COMPARISON OF ENHANCED SCHEMES FOR AUDIO CLASSIFICATION

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1 Volume 4, No. 1, December 13 Journal of Global Research n Computer Scence REVIEW ARTICLE Avalable Onlne at COMPARISON OF ENHANCED SCHEMES FOR AUDIO CLASSIFICATION Dr. V. Radha *1 and G.Anuradha *1 Professor, Research Scholars Department of Computer and Applcatons Avnashlngam Insttute for Home Scence and Hgher Educaton for Women, Combatore 43. radhaasrmal@gmal.com*, anu.anuradhasrnvas@gmal.com Abstract: In the modern era of communcaton, audo plays an mportant role n understandng a dgtal meda. Due to the rse of economcal audo capturng devces, the amount of audo data avalable both onlne and offlne s enormous and technques that can automatcally classfy and retreve these audo data s an mmedate need. An automatc content based audo classfcaton and retreval system conssts of three modules namely, feature extracton, classfcaton and retreval. Ths paper presents a comparatve study of two algorthms that performs these three steps n dfferent manners. The performance of the selected systems are analyzed whle usng four dfferent features (acoustc, perceptual, mel-frequency cepstral coeffcents (MFCC and a combnaton of perceptual and MFCC and four classfers that enhanced Support Vector Machne (SVM and Centrod Neural Network (CNN along wth ts base versons, SVM and CNN. Expermental results showed that the enhanced SVM algorthm when usng the combned feature vector produced mproved accuracy and reduced error rate. Keywords: Audo Classfcaton, Audo Retreval, Support Vector Machne, Centrod Neural Network, Audo Features, Modfed Eucldean Dstance. INTRODUCTION A Content-based Audo Classfcaton and Retreval (CACR System automatcally group audo data n large database nto dfferent audo types (categores whch can then be searched for a partcular sound or a class of sound electroncally based on the content analyss of audo sgnals [15]. The prmary goal s to group audo fles (based on ther content nto one of a number of predefned categores. A general framework s shown n Fgure 1.Audo data collecton conssts of audo nstances or audo fles. The frst step of CACR extracts audo features that represent characterstc nformaton about these audo nstances. The extracted features are stored as feature vectors or spaces, whch are used to tran a classfer. When an nput musc s obtaned, the same features are extracted. The machne learnng algorthm assocates the feature patterns of nstances wth ther classes and maps t to a class. All the data n that class are then retreved as matched audo. Thus, any CACR system, consst of two steps, namely, feature selecton and classfcaton based on the extracted features. The manner of handlng these two steps s drectly related to the effcency of the audo classfcaton and retreval system. The methods proposed for CACR can be grouped ntofve categores, namely, query by example, query by hummng, musc nformaton retreval, smlarty matchng methods and machne learnng methods. Query by Example systems ams at automatc retreval of meda samples from a database, whch are smlar to a user provded example [5, 1,11,3, 8]. Query by Hummng technquesare smlar to Query by Example, but here the user provdes the sample by hummng the song [14, 8, 5, 1, 16] ( Musc Informaton Retreval systems are Google-lke search engnesmanly used to retreve smlar audos[3, 4,, ] v Smlarty Matchng based Methods use dstance measures lke Fgure 1 : CACR System Eucldean dstances to compare nput audo data wth database fles and all the fles that are close (smlar to each other are retreved [1, 6, 8, 7] and (v Machne Learnng Methodsthatuse learnng algorthms lke artfcal neural networks, k nearest neghbours, AdaBoost, to buld models whch play a vtal role durng classfcaton and retreval [13,, 4]. As t can be seen, several algorthms have been proposed under each of these categores, whch ether enhance an exstng algorthm to mprove classfcaton accuracy or propose a new method that work better than exstng algorthms. Two works that belong to the second category are the proposals of [17] and [7] uses a centrod neural network wth a dvergence measure to perform of Gaussan Probablty Densty Functon (GPDF data. In comparson wth other conventonal algorthms, the DCNN desgned for probablty data has the robustness advantages of utlzng a JGRCS 1, All Rghts Reserved 37

