Research Article Discrete and Dual Tree Wavelet Features for Real-Time Speech/Music Discrimination

Transcription

1 Intrnational cholarly Rsarch Ntwork IRN ignal Procssing Volum, Articl ID 6936, pags doi:.54//6936 Rsarch Articl Discrt and Dual Tr Wavlt Faturs for Ral-Tim pch/music Discrimination Timur Düznli, and Nalan Özkurt, 3 Graduat chool of Natural and Applid cincs, Dokuz Eylul Univrsity, 356 Buca, İzmir, Turky Dpartmnt of Elctronics and Tlcommunications Enginring, Izmir Univrsity of Economics, 3533 Balçova, İzmir, Turky 3 Dpartmnt of Elctrical and Elctronics Enginring, Yaşar Univrsity, 35 Bornova, İzmir, Turky Corrspondnc should b addrssd to Nalan Özkurt, nalan.ozkurt@du.du.tr Rcivd 5 January ; Accptd March Acadmic Editor: P. C. Yun Copyright T. Düznli and N. Özkurt. This is an opn accss articl distributd undr th Crativ Commons Attribution Licns, which prmits unrstrictd us, distribution, and rproduction in any mdium, providd th original work is proprly citd. Th prformanc of wavlt transform-basd faturs for th spch/music discrimination task has bn invstigatd. In ordr to xtract wavlt domain faturs, discrt and complx orthogonal wavlt transforms hav bn usd. Th prformanc of th proposd fatur st has bn compard with a fatur st constructd from th most common tim, frquncy and cpstral domain faturs such as numbr of zro crossings, spctral cntroid, spctral flux, and Ml cpstral cofficints. Th artificial nural ntworks hav bn usd as classification tool. Th principal componnt analysis has bn applid to liminat th corrlatd faturs bfor th classification stag. For discrt wavlt transform, considring th numbr of vanishing momnts and orthogonality, th bst prformanc is obtaind with Daubchis8 wavlt among th othr mmbrs of th Daubchis family. Th dual tr wavlt transform has also dmonstratd a succssful prformanc both in trms of accuracy and tim consumption. Finally, a ral-tim discrimination systm has bn implmntd using th Daubhcis8 wavlt which has th bst accuracy.. Introduction Th discrimination of music and spch has bn an important task in multimdia signal procssing with th incrasing rol of th multimdia sourcs in our lif. Th spch/music discrimination MD) systms can b usd in th dvlopmnt of th fficint coding algorithms for audio dcodrs []. Thus, if th spch can b sparatd, it can b codd with a spch codr which nds lss bandwidth rlativ to th audio codr. Also, spch/music discriminator is important in automatic spch rcognition whn th rcordings includ music such as radio broadcasts []. Th contnt-basd multimdia rtrival [3] and automatic channl slctor dsign for radios ar othr mrging applications with a growing intrst for spch/music discriminators. Thr ar svral faturs which ar usd to classify music and spch such as numbr of zro crossings [4], spctral cntroid, and Ml frquncy cpstral cofficints [5, 6]. Th ntropy and dynamism faturs hav bn usd for hiddn Markov modl classificationin[7]. In [8], a systm which looks for th transition btwn music and spch using two charactristics of signals, which ar RM-basd avrag dnsity of zro crossings and avrag frquncy, has bn proposd. In [9], th harmonic faturs ar usd to discriminat spch and music using a hirarchical obliqu dcision tr. An MD systm dsignd for radio broadcasts that has bn proposd in [] uss a thr-stag structur, which dtrmins th spch and music sgmnts that ar sparabl at first glanc with spctral ntropy and rgion growing. Rcntly, a sgmntation mthod has bn proposd in []whichussth proprty that th man and th variancs of th filtr bank changs mor rapidly and rachs highr valu for spch than music. inc it is important to dal with nonstationary signals and to achiv variabl tim and frquncy localization of acoustic data, th wavlt-basd paramtrs bcam an important tool in spch/music discrimination [ 5]. Howvr, thr ar som important choics to mak for rconstruction of th faturs from th wavlt cofficints. In som studis, only on lvl of wavlt dcomposition is

