni ng to anot her song or ar ti st .The mai n advant age of BandNavi i s t hat i t has t he f ol l owi ng t wo m odes. ( 1 1 Navi ga t i on mode In t he navi gat i on mode, a user can di scover connect ed ar t i st s and songs whi l e l i st eni ng t o a song on am usi cpl ayer. ( 2)Medi a acc es s mode In the medi a access mode.a user can l i s ten t o and Corre spondi ng author: M asa t os hi Hamanaka, Ph. D. pr of es s or , res ear ch f iel ds : mus i c i nf or mat i on pr oc es si ng E- mai l : h a ma na k a @i i t . t s uk ub a. ac . j p. t han one band;he or she may have been a gues t musi ci an and/ or have been a member of a number of bands.Ther ef ore,r el ati onshi ps bet ween musi ci ans and bands for m a com pl ex net wor k.W e cal l t he net wor k for m ed by connecti ng t he af il i at i ons bet w een bands and t hei r me mbe r s t he Band Ne t wor k ( Fi g. 1 ) . BandNavi can di spl ay t he bands and musi ci ans t hat are connect ed t o the s ong current l y bei ng l i st eni ng to by t raci ng t hi s net wor k. To ind f t he net works of connect i ons bet ween bands and thei r members,we use web— mi ni ng t echni ques. However, t her e are t wo probl ems wi t h usi ng t he net works acqui r ed by web— mi ni ng t echni ques for t r aci ng m usi ci ans’hi st ori es.The f irs t pr obl em i s t hat web- mi ni ng t echni ques are not per f ect and someti mes col l ect wr ong i nf or m at i on. Even i nf or mat i on f rom Wi ki pedi a has some err ors,and i t i s ver y di i cul f t t o det er m i ne t hese erors by usi ng web— mi ni ng t echni ques. BandNavi :Band- Mem ber Backtr acker Based on M em ber Hi st or y I nf or m at i on 235 expl ai ns t he experi ment al resul t s, and Sect i on 7 concl udes t he paper. 2.Rel at ed W ork Previ ous musi c- retr i eval met hods t hat use queri es s uch a s s i mi l a r i t y— bas e d[ 1- 3]s ea r c hi ng a nd t ext — bas ed s e ar chi ng【 4]a r e us ef ul f or nar r owi ng a l a r ge number of musi cal pi eces,but af ter the l i s t of r anki ngs has been Joe L ynn Tur ner Fi g. 1 Net work showi ng connecti ons bet ween m us i ci ans provi ded, we have t o l i st en t o songs one—by—one because no consi derat i on has been gi ven to f indi ng and bands . songs one f or whi ch has an af i ni t y f rom the l i st.Thus The wrong i nfor m at i on obst ructs t he users t ryi ng t o user s s pend a l ot of t i me l i s teni ng t o new songs whi l e sear ch f or and f ind ar t i st s t hey l i ke. The second not al ways di scoveri ng ones t hey l i ke. probl em i s t hat i t i s very compl i cated and di i cul f t t o Mus i c r ea m [ 5】makes i t pos s i bl e t o i nt e r ac t wi t h show al l t he members of a band i n whi ch many m any musi c col l ect i ons by appl yi ng a si mi l ar i t y- based m usi ci ans have pl ayed.For exampl e,W hi t esnake has st i cki ng f uncti on whi ch at taches si mi l ar musi cal pi eces had more t han t hi rt y members i n tot a1 .If t he syst em t o a musi cal pi ece sel ect ed by the user.However, onl y shows a l i st of W hi tesnake’ s pas t and pr esent M usi cream has t he same pr obl em as previ ous member s.i t i s ver y di i cul f t to di scover cl osel y rel at ed musi c— r e t r i eval met hods[ 1 - 4] ,be ca us e us er s do not bands l i ke Deep Pur pl e. al ways l i ke songs t hat are si mi l ar t o ot her songs t hey To sol ve these probl ems.BandNavi