Audio Content Classification Method Research Based on Two-step Strategy

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1 (IJACSA) Internatonal Journal of Advanced Computer Scence and Applcatons, Audo Content Classfcaton Method Research Based on Two-step Strategy Sume Lang Department of Computer Scence and Technology Chongqng Unversty of Posts and Telecommuncaton Chongqng, Chna Xnhua Fan Department of Computer Scence and Technology Chongqng Unversty of Posts and Telecommuncaton Chongqng, Chna Abstract Audo content classfcaton s an nterestng and sgnfcant ssue. Audo classfcaton technque has two basc parts: audo feature extracton and classfer. In general the audo content classfcaton method s frstly to dentfy the orgnal audo nto text, then use the dentfed text to classfy. But the text recognton rate s not hgh, some words that good for classfcaton are dentfed by mstake causng that the classfcaton effect s not deal. In order to solve these problems above, ths paper proposes a new effectve audo classfcaton method based on two-step strategy. In the frst step the features are extracted by usng the mproved mutual nformaton and classfed wth Naïve Bayes classfer. After classfcaton of the frst step, an unrelable area s determned, and samples wth features n ths area go on to be classfed wth the second step. In the second step, textual features extracted wth CHI statstc method are used to buld a text feature space model. Then audo features contanng MFCC and frame energy are combned together wth the text features to buld a new feature vector space model. Fnally, the new feature vector space model s classfed usng Support Vector Machne (SVM) classfer. The experments show that the two-step strategy classfcaton method for audo classfcaton acheves great classfcaton performance wth the accuracy rate of 97.2%. Keywords Two-step Strategy; Audo classfcaton; MFCC; Frame energy; Nave Byes; Support vector machne (SVM) I. INTRODUCTION Wth the hgh-speed development of nformaton ndustry, the dgtal nformaton grows rapdly. People have urgent demand on the process of dgtal nformaton. Images, vdeo and audo are the man forms of meda n the feld of nformaton processng, and audo occupes very mportant poston. How to quckly grasp the most effectve nformaton s an mportant problem people have to be faced wth. Because the audo classfcaton can solve the problems of nformaton clutter to a certan extent and t s convenent for users to accurately locate the requred nformaton, t has become a key practcal technology. For example, n the telephone bookng and moble servce hotlne, propretor can evaluate employee s job performance accordng to the employee s contents, atttude, tone n the phone etc. At the same tme t plays an mportant role n speech retreval and the depth of the speech nformaton processng wth a broad applcaton prospect. Typcal audo classfers used n the related papers contan Mnmum Dstance method, Support Vector Machne (SVM), neural network, Decson Tree method and the Hdden Markov Model[1][2][3][4] etc. Currently audo content classfcaton research manly has two drectons: one method s frstly to recognze the audo nto text, and then classfy the text after the dentfcaton; the other method s drectly to use audo features, such as MFCC, frame energy, and ptch frequency and so on to classfy audo. However usng the text recognzed form the audo nformaton to classfy n the frst method has some problems n the followng. The recognton rate of the frst method s not hgh. Some words that contrbuton to the classfcaton are recognzed by mstake. The classfcaton method s usually n sngle step classfcaton strategy. These problems cause that the classfcaton effect s not deal. Fan XngHua[5] etc proposed a new hghly effectve two-step Chnese text classfcaton method; t acheved good effect n Chnese text classfcaton research. Ths paper consders mportng ths two-step classfcaton strategy nto audo classfcaton, and verfyng the actual classfcaton effect through experment. It s focused on applyng the two-step classfcaton strategy n audo classfcaton n ths paper. There are several problems needed to be studed. Whether the texts after dentfcaton are the same as plan texts wth the phenomena that most msclassfed ones are n a fuzzy regon consttuted n the twodmensonal space structure. Whether