2 IRN ignal Procssing mployd, and th wavlt transform is applid to or 3 ms windows. To captur th nonstationary information from a tim sris, a longr window should b usd. Th windows in ordr of sconds ar usd in []; howvr, this is too long for a ral-tim application. Also, dcomposing signal into svral frquncy bands givs us mor discriminativ faturs. In anothr rcnt work, Didiot t al. mployd nrgy-basd faturs xtractd from th 5 and 7 bands of discrt wavlt componnts [5]. For audio analysis including MD and gnr classification, Tzantakis and Cook mployd man and variancs of bands of wavlt cofficints and th ratio of th man valus for adjacnt bands using Daubchis 4 wavlts in 3 sc windows []. Th accuracy of th mthod is approximatly 9%. Along with svral advantags, th discrt wavlt transform has som disadvantags such as lack of tim invarianc and oscillatory bhavior. In ordr to cop with ths problms, complx wavlt transform has bn proposd [6]. Th dual tr complx wavlt transform, which is a spcific cas of complx wavlt transform, has bn introducd in [7]. Thrfor, th aim of this study is both to considr th fatur xtraction for MD with DWT to furthr improv prformanc for ral-tim applications and to xamin th faturs obtaind from th statistical paramtrs of th dual tr wavlt cofficints. In ordr to compar th prformanc of th proposd fatur sts, th prviously usd tim, frquncy, and discrt wavlt faturs hav bn usd to classify th sam data st. Th databas has bn constructd from th spch sampls takn from TIMIT databas and th music sampls which ar rcordd from th audio CDs and radios with diffrnt gnrs such as classical, jazz, and pop. Th artificial nural ntworks ANNs) hav bn usd for th comparison of th ffctivnss of th fatur xtraction algorithms. Th papr is organizd as follows: a brif introduction of th prvious and proposd fatur xtraction mthods ar xplaind in ction ; th introduction of th data st and classification algorithm with th rsults ar givn in ction 3; th conclusions and futur works ar discussd in ction 4.. Faturs for pch/music Discrimination In this sction, th rlatd thortical background on th faturs usd for spch/music discrimination systms will b givn brifly... Common Faturs. Th tim-domain faturs such as numbr of zro crossings and frquncy-domain faturs such as low nrgy ratio, spctral cntroid, spctral roll-off and spctral flux ar commonly usd for spch/music discrimination. Also, Ml frquncy cpstrum cofficints ar shown to b succssful in spch/music classification and rcognition applications. For comparison, a fatur vctor constructd from ths faturs hav bn usd for classification in th first mthod of this study.... Numbr of Zro Crossings. It is th tim-domain fatur which rprsnts th numbr of zro crossing in a fram. It is a usful fatur in music and spch discrimination, sinc it is a masur of th dominant frquncy in th signal [5, 6]. Th numbr of zro crossings ar calculatd as Z t =.5 N sgnxn)) sgnxn )), ) n= whr xn)isthnth componnt of th fram of lngth N.... Low Enrgy Ratio. Thisfaturgivsthnumbrofth frams of which th ffctiv or root man squar RM) nrgy is lss than th avrag nrgy. Th RM nrgy for ach fram is dtrmind as X RM = K Xk K, ) whr X k is th magnitud of kth frquncy componnt in th fram. inc th nrgy distribution is mor lft-skwd than for music, this masur will b highr for spch [6]...3. pctral Cntroid. This is th masur of th cntr of mass of th frquncy spctrum and calculatd as C = k= Kk= f k X k Kk= X k, 3) whr X k is th magnitud of th componnt in th frquncy band f k [5, 6]...4. pctral Roll-off. This fatur is important in dtrmining th shap of th frquncy spctrum. Th spctral roll-off point R k is th frquncy whr th 95% of th spctral powr lis blow as summarizd in R k Xk =.95 K Xk, 4) k= k= whr X k is th magnitud of th componnt of th kth frquncy. inc th most of th nrgy is in th lowr frquncis for spch signals, R k has lowr valus for spch [5, 6]...5. pctral Flux. It rprsnts th spctral changs btwn adjacnt frams and calculatd as K ) F t = X t, k Xk t 5) k= whr Xk t is th kth frquncy componnt of th tth fram. Thnthavragofthallframsarcalculatd.Thmusic has a highr rat of changs than th spch dos, thus this valu is highr for music [5, 6]...6. Ml Frquncy Cpstrum Cofficints MFCCs). Th Ml frquncy spctrum is th linar cosin transform of

3 IRN ignal Procssing 3 a log powr spctrum on a nonlinar Ml scal of frquncy [8]. Th Ml scal is inspird from th human auditory systm in which th frquncy bands ar not linarly spacd. Thus, th sound is rprsntd bttr. Th calculation of th MFCCs includs th following stps. i) Th discrt Fourir transform DFT) transforms th windowd spch sgmnt into th frquncy domain, and th short-trm powr spctrum P f )is obtaind. ii) Th spctrum P f ) is warpd along its frquncy axis f in hrtz) into th ml-frquncy axis as PM), whr M is th Ml frquncy using ) M f ) = 595 log + f 7. 