ECE 366 Honors Section Fall 2009 Project Description

Transcription

1 ECE 366 Honors Secon Fall 2009 Projec Descrpon Inroducon: Muscal genres are caegorcal labels creaed by humans o characerze dfferen ypes of musc. A muscal genre s characerzed by he common characerscs shared by s members such as nsrumenaon, rhyhmc srucure, and harmonc conen of musc. Unl recenly muscal genre annoaon was done manually. Auomac muscal genre classfcaon can asss or replace he human user and s valuable for musc nformaon rereval sysems. In hs projec, you wll nvesgae and mplemen dfferen sgnal processng based feaure exracon mehods for muscal genre classfcaon. You wll mplemen he feaure exracon algorhms n MATLAB and ran and es a classfer for genre denfcaon. You wll evaluae he performance of he algorhm based on he accuracy of your genre classfcaon. Background: In hs secon, I ll provde a bref overvew of he mos common feaures [1,2] exraced from musc fles, n parcular he mbral exure feaures. For he followng feaures, he sgnal s broken no small, overlappng segmens n me referred o as analyss wndows. Analyss wndows have o be small enough so ha he frequency characerscs of he specrum are sable. Also, n order o capure he long erm naure of sound exure, a longer wndow, exure wndow s defned. Usually, an analyss wndow of 23 ms and a exure wndow of 1 s s used. 1. Shor-Tme Energy: The energy of he sgnal n each analyss wndow s compued and he mean and he varance of he energy over he exure wndow can be used as feaures. 2. Specral Cenrod: The specral cenrod s defned as he cener of gravy of he magnude specrum of he Fourer ransform. For musc analyss, you dvde he audo fle no overlappng frames of smaller lenghs and compue he Fourer ransform of each frame, hs s also known as he Shor-Tme Fourer Transform. The specral cenrod s hen defned as: C n1 n1 nm [ n] M [ n], where M [n] s he magnude of he Fourer ransform a frame and frequency bn n. The cenrod s a measure of specral shape and hgher cenrod values correspond o brgher mbral exures wh more hgh frequences. Usually he mean and he varance of he cenrod across he dfferen me frames n he exure wndow are used as feaures. 3. Specral Rolloff: The specral rolloff s defned as he frequency R below whch 85% of he magnude dsrbuon s concenraed M [ n] 0.85 n M [ n]. The mean and 1 n1 he varance of he rolloff across me frames n he exure wndow are used as feaures. 4. Specral Flux: The specral flux s defned as he squared dfference beween he normalzed magnudes of successve specral dsrbuons (normalzaon s done by he oal energy n ha wndow) F n] [ n n1 R 2 1 ] [ where [n] s he normalzed magnude of he Fourer ransform a he curren frame. The specral flux s a measure

2 of he amoun of local specral change. The mean and he varance of he flux across me frames n he exure wndow are used as feaures Tme Doman Zero Crossngs: Z sgn( x[ n]) sgn( x[ n 1]). Tme doman 2 n1 zero crossngs provde a measure of he nosness of he sgnal and he mean and he varance of zero crossngs across me frames n he exure wndow are used as feaures. 6. Mel-Frequency Cepsral Coeffcens (MFCC): Mel-frequency cepsral coeffcens are percepually movaed feaures based on he Fourer ransform. Afer akng he Fourer ransform of an analyss wndow, he magnude specrum s passed hrough a Mel flerbank wh varyng bandwdh mmckng he human ear,.e. small bandwdh a low frequences and large bandwdh a hgh frequences. The oupu energy of each flerbank s log ransformed and MFCCs are obaned by akng he Dscree Cosne Transform of he oupus. More specfcally, frs he frequency s scaled usng Mel flerbank H(k, and hen he logarhm s aken usng 1 X '( ln X ( k) H ( k, for m=1,2,,m wh M beng he number of flerbanks k 0 and M<< (he number of frequency pons). The Mel flerbank s a collecon of rangular flers defned by cener frequences f c ( wren as 0 for f ( k) fc( m 1) f ( k) fc( m 1) for fc ( m 1) f ( k) fc ( fc( fc ( m 1) H ( k,. The cener f ( k) f ( 1) c m for fc ( f ( k) fc ( m 1) fc ( fc ( m 1) 0 for f ( k) fc ( m 1) frequences of he flerbank are compued by approxmang he Mel scale wh f 2595log10 1. Then a fxed frequency resoluon n he Mel scale s 700 compued, correspondng o a logarhmc scalng of he repeon frequency, usng ( ) max mn, where ( M 1) max s he larges frequency n he Mel scale. The cener frequences of he flers are compued on he Mel scale as c ( m. To oban he cener frequences n Herz use he nverse ransform c ( / 2595 fc ( 700(10 1). Fnally, he MFCCs are compued by akng he dscree M 1 cosne ransform (DCT) of X ( c( l) X '( cos( l ( m )) for l=1,2, M. For m1 M 2 musc genre denfcaon, usually he frs 13 MFCCs are used. 7. Pch Deecon: Pch s defned as he audory arbue of sound accordng o whch sounds can be ordered from low o hgh. I s also defned as he fundamenal frequency of a harmonc sgnal. There are dfferen ways o deec he pch ncludng me doman auocorrelaon based mehods and frequency doman mehods. The smples algorhm for pch deecon s Harmonc Produc Specrum (HPS). The HPS algorhm measures he maxmum concdence for harmoncs n each specral frame as follows:

3 Y ( ) Y max R r1 X ( r) Y ( ) where R s he number of harmoncs o be consdered and frequency s he range of fundamenal frequences. The resulng perodc correlaon array, Y (), s searched for a maxmum value. Ocave errors are a common problem n pch measuremens from HPS. Almos always n hese error cases, he pch s deeced one ocave oo hgh. To correc for hs error, posprocessng should be done wh he followng rule: If he second peak amplude below nally chosen pch s approxmaely 1/2 of he chosen pch and he rao of ampludes s above a hreshold (e.g., 0.2 for 5 harmoncs), hen selec he lower ocave peak as he pch for he curren frame. Due o nose, frequences below 50 Hz should no be searched for pch. There are oher algorhms as dscussed n [5]. Afer he dfferen feaures are exraced, a feaure vecor of lengh L represenng each audo fle wll be formed. The feaure vecors can be classfed usng paern recognon echnques. In hs projec, snce we are more neresed n mplemenng he feaure exracon algorhms raher han desgnng he acual classfers we wll use a smple neares-neghbor classfer. For each audo fle sample, we wll classfy as he muscal genre ha s closes o. For each musc genre, we wll frs compue he average feaure vecor represenng ha group,.e v v ( k) assumng here are 10 samples n each genre class. We wll use he ake-oneou mehod and compue he Eucldean dsances o each class average and assgn each sample 10 k 1 2 o he class ha s closes o, 1 L ( v( k) v ( k)). The number of correc and ncorrec L k 1 assgnmens wll be couned o fnd he accuracy rae as umber of Correc Assgnmens. 30 Projec Gudelnes: You wll need o do he followng for hs projec: 1. Daa acquson or collecon: You wll need o download sample musc fles and open hem n MATLAB. There are many avalable musc daabases onlne where you can download audo fles. For example, hp://marsyas.sness.ne/download/daa_ses conans mulple daases for musc genre denfcaon. Of course, you can also use your own MP3 fles. You need o choose 10 audo fles belongng o hree dfferen muscal genres such as classcal, jazz, rock, blues, reggae, pop, meal, ec. You can use he MATLAB command wavread for readng.wav fles. Selec a couple of seconds of he audo fle for analyss (abou 2-3 seconds). In parcular, for genre classfcaon I would sugges selecng pars of musc where here s no human voce snce speech vs. musc classfcaon s a dfferen problem. 2. Choce of he feaure exracon mehods: You need o choose sx feaures (ou of he seven provded above) o mplemen. You can mplemen he feaures eher usng he gudelnes gven above or can use oher mplemenaons, n parcular for MFCC and pch deecon here are alernave algorhms ha you may wan o explore. 3. You wll need o dvde he audo fle no smaller me frames for analyss. You can choose he lengh of he me frame based on he number of samples n he audo fle. Expermen wh dfferen number of me frames o see he effec on he exraced feaures and he classfcaon accuracy. Usually an analyss wndow of 23 ms and a exure wndow of 1 s are used. You also need o denfy he amoun of overlap beween he dfferen frames.