audo features such as MFCC, frame power could be effectve for audo content classfcaton. Text features of texts dentfed from audo are fewer than features of the plan texts, and especally some words wth excellent contrbuton to the classfcaton are recognzed by mstake. These problems cause the classfcaton effect n the frst step s not deal. Whether the combne of text features and audo features such as MFCC and frame energy could enhance the accuracy of audo content classfcaton n the second step. Whether ths two-step classfcaton strategy for audo classfcaton s feasble. To am of solvng the above problems, we proposed a new audo classfcaton method based on two-step strategy 57 P a g e

2 (IJACSA) Internatonal Journal of Advanced Computer Scence and Applcatons, combnng text features and audo features. At last the method s studed wth Nave Bayes and Support Vector Machne classfers. The basc fundamental s as follows: n the frst step mproved mutual nformaton s used to extract the characterstcs, and the Naïve Bayes classfer s used for classfyng. If the classfcaton result of the frst step s relable the classfcaton decson wll be gven, otherwse the samples wll go to the second step. In the second step t combnes audo features MFCC, frame energy wth text features selected wth CHI statstc formula as the total classfy features, then uses Support Vector Machne classfcaton method to classfy. In the end, the fnal classfcaton judgment s gven accordng to the results of the two steps. II. CHARACTERISTIC ANALYSIS IN THE AUDIO CONTENT CLASSIFICATION Accordng to the analyss of sports audo and news audo, t s found that usually there are some specal unsteady sounds n the sports audo such as whstle, sonorous voce of narrator and cheers etc. Oppostely the news audo usually s relatvely stable, and the knds of sounds above don t exst. These voces contan rch semantc nformaton and can be very useful to dstngush these two types of audo. Through the experments, t s proved that audo features MFCC, frame energy under dfferent audo category have obvous dstngush ablty. A. Frame Energy Short-tme Energy[6][7] s the energy focused by the sample sgnal n a short audo frame. Sequence of Short-tme energy reflects the detaled change rule of the voce ampltude or energy over tme. sports audo, but news audo rarely appears ths knd of sounds through analyss. Because of these dfferences thers energy dstrbuton curves are obvously dfferent between wth each other. Accordng to the above analyss Short-tme Energy can be consdered as the feature used to classfy the audo. B. Mel Frequency Cepstrum Coeffcent Mel frequency cepstrum coeffcent (MFCC) [8][9] s the cepstrum parameters extracted from the Mel scale frequency doman, and also a knd of percepton frequency cepstrum parameters. In order to accord wth the human s audtory characterstcs, MFCC generally adopts the trangular flter group to flter the energy coeffcent of Fourer Transform, and do the Mel scale transformaton for frequency doman. MFCC coeffcent s frstly used for speech or speaker recognton, but the results of lterature [10][11] show that MFCC coeffcent can mprove the accuracy of audo content classfcaton Fg. 2. one-dmensonal map of MFCC feature Fg. 1. the dstrbuton curve of frame energy Fgure1 (a) and (b) respectvely descrbe the short-tme energy dstrbuton curve of these two types of audo. The Energy of sports audo generally s hgh, and the ampltude usually changes slowly. On the other hand the energy dstrbuton of the news audo s relatvely concentrated, and ampltude changes quckly. Actually there are a lot of sounds such as whstle, sonorous voce of narrator and cheers n the Lu Jan [12] puts forward a knd of audo classfcaton method based on Hdden Markov Model. That paper ponted out that the dfference of MFCC coeffcent Δ MFCC could well reflect the dynamc change characterstcs of audo sgnal wth the calculaton and analyss of mult-stage MFCC and the dfference coeffcent ΔMFCC, so they can be used to revealed the tme statstcal propertes of dfferent types of audo. Fgure2 (a) and (b) respectvely represent the short MFCC feature of sports audo and news audo n one dmensonal mappng. Through the compare of these two pctures t can be found that the MFCC feature mappng values of sports audo jump densely n a short local area and ampltude s small. Oppostely MFCC feature mappng values of news audo jump sparsely densely n a short local and the ampltude s much larger. These dfferences prove that the MFCC features can reflect the rch semantc characterstcs, and the most mportant of all they have good dstncton between two types of audo category P a g e