6) iii) Th rsultd warpd powr spctrum is thn convolvd with th triangular band-pass filtr PM)into θm). Th convolution with th rlativly broad critical-band masking curvs θm) significantly rducs th spctral rsolution of θm) incomparisonwith th original P f ), which allows for th downsampling of θm). θm k ) = M PM M k )ψm), k =,..., K. 7) Thn, K outputs Xk) = lnθm k )), k =,..., K) arobtaind. In th implmntation, θm k ) is th avrag instad of th sum. iv)thmfccsarcomputdas K [ MFCCd) = X k cos d k.5) π K k= )], k =,..., D... Discrt Wavlt Transform. Th multirsolution analysis MRA) provids a tim-frquncy rprsntation wll suitd for nonstationary signals. MRA dcomposs and analyzs th signal at diffrnt frquncis and diffrnt rsolutions in th spac spannd by wavlts and scaling functions. Th continuous wavlt transform of a signal xt) isth projction of th signal on this spac as WTs, r) = s xt)ψ t r s 8) ), 9) whr ψt) isthmothrwavlts and r ar th scal and translation cofficints, rspctivly [9]. For computational issus, discrt wavlt transform DWT) is obtaind. Thr is a rich st of basis functions for DWT and it is possibl to gt a compact rprsntation of signal using this transform. In practical applications, DWT is applid in sampld signal x[n]; n =,..., N as DWT [ n, j] N = x[m]ψ j [m n], ) m= whr ψ j [n] = / j )ψn/ j ). xt) Low-pass filtr L Downsampling a r) L High-pass filtr H H w r) Figur : DWT with two composition lvls [5]. a r) w r) DWT is implmntd using a pyramidal algorithm rlatd to a multirat filtr-bank approach for multirsolution analysis. Mallat has shown that it is possibl to mak frquncy band dcomposition by using succssiv low-pass L) and high-pass H) filtrs in th tim domain as shown in Figur [5]. Aftr ach filtring stag i, th outputs ar downsampld by, and th outputs ar a i r) approximation and w i r) dtail or wavlt cofficints for low- and high-pass filtrs, rspctivly. Approximation cofficints giv local avrags of th signal. On th othr hand, dtail cofficints show th diffrncs btwn local avrags. In this work, Haar wavlt and Daubchis family, which is known as on of th bst familisforspchprocssingapplications,isusdasth mothr wavlt [9, ]..3. Discrt Wavlt Transform Basd Enrgy Faturs. In study of Didiot t al. [5], th nrgy-basd faturs which ar calculatd using wavlt transform hav bn proposd. According to study, th nrgy distribution in ach frquncy band is a vry rlvant acoustic cu and nrgy, calculatd from DWT, can b usd as a spch/music discrimination fatur. In our study, ths nrgy basd paramtrs hav also bn usd in ordr to mak comparison among diffrnt fatur xtraction mthods..3.. Instantanous Enrgy. This is a fatur which givs th nrgy distribution in ach band and givn as fj E = log N j w j r) ), ) N j whr w j r) is th wavlt cofficint at tim position r and frquncy band j and N is th lngth of th analysis window..3.. Tagr Enrgy. Tagr nrgy has bn rcntly applid for spch rcognition and givn as f TE j = log N j N j r= r= w j r) ) w j r ) w j r +) ). ) It is said that th discrt tagr nrgy oprator TEO) allows modulation nrgy tracking and givs a bttr rprsntation of th formant information in th fatur vctor compard to MFCCs in [5]. It is also pointd out that th

4 4 IRN ignal Procssing Tagr nrgy is a nois robust paramtr for spch rcognition, bcaus th ffctofadditiv nois is attnuatd..4. Complx Wavlt Transform. Convntional discrt wavlt transform suffrs from som fundamntal shortcomings dspit its compact rprsntation and fficint computational algorithm. Th first shortcoming of DWT is that a small shift of th signal in tim domain yilds distortion in th wavlt cofficint oscillation pattrn around singularitis. Th scond problm of DWT is th lack of dirctionality for two and highr dimnsion signals. Complx wavlt transform CWT) has bn proposd inspiring by th Fourir transform which dos not suffr from ths typs of problms. CWT is dfind with a complx-valud scaling function and complx-valud wavlt ψ c t) = ψ r t) + iψ i t), 3) whr ψ r t) andψ i t) ar ral and imaginary parts. If ths functions ar 9 out of phas with ach othr, that is thy form a Hilbrt transform pair, thn ψ c t) isananalytic signal and it has a on-sidd spctrum. Projcting th signal onto j ψ c t) j t n), th complx wavlt cofficints ar obtaind as d c j, n ) = dr j, n ) + jdi j, n ). 