A New Hybrid Audio Classification Algorithm Based on SVM Weight Factor and Euclidean Distance

Transcription

1 Proceedgs of the 2007 WSEAS Iteratoal Coferece o Computer Egeerg ad Applcatos, Gold Coast, Australa, Jauary 7-9, A New Hybrd Audo Classfcato Algorthm Based o SVM Weght Factor ad Eucldea Dstace YUK YING CHUNG, *ERIC H.C.CHOI, LIWEI LIU, MOHD AFIZI MOHD SHUKRAN *DAVID YU SHI, *FANG CHEN School of Iformato Techologes, Uversty of Sydey, NSW 2006, AUSTRALIA * ATP Research Laboratory, Natoal ICT for Australa, NSW 430, AUSTRALIA Abstract: - The text-based classfcato domates the covetoal audo classfcato systems, whch tedous maual wor s used to otate the ame, class, or sample rate. However, o most occasos, ths method s ot satsfyg due to ts opaque to real cotet. I order to retreve the audo fles effectvely ad effcetly, cotet-based audo classfcato becomes more ad more ecessary. I ths paper, a ew hybrd approach of audo classfcato algorthm s proposed to mprove the performace of some ms-classfed audo data. The proposed method has bee compared wth the tradtoal Eucldea-based K-Nearest Neghbor classfer. As to mprove the accuracy for specfc problems, a weght factor based o Supportg Vector wll apply to the Eucldea dstace ad K-NN rule to acheve better accuracy. The proposed ew weghted Eucldea algorthm has bee proved to be more sestve to the classfcato crtera. The expermetal results show that t ca mprove the audo classfcato accuracy by 28% at the maxmum ad 7% the overall performace. By usg the ew proposed algorthm, some ms-classfed audo data from a covetoal Eucldea dstace classfer ca be classfed. Key-Words: - audo classfcato, K-Nearest Neghbor, SVM, Eucldea dstace Itroducto Due to the rapdly-creasg amout of audo data archves, t has become dffcult to fd out the partcular audo fles by usg query. The audo classfcato becomes a very crtcal ssue audo retreval. The covetoal text-based audo classfcato s ot oly tme-cosumg, but also subjectve. It seems to be helpless may occasos. I order to classfy the audo fles effectvely ad effcetly, cotet-based audo classfcato s requred. I ths paper we propose the ew hybrd audo classfcato algorthm based o SVM weght factor order to mprove the accuracy of audo classfcato. As the demads ad usage for audo classfcato ad retreval crease, the classfcato methods eed to be mproved to be more automatc ad effectve. The tradtoal text-based audo classfcato fals to recogze the uderlyg cotet of audo fles. I ths paper we propose a ew hybrd audo classfcato algorthm based o SVM weght factor ad Eucldea dstace order to mprove the accuracy for audo classfcato. The proposed algorthm ca extract the weght factor from Supportg Vector classfcato Model (SVM) ad apply to the Eucldea measuremet. I ths paper, two feature extracto algorthms amely, Orgal MFCCs (mel-scaled frequecy cepstral coeffcets) ad Average MFCCs have bee mplemeted. For Orgal MFCCs, the DTW (Dyamc Tmg Warpg) dstace s used to measure the smlarty of two audo fles. I Average MFCCs, the Eucldea dstace s drectly calculated o two feature sets. Furthermore, the SVM (Supportg Vector Mache) classfer s mplemeted. Three classfcato algorthms: DTW dstace, NN ad K-NN have bee mplemeted ad compared wth SVM classfer. The weght factor from SVM s chose because of ts outstadg ablty to mmze structural rss whle the other algorthms are based o eumerato. By combg the SVM weght factor ad DTW dstace we ca cosder the classfcato to be more aware of classfcato boudary cases. The mprovemet of usg the SVM weght factor Eucldea dstace audo data classfcato has bee show secto 5. Secto 2 s the troducto to the audo classfcato system. Secto 3 descrbes the feature extracto for the audo data. Secto 4 explas the audo classfcato algorthms ad the proposed ew weght-knn classfcato algorthm. Sectos 5 ad 6 preset the test results ad cocluso, respectvely.

