Infant Cries Identificaton by Using Codebook As Feature Matching, And MFCC As Feature Extraction

Journal of Thooretical and Applied Information Technology 20"' October 20l3 Yo! 56 No.2

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lS:>N. ＱＹＹＲＭｬｬｍｾ＠ WWW. Hllll.org I -ISSN: 1817-J l?S

INF ANT

CRIES IDENTIFICATION

BY USING CODEBOOK AS FEATURE MATCHING,

AND MFCC AS FEATURE EXTRACTION

1_{i\1E011ANITA OE\\ I RENA '-'Tl ,} 1

AGUS BUONO, 1WISNU ANANTA KUSUMA

1

Diploma Program of Bogor Agricultural Universi ty

2_{Compukr Science Department of Bogor Agricultural University}

E-mnil: 1mcJha nuho a "ahoo.com. ｾｵ､･ｾｨ｡＠

ii

vahoo.co.id , lananta ci'iph.ac.id

ABSTRACT

In this paper, \\C focused on automation of Dunstan Bab) Language. This system uses MFCC as feature

e\lraetion and codebook as ti!oture matching. The codebook or clusters is made from the proceeds of all the buhy's cries du1a, by using the k-mcans clustering. The datn is taken from Dunstan Baby Language videos 1hat has been processed. The d:ita is divided into two, 1raining data and testing data. There are 140 training dato, each of \\hich represents the 28 hungry infant cries. 28 sleepy infant cries, 28 \\anted to burp infant cries, 28 in pain infant cric,. and 28 uncomfortable infant cries (could be because his diaper is wet/too hot/cold air or an} thing else}. lhe ｬ｣ｾｵｮｧ＠ data is 35, respectively 7 infant cries for each type of infant cry. The research '.tr) i ng frame length: 25 ms/frame length

=

275. 40 ms/frame length = 440. 60 ms/ frame length = 660. O\ erlap frame: 0°o. ＲＵｾｯＮ＠ 40%, the number of codewords: I to 18, except for frame length

275 and O\.crlap frame = O u-,ing I to 29 clusters. The identification of this type of infant cries uses the minimum distance of euclidean distance. Accuraq \aluc is between

37%

and

94%.

Sound 'ch' is the most familiar. whereas sound ·m,11· is always missunderstood and generally it is known as 'neh' and 'eairh' . The weakness point of this research is the silent is onl) be cut at the beginning and at the end of speech signal. Hopefully. in the nc\t research. the silent cnn be cul in the middle of sound so that it can produce more specific sound. It has impucl on the bigger accurac) as \\ell.

Keywords: Codebook. D1111.11t111 baby la11g11age, Infant cries. K-means cf11sreri11g, MFCC

I. I NTRODUCTION

The first verbal communication which is mastered b) a bab> is cr:ang. Currently, there is a system that learns the meaning of a 0-3 month old infanl cries \\hich is called ｄｵｮｾｴ｡ｮ＠ !lab) Language (DDL). DBL is introduced b) Priscilla Dunstan, nn

Auslralinn mu!>ician "ho has gol 1nlent to

remember all 1..inJs of sounds. kM\\O as sound photograph .. '\ccording to DBI 'cr-sion, there are lhc bab) languages: .. nch" means hunger, "owh .. means tired \\hich indicates lhat the baby is getting sleepy. "eh'. means that the baby \\ants to burp, "eairh" means pain (\\ind) in the stomach. and "heh" means uneomfonablc (could ll<! due to a \\<Cl diaper, too hot or cold air, or an.> thing else).

The expenise to determine the meaning or infant cries in DBL version is still a bit sparse so the information of bab)

·s

Lr) meaning is not readil) a\·ailable to the parenti.. Current!}. a S)Stem to transl';.:r knowledge nbout DBL is b) attending a training or seminar, or b) ｳｴｵ､ｾ＠ ing their own infant cries meaning (in DBL version) \\hich is nlread)'

packaged in the fonn of optical discs. The materials of DBL can also be downloaded on the internet. DBL system users, particularly in Indonesia, will be

mun: connc.Jcm '"ith the ｾｵｮ｣ｬｵＺｩｩｯｮ＠ they make if

there is a software that can automatically generate the meaning of their infant cries. It can strengthen their conclusions. In addition, this software will olso be useful for parents who do not attend any DBL 1taining or seminar, so parenls can understand the language or the crymg of their baby.