4) Complx Wavlt Transform can b prformd by two schms. In th first on, a complx wavlt ψ c t) that forms an orthonormal or biorthogonal basis is sarchd. Th scond mthod sks a rdundant rprsntation, and it sarchs ψ r t) andψ i t) that provid orthonormal and biorthogonal bass individually. Th rsulting CWT has x rdundancy in -D and has powr to ovrcom th shortcomings of DWT. In this study, th dual-tr approach for prforming complx wavlt transform which is a natural approach to scond, rdundant typ has bn prfrrd..4.. Dual-Tr Complx Wavlt Transform DT-CWT). Dual-tr complx wavlt transform was first introducd by Kingsbury in 998 []. Th dual tr implmnts an analytic wavlt transform by using two ral discrt wavlt transform with two filtr-bank trs; th first DWT givs th ral, and th scond on givs th imaginary part of th CWT. Analysis and synthsis filtr-banks can b illustratd as in Figur, whrh n) andh n) dnot th low-pass/highpass filtr pair for th uppr filtr-bank which implmnts WT for ral part. In th sam way, g n) andg n) dnot th low-pass/high-pass filtr pair for th lowr filtrbank for imaginary part. In this approach, th ky challng is joint dsign of two filtrbanks to gt complx wavlt and scaling function as clos as possibl to analytic []. Th filtrs usd for ral and imaginary parts of th transform must satisfy th prfct rconstruction condition givn as h n)h n +k) = δk), n 5) h n) = ) n h M n). xt) h n) h n) g n) g n) h n) h n) g n) g n) Figur : Analysis filtr bank for th dual tr CWT []. Twolow-passfiltrsofdualtrh n) andg n) satisfying a vry simpl proprty mak corrsponding wavlts to form an approximat Hilbrt Transform pair: on of thm must b approximatly a half-sampl shift of th othr [] g n) = h n.5) = ψ g t) = H { ψ h t) }. 6) inc h n) andg n) ar dfind only on intgrs, it will b usful to rwrit th half-sampl dlay condition in trms of magnitud and phas functions sparatly in frquncy domain to mak th statmnt rigorous G ) jw ) H = jw G ) jw = H jw.5w ) 7). Thr ar two popular mthods for dsign of filtrs for DT-CWT []. Q-hift olution. According to q-shift solution, g n) must b slctd as g n) = h N n), 8) whr N is th lngth of filtr h n) and is vn. In this cas, th magnitud condition is satisfid but not th phas conditionasshownin9)inthfrquncydomain G ) jw ) H = jw, G ) jw H jw.5w ) 9), Th quartr-shift q-shift) solution has an intrsting proprty that causs to tak its nam: whn you ask that g n) and h n) b rlatd as g n) = h N n) andalso that thy approximatly satisfy G jw ) = H jw.5w), thn it turns out that th frquncy rspons of h n) has approximatly linar phas. This is vrifid by writing g n) = h N n) in trms of Fourir transforms G ) jw = H ) jw jn )w, ) whr th rprsnts complx conjugation. This implis that th phass satisfy G jw ) = H jw ) N )w. )

5 IRN ignal Procssing 5 If th two filtrs satisfy th phas condition approximatly, it can b writtn that H jw ).5w = H jw ) N )w. ) and w hav th quation H jw ).5N )w +.5w. 3) As it can b sn, h n) is an approximatly linar-phas filtr. This mans that h n) is approximatly symmtric around th point n =.5N ).5. This is on quartr away from th natural point of symmtry, and solutions of this kind wr introducd as q-shift dual-tr filtrs for this rason []..4.. Common Factor olution CF). Anothr mthod for filtr dsign stag namd as commonfactorsolutioncf) can b usd to dsign both orthonormal and biorthogonal solutions for th dual tr CWT []. In this approach, th filtrs, h and g ar st as h n) = f n) dn), g n) = f n) dl n), 4) whr dn) is supportd on n L and rprsnts th discrt tim convolution. In trms of Z-transform, w hav H z) = Fz)Dz), ) G z) = Fz)z L D. z 5) For this solution, th magnitud part of half-sampl dlay condition is satisfid; howvr, th phas part is not xactly satisfid as in q-shift solution [] G jw ) = H jw ), G jw ) H jw ).5w. 6) o, th filtrs must b dsignd so that th phas condition is approximatly satisfid. 3. Exprimntal tudy and Rsults 3.. Datast and Prprocssing. Th two diffrnt data sts hav bn utilizd, and th faturs hav bn xtractd sparatly for ths two diffrnt datasts. In th first datast, TIMIT databas has bn usd for spch and svral CD rcordings with various musical gnrs hav bn usd for music databas. To obtain th scond datast, radio broadcasts wr rcordd containing music and spch. Th sampling frquncy was st as 44 Hz in vry stag of study. Howvr, sinc th data takn from TIMIT databas is sampld with 6 Hz, thy hav bn intrpolatd in th prprocssing stag in ordr to st sampling frquncy to 44 Hz. For th common faturs, th sgmntation has bn prformd for a fram of 496 sampls with 5 Tabl : Contnt of datasts. Ovrall databas Train st Tst st pch Music pch Music pch Music Datast Datast sampls ovrlapping which corrsponds to a fram lngth of 95 ms. Our xprimnts showd that th us of shortr window lngths limits th discriminativ charactristics of window. Both datasts usd contain sampls with lngth of.5 sc. Whil th first datast includs 49 music and 46 spch sampls, th scond datast contains 9 music and 64 spch sampls