2 Proceedgs of the 2007 WSEAS Iteratoal Coferece o Computer Egeerg ad Applcatos, Gold Coast, Australa, Jauary 7-9, Itroducto to Audo Classfcato Wth the crease the amout of audo data, audo classfcato techology s hgh demad order to provde effectve ad effcet methods. So far, audo classfcato has expereced two dramatc stages. At the begg, the text-based audo classfcato domated. The mechasm s smlar wth may popular search eges such as Google, Yahoo ad Badu. The searchg result s located by usg query eywords. The maual wor s requred to otate attrbutes descrbg audo fles. For textbased techology, t ca acheve a satsfactory performace, provded that the sog s searched by ts ame. Otherwse, t wll be very hard to fd out the smlar audo fles based o specfc audo features. Due to such subjectve characterstcs, there has bee a demad for cotet-based audo classfcato techology to solve these problems. 2. Text-based Audo Classfcato Tradtoal text-based audo classfcato uses the maual effort to deote the descrptve words of the audo fles the database. The user wll perform the query based o these attrbutes. As t s dffcult for the maual classfcato to cover all of the requred features that users eed, the text-based audo classfcato s proved to be subjectve ad ot userfredly. Meawhle, t requres eormous wor to group the smlar audo fles whch s tmecosumg. Aother problem s that the users usually use the query words from ther ow uderstadg. It s very dffcult to match exactly the descrptve attrbutes the database. Fally, the searchg wll fal due to the poor classfcato scheme. 2.2 Cotet-based Audo Classfcato I the doma of cotet-based audo classfcato, the problem s coverted to the computato o the feature data set. The two steps, amely feature extracto ad classfcato method, are volved. Feature extracto refers to trasformg the orgal audo data to the feature vector whch the specfc desred characterstcs are cotaed. It s the frst step audo processg ad has prove to have a large correlato wth the fal accuracy. I the ext step, classfcato methods wll perform the automatc classfcato by aalyzg the derved feature vectors. The classfers vary from each other due to ther dfferet schemes. Geerally, the sample model s eeded to serve the classfcato Feature Extracto The audo features are geerally dvded to two categores: tme doma ad frequecy doma. I tme doma, the audo represetato s expressed as the basc audo ampltude chage wth tme. Some features ca be derved from the statstcs of ampltude, such as slece rate (SR) ad loudess. I the frequecy doma, features are obtaed by applyg Fourer trasform. Such features clude ptch, badwdth, brghtess, harmoy etc. I ths paper, the MFCCs feature s adopted The Proposed Cotet-Based Audo Classfcato (CBAC) The proposed Cotet-Based Audo Classfcato (CBAC) cludes three steps: () feature extracto, (2) classfcato, ad (3) retreval. Feature extracto refers to trasformg the orgal audo data to the feature vector whch the specfc desred characterstcs are cotaed. It s the frst step audo processg, ad t has a large correlato wth the fal accuracy. The classfcato ad retreval fucto together by applyg the effectve formulato of a dstace measure ad the classfcato rules. The audo features ca be dvded to two categores: tme doma ad frequecy doma. I tme doma, the audo represetato s expressed as the basc audo ampltude chage wth tme. Some features ca be derved from the statstcs of ampltude, such as slece rate (SR) ad volume. I the frequecy doma, features are obtaed by applyg the Fourer trasform. Such features cludes ptch, badwdth, brghtess ad harmoy. Fg. shows the bloc dagram of the proposed CBAC. I the proposed CBAC, Mel-Frequecy Cepstral Coeffcets (MFCCs) [][2] were used as the feature extracto algorthm for audo sgal. As the legth of the put query audo data ad the stored audo sequece data are usually dfferet, Dyamc Tme Warpg (DTW) [3] algorthm was used to geerate the same legth of feature vectors. The Nearest Neghbor (NN) [4] rule was used for the classfcato ad retreval of audo data. Sectos 3., 3.2 ad 4.3 expla more detal the MFCCs, audo classfcato ad DTW, respectvely. Fg. The bloc dagram of the proposed cotet based audo classfcato system