l i st s members l i ke. of a band f ound whi l e web mi ni ng i n or der of On t he ot he r hand,c ol l abor a t i ve f il t e r i ng [ 6,7] i mpor tance.Ther efor e, a user can di scover ar ti s t s enabl es us ers wi t h si mi l ar t ast es t o be f ound and cl osel y rel at ed to hi s/ her favori t e band by tr aci ng t he recommends these si mi l ar users’ favor i t e songs by connecti ons oft he m or e i m port ant band members. usi ng t hei r song— pur chase hi st ori es.However t hese , I n t he experi ment , we eval uated how wel l web m et hods have di i cul f t y r ecommendi ng s ongs that are mi ni ng col l ect s t he names of band members.W e used not fam ous.because i t i s di 艏 cul t t o col l ect unfamous 50 bands for t hi s exper i ment ,and 86 percent of resul t s bands’songs f r om song— purchase hi st ori es . consi s t of t he top f ive m os t i mpor tant m em bers of W e t hi nk one way for di scoveri ng an unknown di f ferent bands.W e al so conduct ed an experi ment musi cal m ast er pi ece i s t o recei ve recommendat i ons wi t h 1 7 s ub j ec t s:70 per cent under s t ood how t o us e rom peopl f e who are knowl edgeabl e about musi c BandNavi wi t hi n f ive mi nu t es ,70 per c ent s e n j oyed because t hey probabl y know t he backgr ound t r aci ng t he ba n ds and t hei r m embers’connecti ons,and i nf or m at i on of songs.Our goal i s to devel op a syst em 52 percent di scover ed a band t hey l i ked but di d not t hat ext ract s deep knowl edge about songs by web know befor e. mi ni ng t hat enabl es a user t o f ind songs i n t he same Thi s paper i s organi zed as f ol l ows.Secti on 2 br i ef ly di scusses t he rel at ed wor ks. Sect i on 3 gi ves an , way as a person who i s knowl edgeabl e about musi c. The i dea behi nd M us i cRa i nbow 【 8】i s s i mi l ar t o our s 。 over vi ew of BandNavi and di scusses t he pr obl ems i n because M usi cRai nbow ext r acts knowl edge f r om the devel opi ng t he syst em and t hei r sol ut i ons.Secti on 4 web. M usi cRai nbow di s pl ays s ongs ci rcul arl y expl ai ns t he user i nt er.f ace of BandNavi .Secti on 5 dependi ng on audi o—based si mi l ari t y and adds wor ds descri bes how t o f ind t he Band Net wor k.Secti on 6 retr i eved fr om web pages t o descri be each ar ti st . BandNavi :Band- M em ber Backtr acker Based on M em ber Hi st or y I nf ormat i on m em bers。 and gues t and/or sessi on musi ci ans f or r ecor di ngs and/ or l i ve perf or m ances.If we di spl ay t hi s compl i cated net wor k on i Phone as a cobweb,i t i s ver y di i cul f tt o fol l ow. 237 3. 2. 2 Aut omati c Acqui si t i on of Band M ember N am es To acqui re names of a band’ s member,we use web-mi ni ng t echni ques i n whi ch we f irs t col l ect web To si mpli fy t he net wor k,we can omi t t he gues t and pages about the band by usi ng Googl e sear ch API and sessi on musi ci ans t hat provi de weak connect i ons t hen acqui r e t he band members’ names by a between bands. However, om i t t i ng t hese weak pater n— mat chi ng met hod. Because web— mi ni ng connecti ons i s pr obl emat i c because the omi tt ed techni ques somet i mes col l ect wr ong resul ts,we al so i nfor m at i on can be i mport ant for t he user who want s prepared a manual edi t i ng opt i on f or band member’ s know about t he band m ore deepl y. names.Onl y t he bi g f ans of a par ti cul ar band can use 3.1. 