Research on infant cries has been done by researchers, such as: cries classification of normal nod abnormal (hypoxia-oxygen lacl..s) infant by using a neural neh\Orl.. which produces 85%

accuracy

f

I], the classificntion of healthy infants and infants who experienced pain like brain damage, lip cleft palate, hydrocephalus, and sudden infant death syndrome by using Hidden Markov Model

(HMM)

which produces

91%

accuracy

[2].

Journal of Theoretic al and Applied Information Technology 20111 _{October 2013}

Yo!.

_{56 No.2}

c

2005 - 2013 JA TIT & LLS All r1ghts reserved·

ISSN ＱＹＹＲＭＸＶ Ｔｾ＠

ｵｾｩｮ･＠

a

neural ncl\\ori. '' hicl1 produces 86%

accurac)

13 ].

The classilication of' infont cri..:s <;tudics have prc,iously used a neur.il ｭＮｾｬ｜ＬｵｲｬＮＮ＠ or I l\.1M as the classifier. The research to ｩ､｣ｮｴｩｴｾ＠ the DBL infant cries used codebook as a clussilicr or pattern identifier \\hich is obtained from the k-means clustcring and MFCC as feature .:'\traction. The choice of the method is based on the high accuracy results from the researches, ｾｵ｣ｨ＠ as: automatic recognition of bird:.ongs using ｾｬｬ ﾷ ｃｃ＠ and Vector Quantization (VQ) codebook and produces 85%

accurac} [ .t ]. Besides, the research about speaker n.:cognition S)'Stem also ｳｵ｣｣｣ｾｦＧｵｬｬＩ＠ created b) using MFCC dan VQ {5). The same n.:search that Singh and Rajan (6) did, got 98.57°0 uccuracy by doing speaker recognition ｲ･ｾ｡ｲ｣ｨ＠ using MrCC and VQ. The research about spc.:ch n.:cogni1ion and verification using MFCC and VQ "hich Patel and Prasad (2013) did can do n:cognition '' ith training error rat..: about I 3°'o (7). I he mal..ing of this codehook u!ling k-mcans clustering.

This research aims to perform thc modeling of codebook me1hod \\ilh J..-mcans clustering technique, and Mel ｆｲ｣ｱｵ･ｭ Ｚ ｾ＠ Cepstrum Coellicient!> ＨｾＱｆｃＨＧＩ＠ to identi I)' 1hi: infant cries. I he scope of this research arc: the infant cries classification used is the ver:;ion ol' the Dunstan Dah) Language \\hich is di' ided into groups of hung!') bab}, tired 'sleep} bab). like burping bab) , infnnts e'\pcrience pain in the stomach, and uncomfortable bab} This '>nllmm.: is used 10

identit) the meaning of0-3 month old infant cries.

2. M AT EIUA L S AN D l\1 ETllOOS

The ｭ･ｴｨｯ､ｯｬｯｧｾ＠ of thi' rc!>Carch con<;i<;ls of SC\cral StJges of proccs:.: data collection. preprocessing. codebook modeling of infant cries. testing and analysis. and interface manufacturing. The methodology of the inf.mt cries meaning

itlemific::uion process is sho'' n in rigurc I .

2.1 Data Collection

The data used for this ｾｴｵ､｜＠ ts the infant cries that arc grouped into li\c ,;·1es of hungr)' bab). sleepy baby, like burping ｢｡｢ｾＮ＠ in pain bab) (in the stomach), and the uncomfort.ihl1.: bab). The data is taken from Dunstan Bab) Lmgu:igc 'ideos that has been processed.

The data is di"ided into t\\O, training data and testing data. There arc 140 training data, each of \\hich reprcsems the 28 hungr} infant cries, 28 sleep) infant cries, 28 "anted to burp infant cries, 28 in pain infant cries, and 28 uncomfortable infant cries (could be because ｨｩｾ＠ di:ipcr is wet/too

E-ISSN: 181H t95

hot/cold air or ｮｮｹｴｨｩｮｾ＠ else}. The testing data is 35, rcspccti,el> 7 infant cries for each type of infant cry.