4 4. Evaluaon: Afer you mplemen your feaure exracon and classfcaon algorhms, you need o evaluae hem on a leas hree musc genres each wh en audo samples. You wll evaluae he classfcaon wh respec o he seleced feaures,.e. frs exrac only one ype of feaure and compue he classfcaon accuracy and hen combne he feaures no a larger feaure vecor and compue he classfcaon accuracy wh mulple feaure ypes. You can presen your classfcaon resuls usng a able or bar graphs. You wll also evaluae how he accuracy changes wh respec o he analyss wndow lengh, he amoun of overlap beween he wndows (consder a leas hree dfferen amouns of overlap,.e. 20%, 50%, 80%), and he parameers used n he feaure exracon algorhms such as he number of flers n MFCC exracon. MATLAB Commands: Some MATLAB commands ha can be useful are lsed below for your convenence: Ff-compues he Fourer ransform of he sgnal Ffshf-rearranges he ff values such ha hey are symmerc wh respec o 0 frequency Dc- compues he dscree cosne ransform Sound- can be used o lsen o sound fles n MATLAB Wavread- wll read n any.wav fle no MATLAB Mp3read-read.mp3 fles no MATLAB, see he code provded on hp://labrosa.ee.columba.edu/malab/mp3read.hml Sum-sums up he values n a vecor or marx Mean,Var- compue he mean and varance of vecors/sgnals Abs,angle - Useful for fndng he magnude and he phase of complex funcons. Delverables: For hs projec, I expec you o urn n he followng: A projec repor: The repor should be well-wren wh he followng secons: Absrac, Inroducon, Mehod, Resuls, Conclusons and References. The absrac should nroduce he problem; summarze he approach and major fndngs n less han 150 words. In he nroducon secon, you should summarze he problem and he major approaches. Ths secon wll requre you o do some research on musc genre classfcaon mehods and synhesze hem n your own words. The mehod secon wll follow he asks I oulned above, descrbng how you chose he dfferen parameers n he desgn. The resuls secon should summarze your fndngs n a able forma or bar graphs. Fnally, you should dscuss your fndngs and commen on how he resuls can be mproved. The repor should be yped, 12 pon, doublespaced and no more han 10 pages n oal. You can use MATLAB code wren by ohers as long as you frs es exensvely o make sure ha does wha s supposed o do and ce. MATLAB code ha you developed should be added o he projec under he Appendx secon. Ths secon s no ncluded n he 10-page lm. A CD conanng he audo fles used for genre denfcaon. Gradng: For hs projec, you wll work n groups of 2. Each group needs o mee wh me before ovember 25, For hs meeng, I expec you o do a prelmnary research no he dfferen feaure exracon mehods, choose he sx feaures ha you are gong o mplemen, make a choce on whch hree genres you wll be classfyng, and download and read musc fles no MATLAB successfully. Ths meeng wll also gve you a chance o ask any mplemenaon relaed quesons ha you mgh have. Each group needs o e-mal me and make an apponmen for hs meeng (abou 20 mnues long). The gradng wll be based on he qualy of he repor, your resuls and your dscusson of he resuls. I expec all of he groups o mplemen sx feaure exracon algorhms ncludng a leas

5 he followng componens: es he classfcaon algorhm on hree musc genres wh a leas 10 audo segmens n each genre, quanave analyss of he effec of he sze of he analyss wndow on he resuls, quanave analyss on how dscrmnave he dfferen feaures are and he effec of he dfferen desgn parameers, e.g. ff lengh, lengh of he analyss wndows, he number of Mel frequency flerbanks ec. You can mprove your resuls and can ge exra cred by mplemenng more advanced classfcaon algorhms and oher feaures such as he ones n [1,2] o mprove your denfcaon resuls and esng your algorhm on a larger daase of musc sgnals. The fnal repors and he CDs conanng he audo fles used are due by December 16, 2009 by 5 p.m. References: 1. G. Tzaneaks and P. Cook, Muscal Genre Classfcaon of Audo Sgnals, IEEE Transacons on Speech and Audo Processng, vol. 10, no. 5, July M. F. McKnney and J. Breebaar, Feaures for Audo and Musc Classfcaon, Proc. 4 h In. Conf. on Musc Informaon Rereval, S. Sgurdsson, K. B. Peersen and T. Lehn-Scholer, Mel Frequency Cepsral Coeffcens: An Evaluaon of Robusness of MP3 Encoded Musc, Proceedngs of he Inernaonal Symposum on Musc Informaon Rereval, M. Slaney. Audory oolbox, verson 2. Techncal Repor # , Inerval Research Corporaon, P. de la Cuadra, A. Maser, C.Sapp, Effcen Pch Deecon Technques for Ineracve Musc, Inernaonal Compuer Musc Conference, 2001.