ntirly drivd from radio broadcasts in contrary to th first datast. In th rst of th contxt, th first and scond data sts will b namd as Datast and Datast, rspctivly. For th prformanc valuation, th data sts hav bn dividd into two groups as training and tst sts. A dtaild rprsntation for datast and datast is givn in Tabl. Th four diffrnt fatur xtraction mthods hav bn mployd in this study. Th first mthod has a paramtr vctor which contains tim-frquncy-basd faturs and Ml cpstrum cofficints with lngth of. Th scond and third mthods us DWT-basd faturs. Th scond mthod contains DWT-basd nrgy paramtrs Tagr and instantanous nrgy as dscribd in ction.3.thlngth of fatur vctor for third mthod is. Thnovlmthodsproposdinthisstudyarthlast two fatur xtraction schms. In th third mthod, - lvl DWT dcomposition has bn prformd to covr th analysis frquncy rang in dtail using svral typs of mothr wavlts of Daubhcis family. Th lngth of fatur vctor constructd from th statistical masurs of th cofficints, and ratios btwn th adjacnt subbands is 38. Usag of ratio paramtrs and th slction of th siz of th analysis window ar th important aspct which improvs th prformanc of th classification in this study. Th last mthod is basd on complx wavlt transform CWT), and two diffrnt filtr dsign stratgis including commonfactorsolutionandq shift solution hav bn usd at fatur xtraction stag. CWT has bn prformd for 5- lvl and 7-lvl to avoid furthr incras in th lngth of th fatur vctor which rsults in fatur vctors with lngth of 5and35for5and7bands,rspctivly. Bfor classification stag, th faturs that ar highly corrlatd with th othr faturs hav bn liminatd using principal componnt analysis PCA) to rduc th lngth of fatur vctors. Th principal componnts that contribut lss than.5% to th total variation in th data st hav bn liminatd. Tabl shows th lngth of th fatur vctors bfor and aftr PCA. It has bn obsrvd from Tabl that th DWT-basd nrgy faturs includ discriminativ information. imilarly, approximatly half of prliminary fatur vctors ar corrlatd with ach othr spcially for CWT-basd faturs.

6 6 IRN ignal Procssing Tabl : Lngths of th fatur vctors bfor and aftr PCA. Original lngth Aftr PCA Convntional Common faturs DWT basd Enrgy DWT basd CWT basd DWT Enrgy db8) 5 DWT Enrgy coif) 3 Haar 38 9 db 38 9 db8 38 db5 38 db 38 CF 5 Lvls ) 5 Q hift 5 Lvls ) 5 CF 7 Lvls ) 35 5 Q hift 7 Lvls ) 35 4 Th fdforward artificial nural ntworks with th scald conjugat gradint CG) backpropagation algorithm in MATLAB s nural ntworks toolbox which blongs to th class of th conjugat gradint algorithms hav bn usd for classification. CG algorithm uss stp siz scaling instad of lin-sarch pr larning itration, and this maks it fastr than othr scond-ordr algorithms []. This algorithm prforms wll for ntworks with a larg numbr of wights,whritisasfastasthlvnbrg-marquardtand rsilint backpropagation algorithms; its prformanc dos not dgrad quickly. Also, th conjugat gradint algorithms hav rlativly modst mmory rquirmnts. Th numbr of hiddn nurons has bn prfrrd as 4, and th targt man squar rror has bn dfind as., as a rsult of xtnsiv simulations. All cods and programs in this study hav bn writtn in MATLAB. Th cods for common faturs, DWT-basd statistical and nrgy faturs hav bn writtn by th authors. For DWT-basd analysis, wavlt toolbox of MATLAB has bn usd. For CWT-basd analysis, th cods ar takn from th study of lsnick [] for common factor solution-basd filtr dsign and th programs writtn by two studnts undr suprvision of I. lsnick hav bn usd for Q-shift filtr basd analysis [3]. In th following sction, th gnral classification rsults will b givn for fatur vctors. Th prformanc has bn givn as th accuracy of th classification which can b formulatd as TP+TN Accuracy = TP+FP+TN+FN, 7) whr TP, TN, FP,andFN rprsnt th numbr of spch sampls labld as spch, th numbr of music sampls labld as music, th numbr of music sampls labld as spch and th numbr of spch sampls labld as music, rspctivly. 