Stan

Data collection

Preprocessing Silent cuuung

Feniure extmction· MFCC

Infant cries

Preprocessing' Silent cununa

Fcatun: cxttact1on. MFCC

lntcrfocc milking

finish

Figure I. 711e methodology of iden1ifyi11g the meaning of a crying baby

2.2 Preprocessing

Silent cutting is in the preprocessing stage and

the feature extraction uses MFCC method. The MFCC flow diagram can be seen in Figure 2.

2.3 Infant C rin Codebook M od eling

The codebook that is going to be made is the codebook of ench infant cry data. The codebool.. of clusters is m:ide from the proceeds of all the baby's cries data, by using I.he k-means clustering. Codebook is :i set of points (vectors) that represent the distribution of a particular sound from a speaker in a sound chamber. Each point of the codebook is known as a code\\ord. The recognition process m..:ans that in e"ery sound, the distance of the sound is always counted to each speaker codebook. 'I he distance of incoming sound signals to 11

speaker codebook is calculated as the sum of the distances of C<ich frame \\hich is read 10 the nearest codeword. Finally, input signal is labeled

as

[image:2.628.43.549.72.702.2]

Journal of Theorotical and Applied Information Technology 20"' Oc!obe! 2013. Vol 56 No.2

Q 2005 - 2013 JATIT & LLS. All rights reserved·

ISSN 1'192-lllM!t ----='=''=''=W=.j=m=it=o=rs::._ _ _ _ _ _ __ _ _ _ _ =E·..:.l::.SS::N:...::....:: ＱＺＮＮＺＸＮＮＺＮＮＱＷＮＮＺＮＮＮｊＮＮＺＮＮｉ ｟ ｾ ＮＮＮＺＮＮ＠

compilation result i!> ｣｡ｬｬｾ､＠ t.:n<lebook. I his codebook a' a speaker model

fKI.

speech signal

i

...

y

I· ramc hH

0 O., 01 ... , O, 01

..

\\ llldO\\jng

I', (11)

"'x,

(n)1r( 11), () · II \ ' -I

{ ' 7 1

ＬＮｾＬＬＩＭＰＮＵｊＭＰＴＶ｣＼＾＠ - - - }

,\'-1 o,.,,s ,,·-1

+

rFT

Xn =

L:;J

X"e _m!!

..

11 =

0, 1, 2, .. ... ,N - 1

•

Md frcquenC} \\rappm;:: md(I)"' 2595 · 1011,. (I + ｾＩ＠

M filter is obtained.then calculmc lhc i\ lcl spectrum

(N-1

)

x,

=

tou10

l:

IX(k)I · 11,<k) •

k•O

I= 1, 2, .... ,M

111 (k) is triangle filter' aluc to-1

•

Ccp-.trum Cocfic1ents· [)1,m:tc Cosme

Trnn,form

c

1 = ; .. ｾＢ＠ 1

x

,cos -

(''1-1 ")

₂- ; ; 1 1,2,3,. .. J,

J-number of c:ocffic1c111s, M number of filter

Figure 2· Diagram Alur .lfF('C

K-means clustering i!; a \\ell-known panitioning method. ｏ｢ｪ･｣ｴｾ＠ arc classified as belon&ing to one of k groups. k chosen a priori. Cluster membership is de1ermme<l b) calculating the centroid for each group (the muhi<limcnsional \ersion of the mean) and assigning each object 10 the group with closest centroid. !"his approach minimi£es the overall \\ithin-clustcr dispersion b) iterative reallocation of cluster mem bers.

The pseudo code of the k-mca11' algorithm is to explain hO\\ it \\Orks [91 :

..I Ch<>ose Kus the 11umher of .1m1 rs

8. lnitialb! the codebook

vea

ws of the A. clusters Ｈｲ｡ｮ､ｯｭｾｾﾷＮ＠ for instance)

C. For every• new sampll! veclor.

C. I Compl/fe 1he ､ｬｳｲｵｮ｣ｾ＠ belliee11 the new vector and every cluster 's codebook vector.

C 2 Recompute the closes/ codebook vector with the new \'ector. using a learning rate that decreases in time

The stages of codebook making for a speaker are as follows:

I At each pronunciation (n pronunciation

as

training data), feature extraction is performed b} using MFCC technique on each frame with a certain length and overlap.

