All right.
Well,
thanks,
everyone,
for
joining
us.
The
title
of
our
project
is
Design
a
Digital
Music
Melody
Hearing
Test.
I'm
Patrick
Giuliano,
and
my
co-
presenters
are
Charles
Chen
and
Mason
Chen
who
couldn't
be
here
today.
So
I'm
going
to
be
presenting
on
their
behalf.
And
this
is
a
project,
a
high-
school
STEM
project
inspired
by
ESTEEM's methodology,
which
is
basically
STEM
but
with
AI,
math,
and
statistics
well-
integrated.
Okay,
so
just
to
introduce
this
project
in
the
project
management
flavor
with
the
project
charter.
The
purpose
of
the
project,
in
effect,
is
to
design
a
test
to
test
the
hearing
capability
of
a
musician.
The
experimental
design,
philosophy
or
methodology
we
use
is
JMP's
powerful,
definitive
screening
design
capability.
And
we
designed
the
test
based
on
six
music
melody
variables
in
order
to
test
hearing
capability,
where
each
question
starts
with
a
short
melody
followed
by
four
choices,
and
where
only
one
is
repeated
and
the
other
three
melodies
are
similar
but
not
identical.
From
this
test,
each
listener
has
to
pick
their
best
choice
among
the
options
available.
Once
we
designed
this
test,
we
analyze
the
test
survey
results.
We
build
a
sensitivity
model
in
consideration
of
six
music
hearing
variables,
and
then
screen
the
listeners
to
determine
which
ones
performed
the
best
in
the
music
hearing
test.
And
in
doing
so,
in
the
screening
process,
we
analyze
the
strengths
and
weaknesses
of
their
hearing
capability
in
the
service
of
ultimately
creating
an
orchestra
with
a
grading
of
listeners
who
are
highly
capable
to
evaluate
them.
Okay, so
in
the
service
of
science,
we
have
an
introduction
to
the
mechanism
of
hearing
where
the
ear
is
just
basically
a
frequency-
receiving
apparatus
that
collects
sound
and
vibration
of
the
ossicles in
the
ear
and
cause
the
mechanical
vibration
to
be
converted
into
an
electrical
stimulus,
which
is
interpreted
by
the
brain
by
the
auditory
nerve
and
ultimately
by
the
brain.
All
right,
so
before
we
get
into
the
experiment
and
the
variables
that
we
analyzed,
let's
talk
a
little
bit
about
the
frequency
range
of
hearing
among
individuals
depending
on
their
age.
So
people
of
all
ages
without
hearing
impairment
should
be
able
to
hear
at
a
frequency
of
approximately
8,000
Hertz,
and
gradual
loss
of
sensitivity
to
higher
frequencies
with
age
is
a
normal
occurrence.
And
so
what
the
science
tells
us
is
that
the
auditory
structures
of
younger
people
are
typically
more
capable
of
absorbing
or
interpreting
hearing
higher
frequency
sounds,
which
is,
of
course,
relevant
in
terms
of
which
instruments
people
are
playing,
where
the
violin
has
a
higher
pitch
than
the
cello,
so
perhaps
a
younger
person
might
be
more
suited
for
playing
the
violin
than
an
older
person.
And
so
this
just
gives
you
an
idea
that
basically
people
that
are
in
their
fifties
maybe
may
only
be
able
to
hear
at
12,000
kilohertz...
or
12
kilohertz
rather,
12,000
Hertz,
whereas
people
in
their
20s
can
hear
up
to
perhaps
18
kilohertz.
And
just
to
give
some
context,
the
average
frequency
range
for
what
we
listen
for
the
sounds
that
we
hear
most
often
every
day
is
between
250
Hertz
and
6,000
Hertz.
Okay,
so
what
are
some
challenges
associated
with
hearing
in
the
context
of
sounds
of
different
frequency?
So
people
typically
miss
high
frequency
sounds
more
often
than
low
frequency
ones.
And
people
with
high
frequency
hearing
loss,
they
have
trouble
hearing
higher-
pitched
sounds,
of
course,
right?