3.. Gnral Classification Prformanc. Th rsults of th xprimnts ar summarizd as gnral prformanc in Tabl 3. Tabl 3: Gnral classification rsults. Prformanc %) Datast Datast Convntional Common faturs DWT basd Enrgy DWT basd CWT basd DWT nrgy db8) DWT nrgy coif) Haar db db db db CF 5 Lvls ) Q shift 5 Lvls ) CF 7 Lvls ) Q shift 7 Lvls ) Whn Tabl 3 is takn into considration, it can b sn that wavlt-basd paramtrs hav highr classification rsults than traditional mthods. In gnral, all mthods ar succssful in th classification of sampls in Datast, which indicats that th TIMIT spch data and CD rcordings ar sparabl. Howvr, it is not possibl to say sam thing for Datast, sinc th sampls in Datast rflcts a mor ralistic cas, whr sampls ar rcordd from radio broadcast. Th bst prformanc has bn obtaind with db8 wavlt. Th complx wavlt-basd faturs prform bttr than convntional mthods and wavlts with fwr vanishing momnts. Howvr, thy ar not as succssful as th db8. Th similarity of th mothr wavlt with th analyzd wavforms is an important critrion for th wavlt analysis which may b th caus of this prformanc diffrnc. Thrfor, th accuracy for diffrnt databass may diffrdrastically. Th fatur xtraction mthods ar considrd in trms of thir calculation tims, and th avrag computation tims for fatur-xtraction stag for all mthods usd in this study ar givn in Tabl 4. According to Tabl 4, a sorting among th fatur xtraction mthods can b mad as t C >t DWT >t CWT >t DWTE, 8) whr t C, t DWT, t CWT,andt DWTE show th computation tim for th mthods basd on convntional, DWT, CWT, and DWT-basd nrgy faturs. As xpctd, th convntional mthods tak mor tim, sinc thy includ tim domain calculations. Although nrgy-basd faturs ar th fastst, thy hav poor classification prformanc. Th calculation tims for DWT-basd statistical fatur xtraction show diffrncs according to th usd wavlt in th analysis. Wavlt familis including mor vanishing momnts such as db5 and db spnd mor tim for computation comparing to othr wavlt familis, sinc thy hav longr filtrs. It is ncountrd that th db8 familis as th optimum wavlt for DWT-basd analysis sinc it shows highst prformanc in classification of spch and music and it has accptabl calculation tim.

7 IRN ignal Procssing 7 Tabl 4: Avrag computation tims for usd fatur xtraction mthods. pch msn.) Music msn.) Convntional Common faturs Daubchis8-basd DWT basd.6.7 nrgy faturs Enrgy Coiflt-basd nrgy faturs DWT basd CWT basd Haar Daubchis.4.4 Daubchis Daubchis Daubchis Q-shift-basd CWT faturs CF-basd CWT faturs.3.3 CWT-basd mthod is fastr than DWT-basd analysis, and it shows prformanc rsults clos to DWT. In this prspctiv, CWT-basd faturs can b usd for onlin implmntation as wll. It should b notd that th silnt parts of sampls could b dtrmind quickr than th fatur-xtraction mthods during onlin implmntation, sinc only an nrgy thrshold valu is considrd to mak a dcision if th sgmnt is silnc or not Graphical Usr Intrfac GUI) Dsign for pch/music Discrimination. A graphical usr intrfac has bn dsignd as wll in ordr to prform spch music discrimination visually. An onlin lablling modul has bn also mbddd to th intrfac and obsrvation of prformanc for ral-tim classification has bcom possibl with this tool Main Modul. Main modul can b usd to s classification rsults obtaind by mthods. In Figur 3, thgui dsignd for main modul is shown. Using load fil button, th fil to b analyzd is slctd, and th play button plots and plays th signal at th sam tim. inc tim-/frquncybasd faturs ar also usd at th classification stag, it is important to s th gnral structur of spctrogram. For this aim, thr is a button namd as spctrogram of signal in th modul to plot tim/frquncy proprtis. In th DWT-basd faturs part of modul, -lvl dcomposition is prformd using slctd wavlt from pop-up mnu. It is also possibl to s shap of wavlt and wavlt cofficints using show wavlt and plot wavlt cofficints buttons, rspctivly. For CWT-basd fatur, thr is also a popup mnu which you can slct th filtr dsign mthod for analysis. It can b sn th xisting complx wavlt with its ral and imagind parts using plot filtr Cofficints button in CWT-basd fatur xtraction part of th modul. For tim/frquncy basd fatur xtraction, thr is also a button and th valus of paramtrs such as spctral cntroid, spctral rolloff, spctral flux, th numbr of zro crossings, and low nrgy ratio of loadd fil xist in th blanks whn this button is pushd. It is possibl to gt th classification rsults of four-fatur xtraction mthods simultanously using classification rsults button. If onlin lablling modul button is pushd, th onlin lablling modul will appar in a nw window Ral-Tim pch/music Discrimination Modul. This modul has bn dsignd to obsrv spch/music classification prformanc for onlin implmntations. In th givn graphical usr intrfac in Figur 4, a sampl fil is ftchd using opn button and th onlin lablling procss is startd using start button. paus button maks it possibl to stop th procss tmporarily, and using continu button th labling procss can b continud from whr it is stoppd. Th stop button intrrupts th program and nds th labl assignmnt procss. In th modul, th rd lttrs undr th signal graph shows th prassignd labls for data and, M, and ar usd to indicat spch, music, and silnc parts of data, rspctivly. Onlin classification rsults ar shown with blu color undr prassignd labls, as it can b sn from Figur 4. Ths labls ar assignd for sgmnts which hav th lngth of.5 sc. In onlin labl assigning, th faturs ar xtractd using -lvl DWT with db8 wavlt for ach sampl, sinc it has givn th most accurat rsults in xprimnts and a prviously traind artificial nural ntwork is usd to dtrmin th labls. 