Automatic Infant Cry Analysis

yakzephyrΤεχνίτη Νοημοσύνη και Ρομποτική

24 Νοε 2013 (πριν από 4 χρόνια και 1 μήνα)

80 εμφανίσεις

20
10

UNSW

ENGINEERING @ UNSW


1.
Introduction

Crying

is

the

only

mode

for

infants

to

express

their

physical

and

psychological

status
.

For

many

years,

paediatricians

have

been

searching

for

non
-
invasive

tools

to

measure

brain

function

of

infants
;

m
eanwhile

there

is

growing

evidence

that

infants

with

medical

complications

have

identifiable

cries
.

Hence,

cry

signals

may

carry

informative

features

to

reflect

medical

status

of

an

infant
.

Moreover,

cry

analysis

can

be

automated

with

the

advent

digital

signal

processing
.

Being

cheap,

easy

to

perform

and

completely

non
-
invasive,

automatic

acoustic

analysis

on

infant

crying

is

a

potential

prognostic

or

diagnostic

tool

for

certain

pathologies

in

the

future
.

10

2. Aim

To

implement

an

automatic

cry

recogniser

which

classifies

cries

of

normal

and

pathological

(
asphyxiated

and

hearing
-
impaired
)

infants

by

extracting

relevant

acoustic

features
.

3.
Background

3
.
1
.

The

Infant

Cry

Production

Model

The

infant

cry

production

mechanism

can

be

described

using

the

physio
-
acoustic

model

which

consists

of

(
Figure

1
)
:


the

infant

control

organiser



the

independent

source
-
filter

model


The

infant

control

organiser

is

a

three
-
level

processing
:


Upper

processor

is

the

Central

Nervous

System

(CNS)

which

determines

the

states

of

action
.


Middle

processors

represent

all

vegetative

states,

e
.
g
.

crying
.


Lower

processors

coordinate

different

groups

of

muscles
.

5. Simulation Results




Average accuracy of classification is
94.1%
.

4. Methodology and
Experiments

Implementation of automatic classification involves
two phases
.

Figure 3:
Automatic
i
nfant
c
ry
r
ecogniser

Class

Normal

Asphyxia

Hearing Impairment

Accuracy

84.2%

92.3%

98.1%

Figure
1:

Physio
-
acoustic
infant cry
p
roduction
m
odel

Figure
4:
Process of
decision
m
aking

4
.
3
.

Leave
-
One
-
Out

Training

and

Testing

Training

samples

are

randomly

selected

from

16

(out

of

17
)

infants
.

The

samples

of

the

remaining

infant

are

used

for

accuracy

testing
.

Tests

are

repeated

by

removing

a

different

infant

each

time
.


4.4. Model Training

Models are trained using Gaussian mixture modelling (GMM).


4.5. Decision Making

Classifications

are

made

using

maximum

likelihood

criterion
.

4.1. Database

A

set

of

cry

recordings

from

5

n
ormal
,

6

asphyxiated

and


6

hearing
-
impaired

infants

has

been

recorded

and

labelled

by

paediatricians
.


4
.
2
.

Feature

Extraction


Fundamental

frequency

f
0


f
0

or pitch is the quasi
-
periodic
vibration rate of vocal folds in
the larynx.




Formants

F
i


Formants represents resonant
frequencies of the vocal tract.




Spectral

Centroids

SC
i


A spectral centroid indicates

the dominant frequency of a
given frequency sub
-
band and
is calculated as the average
frequency weighted by
amplitudes:

f
0

= 1 /
T
0

F
1

F
2

F
3

SC
2

CNS

controls

sub
-
glottal

(
respiratory

system),

glottal

(
larynx)

and

supra
-
glottal

(nasal

and

vocal

tracts)

independently
.

It

is

assumed

that

pathologies

will

affect

the

functionality

of

CNS
.

Consequently,

any

malfunction

in

either

group

of

muscles

is

directly

reflected

in

the

cry

sound

produced
.

Therefore,

acoustic

anomalies

can

be

correlated

to

physiological

pathologies
.


3
.
2
.

Infant

Vocalisation

Modes

Infant cry signals present
both voiced and unvoiced
structures:


Voiced cry

when vocal
folds are vibrating.

o
Phonation

vibration rate < 700 Hz

o
Hyper
-
phonation

vibration rate > 700 Hz



Unvoiced cry when vocal

folds are inactive.

o
Dys
-
phonation

Hyper
-
phonation

Dys
-
phonation

Phonation

Phonation

Phonation

Dys
-
phonation

Figure
2:
The three basic infant cry modes

Automatic Infant Cry Analysis

~ An Acoustic Approach ~

Author

: Voon Hian Lee

Supervisor

: Dr. Hadis M. Nosratighods

Student ID

:
3195964

Assessor

: Dr. Julien Epps

6.
Conclusion and Future Work

Average

accuracy

attained

is

94
.
1
%
,

results

show

that

automatic

classification

of

infant

cry

signals

via

acoustic

analysis

is

feasible
.

In

the

future
,

we

will

investigate

other

acoustic

features

to
:


identify

other

pathologies


determine

the

causes

of

crying

(e
.
g
.

pain,

hunger

and

discomfort)


ENGINEERING @ UNSW