And
so
higher
pitch
sounds
can
usually
come
from
women
or
children
and
are
in
the
upper
two
to
eight
kilohertz range.
And
what's
also
typical
with
high
frequency
hearing
loss
in
many
people
is
the
presence
of
a
phantom
sound,
which
is
the
condition
called
tinnitus,
and
that
competing
sensation
of
sound
can
also
inhibit
a
person's
ability
to
distinguish
other
high
frequency
sounds.
So
clearly,
age
is
an
important
factor
in
terms
of
designing
an
effective
hearing
test
and
developing
an
effective
panel
of
listeners
who
are
attuned
to
music.
Although
we
didn't
explicitly
consider
age
in
our
experiment,
as
you'll
see
in
the
subsequent
slides,
it
definitely
could
be
a
factor
that
we
could
explore
further
in
our
sampling
strategy
in
terms
of
the
survey
respondents
that
we
choose.
Okay,
so
the
basic
measure
of
hearing performance
is
called
an
audiogram.
And
what
you
see
in
the
graph
on
the
right
is
just
a
plot
of
hearing
threshold
level
in
decibels
on
the
vertical
axis
versus
frequency
on
the
horizontal.
And
you
can
clearly
see
that
as
hearing
loss
progresses,
the
threshold
level
of
sound
and
decibels
starts
to
increase
and
the
degradation
and
the
performance
is
shown
as
the
plot
splitting
the
performance
by
year
moving
down
into
the
right.
That's
the
trajectory
of
the
line
that's
connecting
the
points
moving
down
into
the
right.
Okay,
so
just
a
little
bit
more
background
before
we
launch
into
the
design
of
the
survey
and
the
analysis.
The
intent
here
is
just
to
emphasize
that
frequency
interference
can
be
a
problem
in
producing
a
melodious
harmony
in
an
orchestra
in
particular
or
in
any
sort
of
musical
composition.
And
what
we're
basically
showing
here
is
the
difference
between
what's
called
fundamental
frequencies
and
harmonics
in
the
context
of
a
piano,
at
least
at
the
note
scale
indicated
at
the
bottom.
Okay, so
what
do
we
know
about
the
music
note
frequency
spectrum?
Well,
each
note
has,
not
surprisingly,
based
on
the
introduction
so
far,
each
note
has
a
particular
frequency.
As
an
example,
middle
C
is
at
around
262
Hertz,
and
higher
notes,
of
course,
are
going
to
have
higher
frequency
and
lower
notes
have
lower
frequency.
And
this
slide
just
gives
you
a
context
for
what
frequency
the
notes
correspond
to.
So
note
A
is
around
much
higher,
440,
then
note
C at 261
in
the
second
set
on
the
right,
in
the
lower
portion
of
the
slide.
Okay,
so
there's
some
relationship
between
frequency
and
the
number
of
notes.
Frequency
needs
to
double
every
12
notes,
and
we
have
12 notes
in
each
octave,
seven
white
and
five
black.
And
so
you
can
see
that
this
relationship,
that
frequency
follows
as
a
function
of
these
notes,
and
n
is
a
power-
law
type
relationship.
All
right,
so
taking
us
back
now
to
the
project
and
the
implementation
and
the
analysis.
So
the
project
plan
has
three
phases.
The
first
phase
is
what
I'm
going
to
cover,
it's
the
analysis
that
I'm
going
to
discuss
today.
The
first
phase
is
effectively
the
process
of
identifying
which
people
are
best
hearing
performers
from
a
collection
of
survey
results
that
we
send
out
based
on
the
survey
that
we
designed.
The
second
phase
is
identifying
the
best
hearing
performers
from
the
survey
results
in
order
to
serve
as
judges.
In
this
phase,
basically,
we
try
to
work
on
forming
the
orchestra
prior
to
phase
three
where
we're
actually
doing
the
forming.
But
in
this
instance,
we're
thinking
about
things
like
which
instruments
have
any
potential
limitation.