4. Conclusions and Futur Work Thr has bn a growing intrst in spch/music discrimination systms which is usually usd in svral applications as a prprocssing stag of svral audio systms. Howvr, still improvmnts for MD systms ar rquird to dvlop ffctiv ral-tim systms. Thrfor, in this study, two fatur-xtraction schms basd on discrt and dual-tr wavlt transforms hav bn proposd which ar suitabl for ral-tim applications. Aftr comparing th proposd faturs with th convntional faturs and rcntly proposd wavlt-basd nrgy faturs, an onlin labling modul has bn implmntd with th Daubhcis8 wavlt. Rgarding th xprimnts and rsults givn in th prvious sction, it has bn obsrvd that convntional faturs ar not vry ffctiv in discrimination of spch/music sampls whn thy ar usd alon. Howvr, if thy ar usd togthr, th accuracy tnds to slightly incras. Th wavlt transform mthods xcpt th nrgy-basd ons hav shown highr prformanc for Databas than th rsults for Databas, bcaus th scond databas consists of th rcordings from radio broadcasts which rflcts a mor ralistic cas. Th slction of th analysis window lngth which spcifis th contnt of th nonstationary signal and th spd of implmntation is an important choic for MD. Th slction of a short window ordr of millisconds as in litratur will not giv th ncssary information on timvarying frquncy contnt, sinc th signal can b assumd

8 IRN ignal Procssing th WHRO voicb ayan rkkk alitli5.wav t ψh t) ψh t) + ψg t) ψg t) Complx ψt) Tim Frquncy Hz) 3 t Figur 3: Graphical usr intrfac for main modul I I Figur 4: Graphical usr intrfac for onlin lablling modul.

9 IRN ignal Procssing 9 as stationary in this intrval. On th othr hand, th usag of long window ordr of sconds, which is rportd as succssful, limits th onlin application of th algorithms. Th proposd algorithm is computationally fficint avrag running tim for th proposd mthod is <5 msn), and this allows th us of mthod for ral-tim implmntation. Th classification prformanc of DWT-basd fatur vctor varis dpnding on th mothr wavlt usd in th fatur-xtraction stag. Whn th numbr of vanishing momnts is incrasd, th wavlt bcoms smoothr. Ths smooth wavlts produc larg cofficints for slowly changing signals lik music, whil it producs rlativly small cofficints for spch signals. This can b usd as a discriminativ proprty for MD. Th Haar and db wavlts hav a fw vanishing momnt this may prvnt th good rprsntation of signal in frquncy domain. On th contrary, db5 and db hav much mor filtr cofficints and vanishing momnts, but this incrass th complxity in th fatur spac and also rquirs longr computations. In this way, db8 has mrgd as th most idal wavlt typ. Among th CWT-and DWT-basd faturs, DWT fatur xtractd with Daubchis8 wavlt has dmonstratd th highst classification prformanc, whil th CWT-basd classification has shown rsults as 99.93% for Databas and 98.3% for Databas. Whn all faturs ar concrnd, w s that Daubchis8-basd paramtrs hav suprior discrimination faturs in trms of classification of spch and music. In th study, th contribution of th ratio paramtrs to th discrimination prformanc hav also bn xamind for DWT- and CWT-basd faturs. It has bn obsrvd that ratio paramtrs provid a contribution about.5% to th ovrall prformanc for DWT-basd paramtrs. Howvr, th rsults wr inconclusiv for CWT. In th classification stag, artificial nural ntworks hav bn usd as classification tool. Th numbr of hiddn nurons has bn prfrrd as 4, and th targt man squar rror has bn dfind as., huristically. Conjugat gradint algorithms hav bn slctd as larning algorithm, sinc thy hav advantags according to othr mthods. Also, principal componnt analysis has bn prformd bfor th classification stag to rprsnt signals mor fficintly and to dcras th dimnsion of fatur vctor. Th obsrvd MD prformanc of CWT-basd faturs was lss than th DWT-basd ons. To obtain a bttr prformanc, a fatur st which rflcts th most powrful proprtis of CWT must b constructd. Th