3 (IJACSA) Internatonal Journal of Advanced Computer Scence and Applcatons, III. AUDIO CLASSIFICATION METHOD BASED ON TWO- STEP STRATEGY A. The Rewrtng for two types of Naïve Bayes Classfer Gven a bnary text vector d=w w w w =0 or 1. If the th feature appears n the text,,,, 1 2 D w =1, otherwse w 0, P k P(w k = 1 c ), P() means the probablty of event (). The dscrmnaton functon for two types of Naïve Bayes classfer can be expressed as follows: D D k1 K k 1 k1 k= 1 D P c 1 d P c1 1- Pk1 f d = log = log + log + P c 2 d P c2 k=1 1- Pk2 (1) P Pk2 W log - WKlog 1- P 1- P When f d 0, text d belongs to type c 1. Otherwse t belongs to type c 2. B. The desgn of the Support Vector Machne SVM Classfer The prncple of Support Vector Machne (Support Vector Machne) [13] can be smply descrbed as follows: t hopes to seek a hyper plane whch can separate postve samples from negatve samples n the tranng set wth the largest blank space on ether sde. It s gven a set of tranng samples as follows. T {( x, y )} ( R n Y) l s.t x n R, 1,... l If y Y {1, 1}, SVM becomes the process of constructng hyperplane ( w. x) b 0 whch separates the two types of sample ponts. Among them, the dstance from the nearest pont n the samples to the hyperplane s called nterval, as shown n the Fgure 3. k2 classfcaton n the frst step, the formula (1) s took apart and two posteror probablty parameters representng the probablty where one sample belongs to one of two types are respectvely fetched out as follows. P X (2) D k1 W log K k 1 1- P k1 D P Y = W l (3) k 1 k2 og K 1- P k2 D 1 P c1 1- Pk1 con = log + log P c 1- P (4) 2 k k2 X represents posteror probablty where the text d belongs to type c 1, and Y represents posteror probablty where the text d belongs to type c 2. Con s a constant only related to the tranng samples set, and would not be changed by text d. So the formula (1) can be rewrtten as follows. d f X Y con (5) Formula (5) represents that the two-step Naïve Bayes classfer can be vewed as a process of seekng a straght lne 0 f d n two-dmensonal space consttuted by X and Y. In ths way, sample text can be expressed as a sngle pont x, y n the two-dmensonal space determned by formula (2) and formula (3), the dstance Dst from ths pont to dvdng lne f d 0 s as follows. Dst 1 2 x ycon (6) Fg. 4. the dstance from the text pont to the dvdng lne. Fg. 3. SVM schematc dagram C. The observatons of msclassfcaton samples n the frst step In ths two-step strategy classfcaton method, n the frst the Hdden Markov Model s used for speech recognton n whch the audo samples are recognzed to the texts. The mproved mutual nformaton formula s used for feature selecton of dentfed texts n the frst step n order to get the Bnary text vector, then the Bayes classfer n part A s used to classfy. In order to study and analyss the result of As shown n Fgure 4, sample text d belongs to type c1 when Dst 0, sample text d belongs to type c2 when 0 Dst. The purposes that the formula (1) s changed to the formula (5), and then evolved nto formula (1) are as follows. Takng advantage of formula (6) can easly nvestgate and analyze text classfcaton error, and dscuss the relatonshp between the dstance Dst and the 59 P a g e

4 (IJACSA) Internatonal Journal of Advanced Computer Scence and Applcatons, classfcaton error n the condton of a gven classfcaton method and textual features set n the twodmensonal space made up of X and Y. It s convenent to assess the relatonshp of relablty of classfcaton and the value of the dstance Dst and determne the unrelable part of the classfcaton results n the frst step by takng advantage of formula (6). In ths paper c 1 and c 2 respectvely represent for sports category and news category. Usng the corpus n the expermental secton as samples set, dstrbuton of the text after dentfcaton can be calculated n the two-dmensonal space wth X as the abscssa value and Y as the ordnate value as shown n Fgure 5. Fgure 5 (a) and Fgure 5 (b) respectvely