2 All frames of n pronunciation are combined into one set and unsupervised clustering is done by using k-mc::ins clustering technique, choosing a number of clusters according to a number of the

desired codeword.

2.4 Ttstlng and Analysis

S1agc of testing means a test to identify the meaning of infant cries. The !low process of identilicatiotVrecognition phase are:

I Each new utterance which is go into the system is read frame by frame, (e.g. the number of frame:. obtained is T), and feature extraction using a MFCC technique is done.

2 Calculate the speech input distance signal to each speaker's codebook in the system.

3 Decision: assign a label on the sound input in accordance with the speak.er \\ith the smallest codebook distance.

Speech input distance \\ith codebook is formulah:<l

as

follows:

I For each frame of the incoming speech inpu1, the di:.tancc to every codeword is calculotcd and codeword \\ith the minimum distance is selected.

2 The distance between the input speech and the codebook is a number of minjmum distance (equation I ). discance(input, codebook)

=

:Ef.1vco11 .. ｷｯＧＺＭＧｊｾ｛､Ｈｦｲ｡ｭ･ＬＬ＠ codeword1c)J

( I)

Distance used in this research is Euclidean distance. Euclidean distance between i object is defined by equation 2 [10].

(2)

Journal of Theoretical and A p plied Information Tech n o log y ＲＰｾ＠ October 2013. Vol. 56 No.2

tl:l 2005 . 2013 JATlT & LLS. All rights reserved·

ISSN: 1992-36-45 \\W\UU!i\.urg £-ISSN: t817-.J 195

• Frame length. 25 ｭｾＯｬｲｯｭ｣＠ kneth - 275.

40 ms/frame kngth - 440.

60 ms/ frame length ｾ＠ 660. • O\erlop frame : 0°o. 25%, 40%.

• the number of co<lewords/numb..:r or clusters: to 18. except for frame length 275 and overlap ""' 0 using I to 29 clusters

The accuration vnlue of each combination of factors and levels "ill be calculated by using equation 3.

accuracy

a numbL'T of ci:scrng data with n1ht 1dcnt1/1catlon

00

= xi

a number of testrnq darn

(3)

2.5 Interface Making

The interface mal..ing uf th1. infant cries identificotion is made bused on the training dntn that produces the ｨｩｧｨ｣ｾｬ＠ accuracy.

3. RESULTS AND DI SCUSS IONS

[image:4.619.28.539.65.759.2]

The making of thi c; research ｩｾ＠ using Matlab R20 IOb 'crsion 7 .1 1.0.584 soil\\ arc. Result of accurac} comparison using cud idcan distance "ith testing data is sho\\ n in Figur..: 3. According to Figure 3 the higher :iccuroc) "hen the frame length

=

440. 01·erlap frame 0 .4. anJ k - 18 \\ith 94°·0

accurac)'. The same ;u.:curaC) can be got ｜ｾｨ･ｮ＠

frame length - 660, orerfap frame 0.25, and k

-14.

The Grapb or Accuracy V1lue Comptimon

I 2 J 4 S 6 7 t 9 10 11 12 13 I• 1$ 16 17 II

nulnMr or chuters (k) - frame length 27S overlap frame 0 - frame length 27S. overlap frame 0 2S

-rmme lcnath 275, overlap frame. 0.4 - frame lcnglh 440. ｯｾ･ｲｬ｡ｰ＠ frame: 0 - frame length 440, ｯｾ｣ｲｬｮｰ＠ frame: 0.25 - frame length 440, overlap frame 0.4 -rmme length 660. O\erlap frame 0 -frame length 660. O\trlap frame 0 2S - fnune len 660, O\Crla frame 04

F1gurtJ Tht graph of acc11racy mlue comparison usmg

testing dolt/

/able Z: Tes1111g 11.smg Testing Data when k-18,

Frame length -660, and overlap

=

0,2S

Data testing to- _Type of

I 2 3 4

s

6 7 cries

Result of tcs1ing using the data testing \\hen k

=

18. frame length .... 440 and oH::rlap frame .. 0.4

can be seen in Table I. Resull of testing using the data testing \\hen k 18. ｦｲ｡ｭｾ＠ length = 660 and O\.erlap fr.imc 0.25 i:. shown ml Jblc 2. Result of

testing using the dat:i testing ''hen k

=

18, frame length

=

275 and O\l:rlap frame .. 0.:?5 can be seen in Table 3.