filtr structur usd in CWT-basd paramtrization has th possibility of prsnc of unsuitabl charactristics in trms of spch/ music discrimination, and as a rsult, th accuracy is obsrvd as lowr than prformanc of DWT-basd faturs. In this mannr, adaptiv filtr dsign is rquird to gt mor succssful rsults. Th datast can b xpandd to includ mixd spch-music sampls. In this way, a multiclass classification can b prformd instad of binary classification for futur studis. Th proposd fatur xtraction is also suitabl for a hardwar implmntation using digital signal procssors to hav a fastr MD systm. Rfrncs [] O. M. Mubarak, E. Ambikairajah, and J. Epps, Novl faturs for ffctiv spch and music discrimination, in Procdings of th IEEE Intrnational Confrnc on Enginring of Intllignt ystms ICEI 6), ayfa, Islamabad, Pakistan, April 6. [] K. El-Malh, M. Klin, G. Ptrucci, and P. Kabal, pch/ music discrimination for multimdia applications, in Procdings of th IEEE Intrntional Confrnc on Acoustics, pch, and ignal Procssing, pp , Jun. [3]A.C.GdikandB.Bozkurt, Pitch-frquncyhistogrambasd music information rtrival for Turkish music, ignal Procssing, vol. 9, no. 4, pp ,. [4] J. aundrs, Ral-tim discrimination of broadcast spch/ music, in Procdings of th IEEE Intrnational Confrnc on Acoustics, pch, and ignal Procssing ICA 96), pp , May 996. [5] E. chirr and M. lany, Construction and valuation of a robust multifatur spch/music discriminator, in Procdings of th IEEE Intrnational Confrnc on Acoustics, pch, and ignal Procssing ICA 97), pp , April 997. [6] E. M. aad, M. I. El-Adawy, M. E. Abu-El-Wafa, and A. A. Wahba, A multifatur spch/music discrimination systm, in Procdings of th 9th National Radio cinc Confrnc NRC ), pp. 8 3,. [7] J. Ajmra, I. McCowan, and H. Bourlard, pch/music sgmntation using ntropy and dynamism faturs in a HMM classification framwork, pch Communication, vol. 4, no. 3, pp , 3. [8] C. Panagiotakis and G. Tziritas, A spch/music discriminator basd on RM and zro-crossings, IEEE Transactions on Multimdia, vol. 7, no., pp , 5. [9]J.Wang,Q.Wu,H.Dng,andQ.Yan, Ral-timspch/ music classification with a hirarchical obliqu dcision tr, in Procdings of th IEEE Intrnational Confrnc on Acoustics, pch and ignal Procssing ICA 8), pp , April 8. [] A. Pikrakis, T. Giannakopoulos, and. Thodoridis, A spch/music discriminator of radio rcordings basd on dynamic programming and Baysian ntworks, IEEE Transactions on Multimdia, vol., no. 5, Articl ID 45496, pp , 8. [] M. Kos, M. Grašič, and Z. Kačič, Onlin spch/music sgmntation basd on th varianc man of filtr bank nrgy, EURAIP Journal on Advancs in ignal Procssing, vol. 9, Articl ID 6857, pp. 3, 9. [] G. E. G. Tzantakis and P. Cook, Audio analysis using th discrt wavlt transform, in Mathmatics and imulation with Biological, Economical and Musicoacoustical Applications, pp , WE,. [3] M. K.. Khan and W. G. Al-Khatib, Machin-larning basd classification of spch and music, ACM Journal on Multimdia ystms, vol., no., pp , 6. [4]. Ntalampiras and N. Fakotakis, pch /music discrimination basd on discrt wavlt transform, in Procdings of th 5th Hllnic Confrnc on Artificial Intllignc ETN 8), vol. 538 of Lctur Nots in Artificial Intllignc, pp. 5, yros, Grc, Octobr 8. [5]E.Didiot,I.Illina,D.Fohr,andO.Mlla, Awavlt-basd paramtrization for spch/music discrimination, Computr pch and Languag, vol. 4, no., pp ,. [6] N. G. Kingsbury, Th dual-tr complx wavlt transform: a nw tchniqu for shift invarianc and dirctional filtrs,

10 IRN ignal Procssing in Procdings of th IEEE Digital ignal Procssing Workshop, 998. [7] I. W. lsnick, R. G. Baraniuk, and N. G. Kingsbury, Th dual-tr complx wavlt transform, IEEE ignal Procssing Magazin, vol., no. 6, pp. 3 5, 5. [8] F. Zhng, G. Zhang, and Z. ong, Comparison of diffrnt implmntations of MFCC, Journal of Computr cinc and Tchnology, vol. 6, no. 6, pp ,. [9]. Mallat, A Wavlt Tour of ignal Procssing, Acadmic Prss, 999. [] N. Kingsbury, Th dual-tr complx wavlt transform: a nw tchniqu for shift invarianc and dirctional filtrs, in Procdings of th IEEE Digital ignal Procssing Workshop,pp. 4, 998. [] I. W. lsnick, Th dsign of Hilbrt transform pairs of wavlt bass, IEEE ignal Procssing Lttrs, vol.8,no.6,pp. 7 73,. [] C. Charalambous, Conjugat gradint algorithm for fficint training of artificial nural ntworks, IEEE Procdings-G on Circuit Dvics and ystm, vol. 39, no. 3, pp. 3 3, 99. [3]. Cai and K. Li, Matlab Implmntation of Wavlt Transforms,.

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