correspondng to the dstrbuton of sports audo and the news audo. It can be seen from the fgure that two types of audo are dstrbuted on two sdes of the dvdng lne n two-dmensonal space. The texts after dentfcaton classfed by mstake are located n the above area of dvdng lne n Fgure 5 (a) and n the below area of dvdng lne n Fgure 5 (b). Through observng of these texts t s clear that the samples classfed by mstake manly concentrate n the area very close to the dvdng lne. calculated by formula (6), and major errors happen n the narrow area close to the dvdng lne. D. The method of determnng the unrelable area after classfcaton of the frst step Through the observaton of classfed result n the frst step of classfcaton process t can be seen that the samples are classfed by mstake manly concentrated on the area close to the dvdng lne. A range where Dst values are near to zero can be determned as the unrelable area n the frst step of classfcaton process, and then decson whether relable or unrelable can be made. Formula (7) s the dscrmnant formula for classfcaton. Dst1 dst Dst2 the classfcaton s unrelable (7) dst for other values the classfcaton s relable In order to get the most optmal boundary constant Dst 1, Dst 2, two evaluaton ndexes are ntroduced: error rate and area percentage. Error Rate: Area Percentage: EC(dst 1,dst 2) ER(dst1,dst2) 100% EC_CON RP T(dst 1,dst 2) (dst1,dst2) T_CON 100% Fg. 5. the dstrbuton of dentfed text Fan XngHua [5] puts forward most of the errors are n a narrow area close to the dvdng lne n the plan text classfcaton study. That s to say, f the texts whose dstance are very close to the dvdng lne are got rd of, then the test performance of the classfer wll be mproved. Ths assumpton has been proven by experments. Ths paper mports ths dea to the frst step of classfcaton process n whch the text after speech recognton s classfed. Accordng to the observaton of the Fgure 5, t s assumed that the performance of the classfer s relate to the dstance Dst EC(dst 1,dst 2) s the count of the samples that are classfed by mstake wth the Dst value n the range of dst1, dst 2. EC_CON s the total number of samples that are classfed by mstake. T(dst 1,dst 2) s the count of samples wth the Dst value n the range of dst1, dst 2. T_CON s the total number of all test samples. In order to convenently draw the curve lnes that show changes of ER and RP followng Dst range, one of two endponts could be fxed to a constant value. Usng the corpus n the expermental secton as samples, Fgure 6 shows the curves of ER and RP wth the fxed constant value of zero. The curve lnes on the rght sde of Y axs reflect the changes of ER and RP followng dst2 value when the value of dst 1 s zero, and smlarly curve lnes n the other sde Y axs reflect the changes of ER and RP followng dst1 value when the value of dst 2 s zero. Through observaton of Fgure 6 can be seen that the ER grows rapdly when the value of dst 2 s small, and then gradually stablzes form the nflecton pont. The stuaton n the left of Y axs s the same as the rght of Y axs. The value of ER n poston of rght nflecton pont wth the dst 2 value of 5 s 49% meanng 49% samples(sports samples) classfed by mstake are concentrated on the range of [0,5], and RP s 15% meanng the sports samples whose Dst values are n the range of [0,5] account for 15% of the total test samples. The nflecton pont on the other sde s the poston wth dst 2 value of - 4, where the ER and RP values are 47% and 19% 60 P a g e

5 (IJACSA) Internatonal Journal of Advanced Computer Scence and Applcatons, respectvely. It means that 47% samples (news samples) classfed by mstake are concentrated on the range of [-4, 0] where samples account for 19% of the total test sample. To sum up, the 97% part of samples wrongly classfed dstrbute on the scope of the [-4, 5], and at the same tme samples whose Dst value are n ths area of only account for 34% of all samples. Therefore, the two nflecton ponts values are farly approprate to be as the endpont value of unrelable area for formula (7). Fg. 6. ER and RP curve changed wth dstance Dst E. The characterstcs of the two-step classfcaton method The most sgnfcant value of two-step classfcaton method put forward n ths paper s able to mx text and audo feathers together for