And
we
may
give
the
same
melody
to
different
test
instruments,
and
not
every
instrument
can
play
every
melody,
obviously.
And
so
the
idea
is,
how
do
we
know
that
the
individuals
that
are
playing
are
playing
these
instruments
accurately?
Well,
we
need
judges
who
have
good
listening
capability.
So
the
judges
that
we
curate
from
phase
one
will
provide
that
excellent
evaluation
in
phase
two.
So once
we
have
that
in
place,
in
phase
three,
we
can
actually
really
form
the
digital
orchestra.
And
we'll
think
about
things
like
how
many
players
should
be
involved,
who
should
play
where,
obviously.
We'll
have
a
good
understanding
of
how
the
melodies
could
be
difficult
for
certain
instruments.
And
this
is
why
we
need
phase
two
in
the
middle.
Okay,
so
here's
our
design
or
survey
question
design.
So
we've
identified
six
variables
for
this hearing
test
related
to
music,
the parameters
in
music:
step,
speed,
notes
changed,
notes
level,
a
repeat
variable,
and
a
difficulty
variable,
a
categorical
variable,
easy
or
difficult.
The
experiment,
as
I
mentioned
before,
is
we're
using
JMP's
DSD,
and
in
addition
to
the
default,
we're
generating
a
default DSD,
and
then
in
effect,
we're
augmenting
the design
by
adding
two
more
center points.
So we're
doing
an
18-
run
DSD,
which
includes
one
center point,
which
is
row
number
three
in
this
table,
to have
indicated
with
a
zero
and
an
arrow
highlighting
row
three.
And
then
we're
adding
two
more
center
points
at
row
10 and
row
20
respectively.
And
the
idea
here
in
terms
of,
we're
replacing
these
center points,
is
we
want
to
get
an
idea
of
how
consistent
the
results
are
throughout
the
experiment.
So
we
try
to
put
a
center point
roughly
at
the
beginning,
in
the
middle,
and
the
end
of
the
experiment.
And
this
is
analogous
to
understanding
whether
a
measurement
process
is
stable,
if
you're
in
a
manufacturing
environment,
getting
a
sense
for
that.
And
then
the
other
important
thing
about
our
design
here
is
that
we're
randomizing
the
test
sequence,
and
that's
something
that
we
can
do
in
JMP
through
the
generation
of
the
design.
And
I'll
show
a
little
bit
about
that
briefly
when
we
come
to
the
next
few
slides.
And
that
randomization
is
really
important
because
it
helps
eliminate
any
bias
due
to
factors
that
aren't
in
the
experiment
when
we
run
the
test.
And
that
bias
is
referred
to
sometimes
as
lurking
variation
or
variation
due
to
lurking
variables.
Okay,
so
there's
another
consideration
that
I
touched
on.
It's
in
the
context
of
randomization,
but
it's
slightly
different
context,
which
is
a
little
bit
more
unique
to
this
particular
application
and
experiment.
And
so
basically,
what
we
did
is
generated an
initial
random
variable
and
assigned
a
random
sequence,
one, two, three, four,
and
randomized.
But
we
did
a
recoding
on
that.
So
we
labeled
one
A,
two
B,
three
C,
and
four D,
and
that'
s what
we
see
in
terms
of
identifying
the
correct
answer.
So
in
the
two
columns
at
the
right
in
this
table,
in
this
20-
row
table,
we're
identifying
what
the
correct
answer
should
be
in
terms
of
the
letter,
which
is
associated
with
a
random
variable
of
one, two, three, four,
w
here
one
corresponds
to
A,
two to
B,
three
to
C,
and
four
to
D.
And
we're
doing
this
to
ensure
a
uniform
distribution
of
the
location
variable.
And
basically
what
that
means
in
practical
terms
is
that
A, B, C,
and
D
all
have
equal
percentage
of
being
selected
at
random.
And
this
is
to
avoid
the
biasing
situation
where
a student
may
pick
the
same
answer
over
and
over
again
in
order
to
possibly
increase
his
or
her
chances
of
performing
well,
or
perhaps
because
the
survey
respondent
isn't
paying
attention
or
isn't
engaged
in
the
survey.