2 V. Radha et al, Journal of Global Research n Computer Scence, 4 (1, December 13, audo data representaton method n whch each audo data s represented by a Gaussan dstrbuton feature vector. The author used a total of 4 features coverng tmbral, rhythmc and ptch features durng classfcaton. Ths feature set s referred to as acoustc feature set n ths paper. On the other hand, [7] used SVMs wth a bnary tree recognton strategy for classfyng and retrevng audo data. For audo retreval, the authors proposed a new metrc, called Dstance-From- Boundary (DFB. When a query audo s gven, the system frst fnds a boundary nsde whch the query pattern s located. Then, all the audo patterns n the database are sorted by ther dstances to ths boundary. All boundares are learned by the SVMs and stored together wth the audo database. Ths system used two types of feature sets, namely, perceptual features and MFCC (mel-frequency cepstral coeffcents features durng classfcaton. The research problem of the present research work s to compare these two works on ther ablty to classfy audo data to enhance the content-based audo retreval process. For convenence, the [7] model s referred to as GL-AC and [17] model s referred to as P-AC n ths dssertaton. The rest of the paper s organzed as follows. Secton presents the GL-AC system and Secton 3 presents the P-AC system. Secton 4 presents the results and compares both the algorthms n ther effcency n classfyng audo data. Secton 5 concludes the work wth future research drectons. GL-AC SYSTEM The GL-AC method conssts of three man modules, namely, feature extracton module, classfcaton module and retreval module. The frst module extracts two audo features namely, perceptual features and mel-cepstral features, whch are then combned to form a thrd feature set. Perceptual features refer to the sensaton of sound by humans. The perceptual features collected are total spectrum power (Equaton 1, subband-power (4 subbands (Equaton, brghtness (Equaton 3, bandwdth (Equaton 4 and ptch. Ptch s the fundamental perod of a human speech waveform and s an mportant parameter n the analyss and synthess of speech sgnals. In GL-AC algorthm, a smple ptch detecton algorthm based on detectng the peak of the normalzed autocorrelaton functon s used. The ptch frequency s returned f the peak value s above a threshold (T =.65, chosen emprcally or the frame s labeled as non-ptched. Apart from ths, two more features, namely, slence rato whch s the rato of number of slent frames to total number of frames and ptched rato whch s the rato of number of ptched frames total number of frames are also calculated. P log F(ω dω (1 P log H L F(ω dω ( ω F(ω dω ω C (3 F(ω dω (ω ωc F(ω dω B (4 F(ω dω Where and s the frequency and half samplng frequency, F( s the power at the frequency, L and H are lower and upper bound of sub-band. The cepstrum can be llustrated by use of the Mel-frequency cepstra coeffcents (MFCCs. These are computed from the FFT power coeffcents. The power coeffcents are fltered by a trangular bandpass flter bank. The flter bank conssts of K=19 trangular flters. They have a constant melfrequency nterval and cover the frequency range of Hz 4Hz. Denotng the output of the flter bank by S k (k = 1,,, K, the MFCCs are calculated as : C K n (log Sk cos[n(k -.5π/K] (5 K k 1 Where n = 1,,, L and L s the order of the cepstrum. To form the feature vector, the mean and standard devaton of the perceptual features along wth slece and ptch rato forms the 18-dmensonal perceptual feature vector denoted as Percfeature Set n ths paper. Smlarly, CepsL feature set s obtaned usng cepstrumfaeturs. Both these features are combned to form PercCepsL feature set usng Equaton 6. PercCeptL = (Perc/s 1 (CepsL/S (6 Where stands for the concatenaton operaton. The second module uses Support Vector Machne (SVM for classfcaton. Durng vdeo retreval, a new Dstance From Boundary (DFM dstance measure s used nstead of the tradtonal Eucldean dstance measure.gven a set of tranng vectors belongng to two classes, SVM tres to separate the data nto two hyperlanes. Several possble hyperplanes can be formed, but the algorthm should select one that maxmzes the margn (the dstance between the hyperplane and the nearest data pont of each class. For ths purpose, kernel functons are used. The problem of audo classfcaton s a mult-class problem and s solved by SVM by combnng results of bnary classfers. The problem s now on the decson process used to combne the bnary classfcaton results to obtan the fnal decson. A common method used frequently s the votng strategy, whch s