3 Proceedgs of the 2007 WSEAS Iteratoal Coferece o Computer Egeerg ad Applcatos, Gold Coast, Australa, Jauary 7-9, Feature Extracto 3. Orgal Mel-Frequecy Cepstral Coeffcets (MFCCs) MFCCs [][2] are wdely used speech recogto systems. It adapts to huma acoustc characterstcs. Fg. 2 shows the bloc dagram of orgal MFCC feature extracto. The MFCCs trasform was coducted the followg four steps:. The audo was hammg-wdowed overlappg steps; 2. The dscrete Fourer trasform (DFT) was computed to trasform the audo data to frequecy doma; 3. The spectral coeffcets were perceptually weghted by a o-lear map of the frequecy scale (Mel-frequecy scale). Tragle flters were used here; ad 4. Aother DFT was used to trasform the coeffcets to cepstral coeffcets. for the put vectors wth same legth, aother ormalzed MFCCs array s troduced ths way: to partto oe musc clp to three parts tme doma. Suppose that there are hammg wdows for oe musc clp. The oe parttog part should have /3 wdows. If there are MFCCs coeffcets oe wdow, we calculate the mea of correspodg coeffcets of each partto so that each partto produces MFCCs coeffcets represetg the /3 wdows. The vectors from three parttos are coected to form a ew vector, whch s used for classfcato. Ths vector should cota 3* coeffcets. Fg. 3 the dagram shows how to extract the average MFCCs feature vectors. C = 24 j= log( Y j )cos[ ( j ) 2 π ] 24 Wdow Wdow /3 Wdow /3+ Wdow 2/3 Wdow Wdow 2/3+ where =,2,...,P P s the dmeso of coeffcet,usual choce P=2. { C } =,2,..., 2 are derved MFCC coeffcets. The trasform formula for the Mel-frequecy scale ad lear frequecy scale s as follows. Audo Seres Fltered Audo Seres Frequecy Doma Mel-frequecy Doma Bad-pass Fltered Seres f mel = l DFT IDFT Hammg wdow mel-frequecy scale trasform tragle flter l 000 ( / 700 ) Fg.2 Cepstral Coeffcet Orgal MFCC feature extracto 3.2 Average MFCCs Feature Aalyzg the MFCCs derved from hudreds of wdows s too log,whle the SVM requremets Fg.3 Average + MFCC 2 feature 2+ vectors 3 4 Audo Classfcato The classfcato rules [][2] are eeded for the classfcato of the audo data. The most commolyused algorthms are Nearest Neghbor (NN) rule, K- NN rule, HMM rule, Neural Networ classfer, NFL rule ad SVM rule. I ths paper two dstace measuremets Eucldea dstace ad Dyamc Tme Warp (DTW) dstace were mplemeted. The Eucldea dstace s used for the vectors wth the same legth whle the DTW algorthm s used to solve the seres wth dfferet legths or dfferet phases. 4. Eucldea Dstace The Eucldea dstace s a effectve way to measure the smlarty betwee two arrays wth the same dmeso ad phase [5]. For ay dmeso of, t s defed as: D= = 2 ( x y ) = I the audo classfcato, we use Eucldea dstace to measure the dstace betwee two MFCCs feature vectors from two dfferet audo fles. Because each hammg wdow geerates a feature vector wth the same legth, the Eucldea

4 Proceedgs of the 2007 WSEAS Iteratoal Coferece o Computer Egeerg ad Applcatos, Gold Coast, Australa, Jauary 7-9, dstace ca compute the dfferece betwee two wdows effectvely. The Eucldea dstace has bee mplemeted two classfcato methods. For the orgal MFCCs feature, the Eucldea dstace s used to calculate the dstace betwee feature vectors of two wdows, whch s a compoet DTW computato. For the Average MFCCs feature, the Eucldea dstace s to calculate the dstace betwee the average feature vectors of two dfferet audo fles, ad the result s drectly used NN ad K-NN rules. g(c) = m g( -, j) + d(, j) g( -, j -) + 2d(, j) g(, j -) + d(, j) Fg.5 DTW dstace betwee two vectors I ths paper the DTW algorthm was used to measure the dstace betwee the MFCCs from dfferet soud clps. Fg.4 Eucldea dstace betwee two vectors 4.2 Nearest Neghbor (NN) The Nearest Neghbor (NN)[0] rule s used to classfy the observato to the category that oly depeds o a collecto of correctly classfed samples. It ca be defed the followg: X (X,X 2, X ) If m d(x, X)= d(x, X) (where =,2 ) The NN rule chooses to classfy the X to the category θ, where the X s the earest eghbor of X ad X belogs to θ. 4.3 Dyamc Tme Warpg (DTW) Whe the feature vectors are geerated, oe problem arses: the legths of the put ad the stored sequeces are ulely to be the same. The Dyamc Tme Warpg (DTW) [6] algorthm ca be used to solve ths problem. DTW algorthm s defed as follows: () Let g(,) = 2d(,), j =. (2) Let = max(, j-r), 2 = m(j + r, I), Compute g(, j), (=,, 2 ). (3) j < J? YES (2) NO Compute D(T, R) = g(i, J) / (I+J) The geeralzed algorthm s as follows: 4.4 SVM Classfer The SVM theory s based o bary classfcato. The core cocept of SVM algorthm s to derve the hyperplae to separate the two classes. The oe that maxmzes the marg of hyperplaes betwee two classes s called optmal hyperplae. To solve the mult-dmeso problem, the Kerel fucto serves to map the put vectors to a hgh-dmesoal feature space whch the mapped data s learly separable [6].The SVM method provdes a way to mmze the structural rs. The mult-class classfcato also ca be acheved by SVM by combg the bary classfcatos. 