All right,
so
here
is
where
we
come
to
an
evaluation
of
the
performance
of
the
design,
of
the
DSD
design.
The
way
we
approach
this
is
through
the
evaluation
of
the
statistical
power
of
the
experiment
which
is
shown
on
the
left,
on the
panel
on
the
left
through
an
evaluation
of
the
confounding
pattern
or
the
extent
to
which
factors
are
correlated
in
the
experimental
design,
and that's
shown
in
the
panel
in
the
middle,
and
the
uniformity,
what
we
call
the
uniformity
of
the
design,
which
is
simply,
what
does
the
structure
of
the
design
look
like
in
a
multivariate
space?
Have
we
covered
all
of
the
design
points
in
an
approximately
uniform
way
so
that
we're
able
to
predict
across
the
entire
range
of
the
experiment
with
the
same
degree
of
precision?
And so what
these
three
indicate,
and
going
back
over
to
the
left,
is
that
the
overall
power
for
each
of
the
factors
in
the
experiment
is
greater
than
90 percent,
which
is
good.
And
it
shows
us
that
we
have
good
sensitivity
to
detect
effects,
if
they're
actually
there
in
the
population.
The
panel
in
the
middle
shows
that
the
risk
of
what
we
call
multicolinearity
or
excessive
correlation
among
the
experimental
factors
is
low
because
all
of
the
pairwise
correlations
in
this
correlation
matrix,
most
of
them
are
blue,
where
a
more
bluish
correlation
corresponds
to
a
lower
correlation,
where
solid
blue
indicates
zero
correlation.
And
the
squares
that
are
closer
to
a
red
shading
indicate
a
higher
extent
of
correlation
among
factors
or
terms
in
the
experiment.
And
so
overall,
what
we
see
is
that
we
look
for
correlations
that
don't
exceed
0.3,
and
that's
typically
all
the
squares
in
this
plot
with
the
exception
of
those
slightly
reddish
squares
where
the
correlation
is
a
little
bit
higher.
And
that's
because
we
have
categorical
factors,
right?
We
have
at
least
one
categorical
factor
in
this
experiment.
And
if
we
didn't
have
the
presence
of
a
categorical
factor,
this
plot
would
look
even
bluer.
So
we
say
that
in
DSD,
we
don't
recommend
adding
too
many
categorical
variables
into
the
experiment,
because
if
we
do,
then
we
increase
this
correlation
problem,
which
affects
our
ability
to
produce
estimates
in
our
model
that
are
precise,
leads
to
inflation
of
variance
in
our
estimates.
And
the
final
plot
on
the
right,
on
the
far
right,
which
is
an
indication
of
the
uniformity
of
this
design,
is
a
scatter
plot
matrix
in
JMP,
and
it
shows
each
variable
versus
every
other
variable.
And
what
we're
looking
for
is
for
white
space
to
be
minimal
in
this
plot.
What
I've
drawn
is
a
little
circle
here
which
your
eye
can
easily
pick
out.
There's
a
little
bit
of
extra
white
space
there
at
the
intersection
of
Repeat
and
Step.
And
that,
again,
is
because
we
have
a
categorical
variable
in
our
experiment.
And
so
truthfully,
there's
no
perfect
zero
in
the
main
effects,
no
true
center point in
the
main
effects
due
to
the
presence
of
that
difficulty
variable,
the
categorical
variable.
And
that's
reflected
in
the
non-
symmetric
pattern
of
the
scatter
plot
matrix
on
the
right,
slightly
non-symmetric,
where
that
asymmetry
is
indicated
in
that
white
space
and
with
the
circle
that
I've
drawn.
Okay,
so
before
I
discuss
this
slide,
I
just
want
to
quickly
show
you
how
I
got
to
these
design
diagnostics.
So
what
you're
seeing
here
is
the
table
that
I
just
showed
you.