•

'a' 'a' 'a' 'a' 'a' 'a' a-eairh

'c' 'e' 'e' 'c' 'c' 'c' 'c' c-eh

'h' 'h' 'h' 'h' 'h'

•

'h' h-heh 'n' 'n' 'n' 'n' 'n' 'n' 'n' n-neh

Table I: Te.mng usmg te.1t111g cl///11 when k- 18. 'o' 'o' o-owh

Frame le11g11t ·/.10, mul ol'erl.1p ｾ＠ 0.

Tobie J: Testing using Testing Data when k=/8.

Datn testing 1 o- Type of Frame length 660, and 01•er/ap • 0,25

2 3 4 5 6 7 cries Data sample 10- _{Type of}

'a' 'a' 'a' 'a' 'a' 'a' ·a· _{n-eairh 2 3 4 5 6 7} cries

'e' 'e' 'c' 'c' \:' \:' 'e' c-eh _{'a' 'a' 'a' 'a' 'a' 'a' 'a' a-eairh} 'h' 'h' 'h' 'h' 'h' 'h' h-heh _'c' •c• _'e' 'e' 'e' 'e' 'c' c-eh 'n' 'n' 'n' 'n' 'n' 'n' 'n' n-nch

ｾ

ｾ＠

0-0\\h

'h' 'h' 'h' 'h' 'h' 'h' h-heh

'n'

•

'n' n-neh 'h'

'o' 'o' 'o' o-owh

Journal of Theoretical and Applied Information Technology 20'" October 2013. Vol. 56 No.2

C> 2UU5 - 2013 JATIT & LLS All rights reserved

ISSN· 1992·8<1-45 \\'\\\\ .j3til org E-ISSN: 1817-319!1

I rom the result

or

testing \IC l..mm that i.ound 'ch' is the most fom1har (Table I, 1able2, Table 3).

Whereas sound '011h· is al11,1ys missunderstood and general!) it is kno11n ｡ｾ＠ ·ni:h' and 'cairh' (Table 2, Table 3 ). The ｭｩＡ＾ｴ｡ｨｾ＠ in iJcntification is caused b)' training data variation ·owh' is bigger

than the others. Codebook ilustration ·eh· and '01\h · when k-18, frame length-=4·IO, and overlap frame

=

0.4 is shown in Figure 4. 1 he figure show codebook distribution ·01\h' ｩｾ＠ bigger than 'eh'

codebook.

OB ,. ｾ＠

l\ I

Ｇｾ＠ It I

ｾｉｾ＠

o; , '111

't, ,,

\ _J

'•'

_I

j

41

ＮＬＬＭ ＮＮ］］ ］ｾＭｉ＠

,,-

-

-

.

ｾﾷ＠

ｾＭｾＭＭ

c; - '

•

ve

, -\

_{...__,.} \t

,, ,

II

.01 - ' ' IJ

\I II

,",','-'::

----

-\

｜ｾ＠

,-.."':..,' ｾ＠

'

-

'

,

.

'- - : --

--- --

:.

....

,

ＱＮｾＮ＠

.,

O• 01

The l ting co lparison \)f 11:,ting data and

trnining data C) use eucltJean distance "ith

fr:une leng1h "' 275 nnd O\'Crl.1p frame = 0 can be

seen in Figure 5.

Th• Cr•pll of AttllfA<) ｲｵｩ｡ｾ＠ I t1h

0••• ""'

ｦｲ｡ｭｩ｡ｾ＠ Hc .. ra" O.ata •hf'• fnu•t Lt1t1tlt ｾﾷ＠ ·• Q\ f l•e• • t

100".

-<)(/ . .

ｾ＠

SO".