classfyng. Generally the tradtonal method frstly recognzes the audo nto text, does feature extracton and chooses an approprate classfer to classfy. But the recognton rate s not hgh and some words that well contrbute to the classfcaton are recognzed by mstake or recognzed to other homophones, these problems cause that the classfcaton effect s not deal. Accordng to the analyss of two knds of audo, t s found that there are usually some specal unsteady sounds n the sports audo such as whstle, sonorous voce of narrator and cheers etc. Oppostely the news audo s usually relatvely stable, and these knds of sounds don t exst n general. Through the experment, t s proved that the audo features contanng MFCC and frame energy have good ablty to dstngush raw audo dfferences above. Twostep strategy classfcaton method n ths paper effectvely combnes audo and text features wth each other n order to get a hgh performance classfcaton. IV. EXPERIMENT A. The Experment Database The audo corpuses used n the experment are all from the real envronment context. Sports corpus s manly from CCTV lve program basketball and soccer sports events and news corpus are from CCTV news broadcast program. There are 1050 sports audo samples and 1200 news audo samples. The proporton of tranng samples and testng samples s 3:1 on the experment. In order to ensure the unformty of tranng samples and testng samples, the tranng sample and test samples are obtaned n a cross way. Samplng frequency s 16 KHZ, and capacty of data s 491 MB. B. Evaluaton ndex The accuracy performance of classfcaton can be measured n the followng ndex: the classfcaton accuracy of one category and the average classfcaton accuracy. The defntons are as follows: count of C samples predcted to be C accuracy of C = count of samples predcted to be C count of C samples predcted to be C average accuracy= the total number of test samples C. Experment steps of Two-step classfcaton method Desgn and mplement the contnuous Chnese speech recognton system based on HTK. The speech recognton rate s 78.04% n ths paper s experment. Complete feature extracton usng the enhanced mutual nformaton formula for the text after dentfcaton. Use the mproved Naïve Bayes classfer for the frst step. Analyze and observe the experment result n the frst step, and get a good boundary of unrelable area. Accordng to the boundary of the unrelable area, determne a fuzzy regon. Use the SVM classfer to classfy the samples n the fuzzy regon n the second step. Make the fnal classfcaton decson. D. Analyss of expermental results Ths paper has done the classfcaton experments by fve knds of methods: Method 1: extract text features usng the mproved mutual nformaton formula, and use Naïve Bayes classfer to classfy the audo. Method 2: use audo features contanng MFCC and frame energy to construct vector space model, and use the SVM classfer to classfy the audo. Method 3: use CHI statstc formula to extract text features, and combne two knds of features ncludng audo features contanng MFCC frame energy and text features to construct vector space model. At last use the SVM classfer to classfy the audo. Method 4: Use Two-step strategy method combnng method 1 wth method 2 to classfy the audo. Method 5: Use Two-step strategy method combnng method 1 wth method 3 to classfy the audo P a g e

6 (IJACSA) Internatonal Journal of Advanced Computer Scence and Applcatons, Classfcaton Method TABLE I. Sports THE CLASSIFICATION RESULTS News Method % 88.78% 89.72% Method % 85.96% 83.84% Method % 93.23% 94.16% Method % 95.32% 95.79% Method % 96.62% 97.2% Average accuracy rate The table 1 shows the results of fve knds of classfcaton methods. Through analyss of method 2 s experment results t can be seen that the performance of the method usng only audo features to classfy s not good. The contrast of results of method 2 and method 3 shows that combnng two knds of features ncludng text features and audo features wth each other smply can well mprove the classfcaton effect. Accordng to the compare of classfcaton effects of method 4 wth method 1 and method 2, t can be seen that the addton of audo features mproves the problem that the accuracy rate s not hgh because of the naccuracy n text nformaton dentfcaton. Obvously t comes to a very mport concluson that