2 How t o Acqui r e M em ber s’Names t hi s manual edi t or,because t hey ar e ass umed t o be Wi ki pedi a and M usi cBr ai nz have pages f or bands knowl edgeabl e about t he band.The BandNavi hel ps t hat l i s t t hei r mem bers’names.However,t hese pages t he user to edi t i ng t he i nf or m ati on of band m em bers’ ar e not enough,because many new or mi nor bands do names by di s pl ayi ng t he web pages f rom whi ch t he not have pages on Wi ki pedi a or M usi cBr ai nz,and even band m embers’names was extr act ed by web—mi ni ng when they do,most of t hese pages do not have t he t echni ques. i nf ormati on about t hei r members.Fur thermore,guest or sessi on musi ci ans are not ment i oned on m ost band 4.U ser Int erf ace pages on W i ki pedi a and M usi cBrai nz,s o i nf or m ati on BandNavi consi s ts of a cl i ent appl i cati on on t he or f cons tr uct i ng t he net wor k cannot be suf i ci f ent l y i Phone and a ser ver appl i cat i on t hat aut omat i cal l y acqui red rom f t hese pages. acqui res t he band m em ber s’ nam es. The BandN avi cl i ent appl i cat i on has t hree modes:navi gati on mode 3. 2 Sol ut i onsfo,The se Pr obl ems 0ur sol ut i on f or t hese probl ems l s to i nt r oduce web ( s e ct i on 4. 1 ) ,medi a ac ce ss mode( s ec t i on 4. 2) ,and br ows e mode( s e ct i on 4- 3)( Fi g.3) . mi ni ng—methods t hat col l ect t he i nf or m ati on of m embers’name by tr awl i ng t hrough web pages. 3. 2. 1 I nt roduce I mpor t ance Level of M embers To sol ve t he pr obl em of compl i cat ed net wor ks,we i nt roduce an i mpor tance l evel f or mem ber s. W e 4. 1 Navi gat i onM ode I n navi gat i on mode,we can t race musi ci ans’wor k by i terat i vel y l i st i ng band members and ot her bands i n whi ch each member has pl ayed. cal cul at e t he i mpor tance l evel of each m ember i n a W hen a user pl ays a song usi ng t he BandNavi cl i ent band by usi ng document f requency i n t he web pages appli cati on on an i Phone, t he cl i ent appl i cat i on when col l ect i ng band member s’names usi ng Googl e aut omat i cal l y acqui r es and di spl ays member s’names API.Thus BandNavi l i st s band members i n t er ms of by communi cat i ng wi t h t he dat a ba s e on t he s e r ver( Fi g. t hei r order of i mpor tance.For exampl e,a band mem ber 3a ) .The n,bands i n whi c h a mus i c i an ha s pl ayed a r e who pl ayed i n t he band f or a l ong t i me and i s of t en di s pl a ye d whe n a us e r t aps hi s / he r name( Fi g.3b) . Ne xt , m ent i oned on t he web pages i s l i st ed hi gher t han a names of anot her band’ s member s ar e di spl ayed when band member who i s i nf requentl y ment i oned on a us e r t a ps t hat ba nd’ s name( Fi g.3 d) . t he web pages.Theref ore a user can expl ore t he The band— me mbe r l i s t( Fi gs.3a a nd 3d)and Band Net wor k by consi deri ng t he st r engt h of bands — pl ayed— i n l i s t ( Fi g. 3b) a r e or der ed by rel at i ons hi p bet ween bands and t hei r past and pr esent i mpor tance l evel s. In t he band— member l i st , each m em bers. me mber ’ s name has t he r el e vant i ns t r ument ( s 1 wr i t t en 240 BandNavi :Band— Mem ber Backtr acker Based on Mem ber Hi stor y I nform ati on of extr act ed web page,and t he l ocati on i n t he web page met al because t he l i neups of heavy m et al bands of ten where t he pat t er n was m at ched. change,so f ew bands ar e i sol ated f rom ot her bands i n t he Band— Net wor k. 5. 