4.5 The Proposed Weght-KNN Classfcato I ths paper we propose a ew hybrd musc classfcato algorthm based o the SVM ad Eucldea dstace. I the Eucldea dstace, each dmeso s calculated equally, whle for some classfcato crtera, some dmesos may ot be as mportat as other dmesos. The purpose of ths method s to extract the weght factor from the trag SVM model. Due to the outstadg property of mmzed structural rss the Supportg Vector Classfer, the model ca mply the bas for each dmeso uder the classfcato crtera. To mplemet the weght factor Eucldea dstace calculato, the Eucldea dstace wll be more sesble to the classfcato crtera. It ca become more flexble to solve

5 Proceedgs of the 2007 WSEAS Iteratoal Coferece o Computer Egeerg ad Applcatos, Gold Coast, Australa, Jauary 7-9, dfferet classfcato by offerg dfferet results. I order to aalyze the problem the same feature space, we choose to use lear decso fucto to derve the weght [8]. The explaato of the Weght-KNN s as followg: The weght vector s w l = ( w w ) = y α x =,..., the decso fucto f ( x) ( w x + b) = sg of the SVM [8].It ca dcate the correlato level of each dmeso regardg to the specfc classfcato crtera. The mproved Eucldea dstace ca be derved as d weght = l 2 l l ( w ) ( x x ) j l= The effect of Weght-KNN ca be proved as followg: I the decso fucto, 2 g ( x) = w x + b, w = ( w, w,..., w ), s the dmeso of feature vector x.to ufold the decso fucto, we derve l l f ( x)= sg {(... + w x w x ) + b}.for ay, x s costat whch refers to the put vector.we 2 suppose that for w = ( w, w,..., w ), w s costat whe.we chage w to observe the effects. Cosderg the sg of x ad w, the w x = w x or w x = w x.suppose costat + + ϕ 0 = w x w x + w x w x + b The orgal decso fucto s coverted to f ( x)= sg{ w x + ϕ 0 }. It s clear that whe x s uchaged, the larger the w s, the more the devato s fromϕ 0, whch dcates the fluece o the fal objectve value. Lewse, for ay gve, the larger the w s, the more t wll elarge the fluece of x. Ths classfcato method s used our tests to classfy the two classes that are dffcult to dscrmate each other wth the covetoal Eucldea-based KNN method. By applyg the SVM-based weght, the Eucldea dstace ca be more sestve to the classfcato boudary, whch, 2. the expermets, s the boudary of dfferet strumets. The fucto of the weght factor as appled to the Eucldea dstace. The proposed ew algorthm taes the advatages of Eucldea dstace, NN rule, ad the SVM. As the SVM s outstadg for ts mmzed structural rss, the hyperplae ca dcate the dstrbuto of each dmeso. For Eucldea dstace ad NN rule, they are methods based o eumerato whch serve well retreval. By combg these two algorthms together, the eumerato method ca be aware of the boudary ad crtera of the practcal classfcato. Therefore t ca acheve better results both classfcato ad retreval. 5 Testg Results We have tested ad valdated the postve effect brought by SVM-based weght factor. I ths test, the saxophoe ad gutar are ms-classfed the classfcato of some sets. These two classes of audo data are tae out from the sample set ad perform the test. We use the Average MFCCs (melscaled frequecy cepstral coeffcets feature) feature vectors, ad Eucldea dstace wth K-NN s carred out to do bary classfcato. The effects of addg the SVM-based weght wll be observed. I ths test, 28 saxophoe clps ad 28 gutar clps are used. The audo data fles are dvded to 4 parts, wth each part of 7 clps for gutar ad 7 clps for saxophoe. For each tme, oe part s tae as the testg set whle the others are used to tra the model. To valdate the weght effects for the geeral gutar ad saxophoe dscrmato, we apply the same weght the four sets of tests, whch s from the trag model of set. The test has chose MFCC2 as the bass to derve the Average MFCCs feature. The hammg wdow shfts every 256 samples, ad there are 52 samples each hammg wdow. The SVM model s traed by a lear classfer, wth C=2. Fg.9 shows how we test the effect of SVM weght factor added to the Eucldea dstace measuremet.