the classfcaton accuracy rate of Method 4 based on two-step strategy has great classfcaton accuracy promoton. Ths concluson apples to the contrast method 5 wth method 1 and method 3 at the same tme. What s more, the dfference between Method 5 and Method 4 s that Method 5 mports text features agan n the second step of classfcaton process n order to get a larger number of classfcaton features n the second step. The contrast of results of method 4 and method 5 shows that mportng the text features for samples n the unrelable area of the frst step can acheve a better correcton effect. V. CONCLUSION As a man form of meda audo plays an mport role n the feld of nformaton processng. Audo classfcaton has become a hot practcal technology wth a wde applcaton prospect n the felds of speech retreval, deep voce nformaton processng. In general the audo content classfcaton method s frstly to dentfy the orgnal audo nto text, then use the dentfed text to classfy. But the text recognton rate s not hgh, some words that are good for classfcaton are dentfed by mstake causng that the classfcaton effect s not deal. Ths paper provdes a new effectve audo content classfcaton method based on two-step strategy. The basc fundamental s as follows: n the frst step mproved mutual nformaton s used to extract the characterstcs, and the Naïve Bayes classfer s used for classfyng. If the classfcaton result of the frst step s relable the classfcaton decson wll be gven, otherwse the samples go to the second step. In the second step t combnes audo features MFCC, frame energy wth text features selected wth CHI statstc formula as the total classfy features, then uses Support Vector Machne classfcaton method to classfy. Through the experments, t comes to a concluson that the audo content classfcaton method based on two-step strategy n ths paper s effectve n enhancng the performance of audo content classfyng, and t can acheve the great classfcaton performance wth the classfcaton accuracy rate of 97.2%. REFERENCES [1] K.Subashn,S.Palanvel,V.Ramalgam, Audo-vdeo based segmentaton and classfcaton usng SVM, 2012 Thrd Internatonal Conference on Computng Communcaton & Networkng Technologes (ICCCNT),vol., no., pp.1-6, July 2012 [2] T.Gannakopoulos, D.I.Kosmopoulos, A.Arstdou,and S. Theodords, A mult-class audo classfcaton method wth respect to volent content n moves usng bayesan networks, n IEEE Workshop on MSP, pp.90-90, [3] Kranyaz.S, Ahmad Farooq Quresh, Gabbouj.M, A generc audo classfcaton and segmentaton approach for multmeda ndexng and retreval, IEEE Transactons on Audo, Speech and Language Processng, vol.14(3), pp , [4] B.Lang, SY.Lao, HX.Lao, JY. Chen, Audo Classfcaton and Segmentaton for Sports Vdeo Structure Extracton usng Support Vector Machne, 2006 Internatonal Conference on Machne Learnng and Cybernetcs, pp , [5] XH. Fan, and MS. Sun, A hgh performance two-class chnese text categorzaton method, Journal of Computers, Chna, vol. 29(1), pp , [6] Umapathy,K, Krshnan,S, Rao,R.k. Audo Sgnal Features Extracton and Classfcaton Usng Local Dscrmnant Bases," IEEE transactons on audo, speech and language processng, vol.15(4), pp , [7] U.Shrawankar, V.Thakare, "Feature Extracton for a Speech Recognton System n Nosy Envronment: A Study", ICCEA, pp , [8] S. Jothlaskm, S. Palanvel, V. Ramalngam, Unsupervsed speaker segmentaton wth resdual phase and MFCC features, Expert System Wth Applcatons, Inda, pp , 2009 [9] H. Cao, C. Xu, X. Zhao, SJ. Wu. The Mel-frequency cepstral coeffcents n speaker recognton, XAn: Journal of northwest unversty (natural scence edton), vol.43,, pp , 2013 [10] SahamM, Dumas S, Hecheman D, Horvtz E. A Bayesan approach to flterng junk E-mal, Madson Wsconsn AAAI Techncal Report WS , pp [11] L, S.Z. Content- Based classfcaton and retreval of audo usng the nearest feature lne method, IEEE Transactons on Speech and Audo Processng, vol.8(5), pp.619~ [12] J. Lu, YS. Chen, ZS. Sun, FY. Zhang. Automatc audo classfcaton based on Hdden Markov Model, BeJng: Journal of software, vol.13(8), pp , [13] Subashn. K, Palanvel.S, Ramalgam.V, Audo-vdeo based segmentaton and classfcaton usng SVM, 2012 Thrd Internatonal Conference oncomputng Communcaton & Networkng Technologes(ICCCNT), pp.1-6,26-28, P a g e

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