3 M emberName Fi l t eri ng I n t he member name f il teri ng,the ser ver appl i cati on 6. 1 Eval uat i o n o f Aut omat i c Ac qui si t i on o f Band M embers’ Nnm es r emoves t he names of musi ci ans who have not pl ayed i n the band f rom musi ci ans’names extr act ed i n secti on 5. 2.The member name il f t er i ng consi st s oft wo st eps. W e eval uat ed how wel l t he syst em f o und t he band members’ names. Thi s eval uat i on r equi red us t o (1 )Re move wr ong mus i ci a ns name pr epar e t he corr ect dat a sets of band names and band The syst em r emoves names of m usi ci ans t hat have members’names.W e sel ect ed 50 bands randoml y fr om ver y l ow possi bi l i t y of bei ng band members.Fi rs t,t he t he 3 54 bands and m anual l y i nvest i gat ed t he member s’ s ys t em c al c ul a t es t he doc ume nt ̄e quenc y( DF) ,whi ch names,i ncl udi ng t hose of guest or sessi on musi ci ans, i ndi cat es how many t i mes t he name appear s i n t he by usi ng of i ci al web pages and CD bookl et s.Then we col l ect ed web pages.The DF i s nor m al i zed f rom 0 to 1. as ked someone who knows a l ot about heavy met al Second,the s yst em rem oves t he m usi ci ans’names f or musi c t o ver if y t he r esul t s manual l y. whi ch DF i s l owe r t ha n t he t hr e s hol d( t h) . W e eval uat ed t he Recal l and Preci si on of BandNavi . ( 2)Ext r ac t band member col l oca t i on Recal l and Preci si on dif fer dependi ng on t he num ber of The s ys tem ext ract s band member col l ocat i on t o ind f m usi ci ans to be i nvest i gat ed f rom t op D F m usi ci an t he actual band m embers,because we can ass ume the wher e t he BandNavi shows t he band. member l i st t hat same band members ar e menti oned on nearl y ever y i s soaed by DF.Tabl e 1 shows t he r esul t s of t he web page about the band. Fi rs t ,t he syst em sel ects t he two musi ci ans t hat have t he hi ghest DF as t he most i mpor t ant members of t he band.Second,t he syst em sort s t he t upl es f rom t he same w eb pages and t hen di vi des them i nt o gr oups w her e t he pat t er ns f or extr act i ng m usi ci ans’nam es ar e changed. experi ment .Bot h Recal l and Preci si on ar e over 0. 7 when we use al l members or f eval uat i on.W hen we use t he t en member s wi t h t he hi ghest DF,Preci si on rai ses to 0. 81.W hen we use t he f ive member s wi t h t he hi ghest DF。Pr eci si on rai ses even m or e to 0. 86. 6. 2 Ev al u at i on b y a Que s t i onnai r e Fi nal l y, t he syst em extr act s band mem bers t hat col l ocat e i n a same gr oup as t he two mos t i mpor tant members. I n Fi g.4,Name 1 and Name 2 i ndi cate t he i mport ant m embers.Name 3 i s ext ract ed as a band m ember, because N am e 3 i s i n t he sam e group as N am es 1 and 2. On t he ot her si de,Names 4 and 5 are r emoved,because Name 4 and 5 ar e not i n t he same group as Names 1 w e prepar ed quest i onnai res about BandNavi and pos t ed t hem at t he fol l owi ng URL: ht t p: / / musi cdb. i i t . t s ukuba.ac. j p/ bandna vi . ht m1 .So f ar , we have r ecei ved 1 7 responses f rom user s.The r esul t s i ndi ca t e t hat 70 per c ent of s ub j ec t s under s t ood how t o use t he BandNavi wi t hi n f ive mi nut e.70 percent e  ̄oye d t r aci ng t he bands ’a nd t hei r me mbe r s ’ connect i ons, and 52 percent di scovered a new band and 2. 