6 Proceedgs of the 2007 WSEAS Iteratoal Coferece o Computer Egeerg ad Applcatos, Gold Coast, Australa, Jauary 7-9, d = l l 2 ( x x ) j l= d weght = l 2 l l 2 ( w ) ( x x ) j l= 2 ( w, w w ) w =,..., Fg.9 Testg o the effect of weght factor Accordg to the testg results, we fd that that the Weght-KNN ca mprove the classfcato result dscrmatg the Gutar musc from the Saxophoe musc. I each set of testg, those classes wth bad performace whe o weght added have show the creased accuracy the Weght-KNN testg. The treds are worg for all sets of testg, by creasg the accuracy 28% Saxophoe recogto. The classfcato performace s mprovg whe we apply the SVM Weght factor Eucldea dstace measuremet. 6 Cocluso I ths paper a ew hybrd audo classfcato algorthm has bee proposed. Due to the poor classfcato accuracy of the tradtoal Eucldeabased algorthm, we have proposed a ew algorthm by extractg the weght factor from Supportg Vector Model (SVM) ad applyg t to the Eucldea dstace measuremet. From the test results show secto 5, t has show that the proposed weght scheme Eucldea dstace s successful to mprove the recogto accuracy by 28% a dvdual expermet ad 7% for the overall. Due to the mmzed structural rss for Supportg Vector Classfcato, the weght ca dcate the relevace for each dmeso regardg a certa classfcato crtera. By usg the weght factor of Supportg Vector, the Eucldea-based dstace measuremet becomes more sestve to the classfcato boudary. The superor results audo data classfcato have preseted ths paper. It ca gve a more promsg drecto to the use of the SVM-based weght the Eucldea dstace ad K- NN rule. Refereces: [] Wold, E., Blum, T., Keslar, D., et al. Cotet- Based classfcato, search ad retreval of audo. IEEE Multmeda Magaze, 996,3(3):27~36. [2] Lu, Zhu, Wag, Y., Che, T. Audo feature extracto ad aalyss for scee segmetato ad classfcato. Joural of VLSI Sgal Processg Systems for Sgal, Image, ad Vdeo Techology, 998,20(/2):6~79. [3] J. Foote, Cotet-based retreval of musc ad audo, Multmeda Storage ad Archvg Systems II, Proc.of SPIE, C. C. J. Kuo et al., Eds., 997, vol. 3229, pp [4] Phlppe Smard,Marcel Mtra.The Nearest Neghbor Rule: A Short Tutoral [5] Keogh, E. ad Rataamahataa, C.A. Exact dexg of dyamc tme warpg. Kowledge ad Iformato Systems, ~386. [6] L, S.Z.a.G., G. Cotet-based Audo Classfcato ad Retreval usg SVM Learg. Mcrosoft Research Cha, [7] Chag, C.-C. ad L, C.-J., {LIBSVM}: a lbrary for support vector maches.200 Retreved from [8] zhou, C.Z., Le, L. ad Zheg-a, Y. Feature- Weghted K-Nearest Neghbor Algorthm wth SVM. ACTA SCIENTIARUM NATURAIdUM UNIVERW ATIS SUNYATSENI, 44 ().