And
I've
generated
this
design
using
the
DSD
platform
under
the
DOE
menu,
under
Definitive
Screening
and
Definitive
Screening
Design.
And
after
I
did
that,
JMP already
generate,
after
I
complete
the
design
table
generation
process
and
fill
in
the
results,
JMP generates
a DOE
dialogue
script,
saves
it
to
the
data
table,
and
I
can
actually
relaunch
the
DOE
dialog,
and
I
can
also
evaluate
the
design.
So
I'm
going
to
go
ahead
and
quickly
click
on
Design
Evaluation.
And
this
is
just
an
overview
of
the
design.
And
right
here
under
Design
Evaluation
is
where
I
get
the
diagnostics
related
to p
ower,
which
I
showed
you
on
the
left
panel
on
that
slide,
the
diagnostics
that
indicate
to
the
extent
to
which
factors
or terms
in
the
experiment
are
correlated.
And
that's
shown
here
in
the
color
map
on
the
correlation.
And
to
generate
the
plot,
looking
at
the
uniformity
among
the
factors,
I
actually
have
to
go
in
and
do
that
in s
catter
plot
matrix
under
the
Graph
menu,
S
catterplot
Matrix.
So
that's
just
some
context
for
you.
And
now,
I'm
just
going
to
quickly
bring
up
the
next
slide
and
then
come
back
to
JMP
here
just
to
dynamically
show
you
what
we're
doing.
So
here's
probably
the
most
interesting
part
of
this
experiment.
How
do
we
increase
the
survey
test
difficulty
and
do
it
in
a
smart
way?
Well,
we
can
use
hierarchical
clustering
analysis
to
do
that.
Now,
we
already
know
the
correct
answer.
It's
indicated
here
in
the
corresponding
column.
The
Choice
column,
the
columns
of
the
four
variables
on
the
right
which
indicate
the
choices
corresponding
to
the
20
melody
choices
are
indicated
there.
So
we
know,
for
example,
in
the
first
row,
the
correct
answer
is C
corresponds
to
melody one
where
the
C ID
number
is
one.
So
we
already
know
the
correct
answer
where
we've
assigned
it
in
terms
of
row
order
based
on
a
random
number,
but
how
do
we
pick
the
other
three
answers?
Well,
based
on
hierarchical
clustering,
we
can
get
a
sense
of
how
close
each
of
the
other
three
answers
are
to
the
correct
answer.
And
in
this
way,
we
can
make
the
test
a
little
more
difficult.
So
all
the
answer
choices
are
from
the
20
melodies.
How
do
we
pick
the
closer
formalities
for
each
question,
or
the
closest
formalities,
if
you
will,
or
even
maybe
melodies
that
are
relatively
close
together
based
on
the
clustering
criterion,
but
not
honoring
that
criterion
strictly,
right?
So
this
might
seem
a
little
bit
nebulous,
but
in
effect,
all
we're
really
doing
is
telling
JMP to
assign
a
clustering
scheme
by
row
and
based
on
some
clustering
criterion
that
we
specify.
And
by
default,
that
criterion
is
Ward.
So
I'm
just
going
to
show
that
dynamically
here.
So
I
have
the
table
open.
All
I
did
here
is
run
Hierarchical C
luster
under
the C
lustering
menu.
And
once
I
ran
this,
I
went
ahead
and
invoke
Cluster S
ummaries,
which
I
turned
on
here.
And
then
watch
what
happens
here
when
I
click
on
each
of
these
clusters.
So
you
can
see
that
when
I
click
on
each
of
these
clusters.
These
are
the
clusters.
So
seven and
18
are
associated
with
each
othe
r,
14
and
17, eight
and
nine,
row
two
and
13,
and
so
on.
So
this
is
the
idea.
We're
using
the
power
of
JMP
to
identify
rows
that
are
associated
with
each
other.
And
in
this
way,
by
arranging
the
answer
choices
close
to
each
other,
we
make
it
relatively
close
to
each
other
by
following
some
schema
like
this,
we
make
the
test
more
difficult.