computaton expensve as t requres c(c-1/ comparsons. Ths problem s solved n GA-AC wth the use of bottom-up bnary trees. The formaton of bnary tree starts at the lowest level where a comparson s made between each par and a wnner s chosen. At the next stage, the wnner wll be moved one level up and the process s repeated. At the end of teraton, a unque class label wll be at the top level. Usage of bnary tree reduces the number of comparsons requred from c(c-1/ tmes to (c-1 tmes. Durng the retreval stage, conventonal methods use Eucldean dstance to measure the smlarty between audo patterns of the database and query. The tradtonal method JGRCS 1, All Rghts Reserved 38

3 V. Radha et al, Journal of Global Research n Computer Scence, 4 (1, December 13, has dsadvantages lke beng senstve to the sample dstrbuton, dfferent query patterns of the same class produces dfferent retreval results and fnally, the average retreval accuracy s low.these problems are solved by the use of a new metrc called Dstance from Boundary metrc and work on the prncple that a boundary exsts and separates the samples belongng to one class wth the remanng. Ths nonlnear boundary encloses the smlar patterns nsde no matter what the dstrbuton s. These boundares can easly be combned wth SVM tranng process and requres only smple operatons and therefore, are computaton nexpensve. probablty data whle t stll keep advantageous features of the CNN. Because the CNN have been proven to outperform other conventonal clusterng algorthms such as k-means and CNN, the D-CNN should show mprovements over the k-means and CNN algorthms n probablstc data. The pseudo code of D-CNN algorthm s gven n Fgure. P-AC SYSTEM The P-AC method consst of two modules, namely, feature extracton and classfcaton. In the feature extracton module, three types of features, namely, tmbral texture features, rhythmc content features and ptch content features are extracted from the audo data. The Tmbral texture features should exhbt propertes related to general tmbre of the sound. They are based on a Short Tme Fourer Transform (STFT and they are calculated on short-tme frames of a sound (MFCC. The feature vector for descrbng tmbral texture conssts of the followng features: means and varances of spectral centrod, rolloff, flux, zero crossngs over texture wndow, low energy and mean and varances of the frst fve MFCC coeffcents over the texture wndow. The rhythmc content features represent rhythmc structure of the musc. The selected features are relatve ampltude of the frst and the second peaks, rato of the second and the frst peaks, perod of the frst and the second peaks, overall sum of the beat hstogram. These features are based on detectng the most salent perodcty of the sgnal by usng Dscrete Wavelet Transform technque. The Ptch content features characterze audo sgnals n terms of energy of dfferent frequency bands and are based on multple ptch detecton technques [6]. The classfcaton module uses a dvergence based CNN classfer [6, 19, 9] whch uses Bhattacharyya dstance (Equaton 7 nstead of the tradtonal Eucldean dstance D(G,G 1 (μ 8,μ T 1 (μ,μ 1 ln. (7 Where μ and denote the mean vector and covarance μ and matrx of Gaussan dstrbuton G, respectvely., denote the mean vector and covarance matrx of Gaussan dstrbuton G, respectvely and T denotes the transpose matrx. The concept of wnner and loser n the CNN can be adopted for the D-CNN wthout any change except for applcaton of the dvergence measure for the dstance calculaton. In ths case, however GPDFs have two parameters to consder: mean, μ, and dagonal covarance,. The weght update for mean s the same as the CNN weght update. By usng the dvergence dstance as ts dstance measure, the D-CNN have abltes n clusterng the Fgure : Dvergence-Based Centrod Neural Network Algorthm (Source: [18] EXPERIMENTAL RESULTS Durng expermentaton, two datasets were used. The frst s an audo dataset downloaded from [1]. Ths dataset has 49 sounds havng 16 classes. The names of the audo classes are altotrombone, anmals, bells, cellobowed, crowds, female, laughter, machnes, male, oboe, percusson, telephone, tubularbells, volnbowed, volnpzz, water. Ths dataset s referred to as MuscleFsh dataset. The second dataset was created wth,663 audo sgnals havng rock, pop, azz, hphop, folk, country, speech and natural sounds. Ths dataset s referred to as Web dataset. Wth both datasets, a 7%-3% hold-out method was used to separate the tranng and testng datasets. All the experments were conducted usng 1-fold method and the average results are proected. To evaluate the classfcaton performance, two metrcs, namely, error rate and average retreval accuracy are used. Error rate s defned as the rato between the number of msclassfed examples and the total number of testng examples. The average retreval accuracy s defned as the JGRCS 1, All Rghts Reserved 39