-ＱｾＮ＠

ＶＰＢｾ＠ t1•mm1 dau

so-. -1l"itm1data

·IO"o

I l ｾ＠ 1 Q II ll

ﾷｾ＠

11 1·1 11 1 l.I 27 2'I

numbC'r or clu,ft•.--( 1')

F1g1ue 5: The graph o/uccum<1 1t1mg '<'fling data and

trammg data Ｑｾｨ･ｮｦｭＱＱＱ･＠ length : -j, overlap"' 0

The illustration about the c'.planation is displa)ed in Figure 6 and I 1gur.: 7. Codcbool. illustration of ·o,.,.h', ·neh", anti · 011h' testing datn

is di5playcd in ｆｩｧｵｲｾﾷ＠ 6. Cou.:tiook illustration of

'o\1h'. ·nch'. and ·owh' training data is displa}cd in Figure 7.

There arc 2 codebook in Figure 6 and Figure 7, that is codebook 'O\\ h' and ' neh'. When the testing

using 'owh' testing data, sound ·owh' is known

as

owh (illustration in Figure 6) but when 'owh' training data is tested, its known as 'nch' (illustration in Figure 7). It is because ·o,.,,h. training data (symbol • red coloured in Figure 7) is close to nch codebook (symbol o green coloured) (Figure 7). ll proves that evcthough 01.,.h training data has been modelled as 'owh' codebook, the testing still identify it as 'neh'.

H

°""'

...

0

0 0 0

146

'

..

°ti ., l.dJ f!J Jll f 0

.

,.. _{tSI ,)} 0

( c

"

_,

.

.

0 ₀

.

.

"' 0

-1'

.

Figure 6 Codebook 1/lus1ra11on of owh and neh, also 'owh ·testing data

0 Ｍｾｯ､ｯ｣ＺＮｯｯｬ＼＠

,,..._

•

Ｇｾ＠

_.,_

....

.

+

J

05

0

• o o •

. .

tb

11

•

! ·O.S [jl.ill+• •O•

•

. .

1111 c

'

•I

•

....

D _{ｾｃ ｯ＠} Co

..

...

# • 0 IS _{._.}

...

.

[image:5.628.17.546.134.720.2]

ｾＭＲ＠ _-u + + • ' "

.,

Figure 7: Codtbook 1llustral1on of owh and neh. also

'owll · lrammg data

\

Jou rnal of Thoorotical and Applied Information Technology 20"' October 2013 Vot 56 No.2

Q 2005 • 2013 JATIT & LLS. All rights reserved·

ISSN: ＱｾＲＮｳｾｳ＠

u

[image:6.626.20.533.49.768.2]

...

ｾ＠

Figure 8. 7he /111erfacc Of lde1111/1,a11011 Of lnfafll Cms

4. CONCLUSIO:\

Codebook model and \.II CC 11ith the higher accunicy is: frame length I 10, m crlap frame ... 0.4. k - 18. The dis1ancc usi1w \\ hich produce the higher <lccuraq is euclidean d1-,rnm:c. That model can produce accuracy recognition of infant cries \\ith the higher about 94%. 'I he rc:;carch is just cut the silent at the beginning anJ al the end of speech signal. I loJ)(!full), in 1he nc\t ｲ｣ｾ｡ｲ｣ ｨＮ＠ the silent can be cut in the middle of <,oumJ so 1hat it can produce more specific sound. II ha' impact on 1he bigger accurac)' as \\Cll.

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[I] Poe I M, Ekkcl I . Anal) 1ini; Int am Cries Using a Committee or Neural '.\ct\\\ll'kS in order to Det1..-ct I lypoxia Rdutc<l Disorder.

International Journal 011 I 1if'< wl l11telligence Tools (IJAll) \ ol. 15, 'l'. 3. 2006, pp. 397. 410.

[2J Lederman D, Zmora E, I luuschild t S, Stellzig· Eisenhauer A. \\'crmkc K. 2008. Cla!>sitication of cries 11f inJJnls \\ilh clefl-palaLc using parallel hidden f\ l,1rkov models.

/111ernationol Federatio11 Ii.Jr Hcdical and Biolo[!ical ﾣＱＱｾＱＱＱ＼＿･･ｲＱＱＱｧ＠ \ ol 16, 2008, pp.

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or

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E-ISSN: 1817.J l9S

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LBG

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(7) Patel K, Prasad RK. Speech Recognition and Verification Using MFCC & VQ

International Journal of Emerging Science and Engineering (JJESE) Vol. I, No. 7, 2013. pp. 33-37.

[8) Linde Y, Ouzo A, Gra.> RM. 1980. An Algorithm for Vector Quantizer Design.

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