6.Experi m ent al Resul ts Tabl e 1 Recal l and preci si on f or acqui si t i on of band m em ber s’nam es . W e i nput t o t he syst em the names of 3 54 bands cl assi ied as ‘ f ‘ heavy met al ” appeari ng on t he home pages of r ecor d compani es,resul t i ng i n t he syst em col l ecti ng 3, 508 musi ci ans’names.W e sel ect ed heavy BandNavi :Band- Mem ber Backtr acker Based on M em ber Hi st or y I nf or m at i on t hey l i ke.However,f ive per cent needed mor e t han 1 5 [ 2] F.Vi gnol i .S.Pauws.A mus i c r et r i eva1 s ys t em bas ed on us er - dr i ven si mi l ar i t y and i t s eval uat i on. i n:Pr oc.of mi nutes to underst and how t o use BandN avi.W e pl an t o i nvest i gate why some peopl e t ook s uch a l ong ti me 24l I SM I R 2005.2005.PP.272. 279. 【 3】 E.Pampal k.A MATLAB t ool box t o comput e mus i c si mi l ar i t y f rom audi o i n:Pr oc.of I SM I R2004.2004.PP. t o unders tand BandN avi , and we w i l l i mpl ement 254. 257. changes i f necessar y. ng,A. F.Sm ea t on.Eval uat i ng a musi c i nf or ma t i on 【 4】 T.Sodi r et r i eval s ys t em — TREC s t yl e.i n:Pr oc.of I SM I R2002. 7.Concl usi ons W e devel oped BandNavi ,whi ch enabl es a user t o 2002,PP.7 1 -78. [ 5】 i nt e r f ace f or s t re ami ng, s t i cki ng, s or t i ng,and r ecal l i ng sear ch for new songs and art i st s usi ng an i Phone cl i ent appl i cat i on based on rel at i ons hi ps bet ween bands and mus i c alpi eces ,i n: Pr oc.oflSM I R 2005,2005,PP. 404- 411. 【 6 】 W. W . Cohen. W . Fa n . W eb. col l abor at i ve f il t er i ng: Recomm endi ng mus i c by cr awl i ng t he we b,Comput er band members. The BandNavi i s now avai l abl e t hr ough Appl e’ s M .Got o. T. Got o. Musi cr eam :new mus i c pl aybac k Ne t wor ks 33 f 2000)685. 698. [ 7J A.Ui t denboger d,R.va n Schyndel ,A r evi ew of f act or s App St ore.I n t he experi ment,we onl y used 357 heavy af f ect i ng musi c r ecommender s ucces s. i n: Pr oc. met al bands;we have si nce added 3000 m or e bands I SM I R2002.2002.PP.204. 208. fr om t he r ock and pop genr es. W e pl an to ext end t he syst em so that user s car l f ind new musi c by usi ng t he i nf ormati on about t he names of not onl y band m em ber but al so com posers,ar ranger s, pr oducers, or r ecordi ng st udi os acqui r ed by web— mi ni ng t echni ques. R ef erences [ 1 】 G.Tz a ne t a ki s ,P.Co o k,Mu s i c al ge n r e c l a s s i ic f a t i on of audi o si gnal s ,i n:IEEE Tr a ns.on Speech and Audi o Pr oc. , 2002,PP.293- 302. 【 8】 M. G. E.Pampal k.M us i c Rai nbow:A new us er i nt er f ace t o di s cover ar t i s t s us i ng audi obas ed s i mi l  ̄i y t n d a web. bas ed l abel i ng.i n:I SM I R I nt er nat i onal Conf er e nce on M us i c I nf or mat i on Ret r i eva1 .2006.PP.367. 3 7O. .K.I s hi da,T. Ni s hi mur a, [ 9】 Y.M atsuo.J.M ori,M .Hamasaki H.Takeda,K.Has i da,M . I . s hi z uka et a1 . .Pol yphonet: An advanced s oci al net wor k ext r act i on s ys t em.J our na1 of W eb Sem a nt i cs 5(20071 262— 278. abl e onl i ne at :ht t p: / / www. yout ube. com . 【 l O 】 YouTube,avail Tune M us i c St or e. avai l abl e onl i ne a t: 【 1 1 】 Apple i ht t p: / / www. appl e. com/ i t unes .20l1. 【1 2】 Googl e Code,avai l abl e onl i ne at :ht t p: / / code. googl e. com, 20l1 .
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