4 Average Error Rate Average Accuracy V. Radha et al, Journal of Global Research n Computer Scence, 4 (1, December 13, average percentage number of patterns belongng to the same class as the query n the top matches. Further, to analyze the effcency gan obtaned by D-CNN and SVM- BTS, the results are compared wth the tradtonal counterparts, CNN and SVM, respectvely. The average accuracy obtaned whle usng four dfferent datasets, Acoustc, Perc, CepsL and PercCepsL for the four classfers, CNN, SVM, D-CNN and SVM-BTS s shown n Fgure 3.From the results, t s evdent that the concatenated PERC and CEPSL feature sets whle used wth SVM-BTS algorthm produced better accuracy when compared wth other classfers and feature sets. The SVM-BTS classfer showed.33%, 1.37% and.56% accuracy effcency gan when compared wth CNN, D-CNN and SVM respectvely. Ths shows that the usage of concatenated features wth SVM-BTS s well suted for automatc audo classfcaton and retreval. The average error rate of the four classfers whle usng dfferent features sets s shown n Fgure 4.The trend obtaned whle consderng the error rate s smlar to that of accuracy. The SVM-BTS classfer wth the combned feature set produces the lowest error rate CONCLUSION Acoustc Perc CepsL PercCepsL Fgure 3: Average Accuracy CNN D-CNN SVM SVM-BTS Acoustc Perc CepsL PercCepsL Fgure 4: Average Error Rate Ths paper presented a comparatve study on two audo classfcaton and retreval systems, namely, GP-AC and P- AC, whch enhanced the tradtonal classfcaton algorthms. Whle both the algorthms follow the same steps, they dfferent n the number of feature sets and classfers used. The GP-AC used three features sets (perceptual, MFCC and a combned set whle P-AC used acoustc features composed on tmbral texture, rhythmc content features and ptch content features. The GP-AC used an enhanced SVM classfer usng bottom-up bnary tree to reduce the computatons whle the P-AC enhanced Centrod Neural Network (CNN to employ Bhattacharyya dstance nstead of Eucldean dstance. Expermental results showed that SVM combned wth DFB dstance measure usng the combned feature vector s more accurate and produced mnmum error and hence s the best canddate for audo retreval systems. REFERENCES [1]. Aronovch, L. and Spegler, I. (7 CM-tree: A dynamc clustered ndex for smlarty search n metrc databases, Data and Knowledge Engneerng, Vol. 63, Pp []. Aucouturer, J.J., Pachet, F. and Sandler, M. (5 the way t sounds: Tmbre models for analyss and retreval of musc sgnals, IEEE Transactons onmultmeda, Vol. 7, No. 6, Pp [3]. Barrngton, L., Chan, A., Turnbull, D. and Lanckret1, G. (7 Audo nformaton retreval usng semantc smlarty, IEEE Internatonal Conference on Acoustcs, Speech and Sgnal Processng, Pp [4]. Casey, M.A., Veltkamp, R., Goto, M., Leman, M., Rhodes, C. and Slaney, M. (8 Content-based musc nformaton retreval: Current drectons and future challenges, Proceedngs of IEEE, Pp [5]. Chen, L. and Hu, B.G. (7 An mplementaton of web based query by hummng system, ICME, Pp [6]. Gartner, D., Kraft, F. and Schaaf, T. (7 An adaptve dstance measure for smlarty based playlst generaton, IEEE Internatonal Conference on Acoustcs, Speech and Sgnal Processng, Pp [7]. Guo, G. and L, S.Z. (3 Content-Based Audo Classfcaton and Retreval by Support Vector Machnes, IEEE Transactons on Neural Networks, Vol. 14, No. 1, Pp [8]. Guo, L., He, X., Zhang, Y., Lu, Y. and Peng, K. (7 A nose robust content-based musc retreval for moble devces, IEEE Internatonal Conference on Multmeda and Expo, Pp. 5. [9]. Hartgan, J. (1975 Clusterng Algorthms, New York, Wley. [1]. Helan, M. and Vrtanen, T. (7a A smlarty measure for audo query by example based on perceptual codng and compresson, Proceedngs of 1th Internatonal Conference on Dgtal Audo Effects, Bordeaux, France. [11]. Helen, M. and Vrtanen, T. (7b Query by example of audo sgnals usng Eucldean dstance between Gaussan mxture models, IEEE Internatonal Conference on Acoustcs, Speech and Sgnal Processng, Pp [1]. Last Access Date : JGRCS 1, All Rghts Reserved 4

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