All right,
so
just
launch
back
into
slides
here
again.
Okay.
All
right,
so
basically
the
last
step
here
in
terms
of
completing
this
experiment
is
in
addition
to
using
a
passive
criteria
for
increasing
the
difficulty
of
the
test,
we
want
an
active
criteria.
So
we
want
to
be
able
to
separate,
in
effect,
the
beginner
level
from
the
advanced
level.
So
think
of
it
like
this.
If
every
question
was
super
difficult
or
if
all
the
choices
were
very
hard
to
discriminate
from,
then
you
wouldn't
be
able
to
distinguish
between
an
advanced-
level
respondent
and
a
beginner-
level
respondent
because
everybody
would
miss
all
of
the
questions.
Similarly,
if
you
made
all
the
questions
too
easy,
then
you'd
have
all
experts
and
no
beginners,
and
so
you
have
no
differentiation.
So
based
on
the
science,
we
have
a
hypothesis
that
step
and
speed
are
the
most
important
factors
for
performance,
for
hearing
performance,
for
discriminating
between
a
good
melody,
a
good
composition,
and
a
bad
musical
composition
and
a
bad
one.
So
are
we
sure
about
that?
Well,
one
thing
we
can
do
is
we
can re
code
the
step
and
speed
by
a
50 percent reduction
if
it's
at difficulty
level
equal
to
difficult.
And
by
doing
that,
in
effect,
we
still
have
five
variables
and
those
are
indicated
in
the
shaded,
right?
So
the
recoded
step,
recoded
speed
are
the
two
columns
that
are
shaded.
And
then
we
have
the
notes
changed,
the
notes
level,
and
the
repeat.
So
the
DSD
is
still
orthogonal.
We
still
have
three
levels.
We
have
five
variables,
but
actually
we
could
incorporate
up
to
six
in
the
DSD
design.
So
how
do
we
increase
our
value
in
effect
by
increasing
that
variable
number
to
six?
Well,
we
can
add
the
difficulty
variable
or
the
categorical
variable
which
indicates
either
easy
or
difficult.
So
we
decided
to
use
step
and
speed,
combined with
these
other
three
variables,
and
the
total
sample
size
is
still
18
plus
2
or
20
with
the
two
center
points
and
the
one
center point
by
default.
But
now,
we
get
five
levels
for
speed
and
step,
not
three.
So by
doing
this
little
transformation,
we
smartly
create
five
levels
on
two
variables
instead
of
just
having
three
levels,
which
is
typically
what
we
would
have
in
a
DSD.
So
I
think
this
is
a
unique
approach
that's
also
quite
specific
to
this
problem
context
and
gives
us
more
levels
in
our
design.
Okay, so
this
is
our
design.
How
are
we
going
to
create the...
What
software
are
we
going
to
use
to
basically
generate
the
hearing
tests?
Okay,
well,
this
is
just
an
overview
of
Music S
oftware S
ynthesizer,
which
is
what
we
use,
soft
synth.
And
we
utilize
it
to
create
24
multiple
choice
music
melody
hearing
tests.
It's
obviously
convenient
and
portable
and
fast.
All
right,
so
how
do
we
distribute
this
survey
smartly?
Well,
our
approach
is...
Many
people
do
one
sampling
method.
But
here,
our
approach
is
to
integrate
all
the
different
sampling
methodologies,
cluster
sampling,
stratified,
and
some
additional
clustering
within
in
order
to
distribute
the
survey
to
the
right
audience
to
make
the
survey
the
most
useful.
So
when
you're
ready
to
send
out
the
quiz,
how
do
you
do
it?
Well,
I have
some
examples
here.
Who
should
play
the
music?
Well, there
are
people
who
know
the
music
and
people
who
don't.
So
we
only
want
to
send
the
surveys
to
people
who
are
already
familiar
with
the
music,
right?
Because
ultimately,
we
want
to
use
these
people
to
evaluate
the
performance
of
an
orchestra.
In the
stratified
sampling
sense,
we
have
different
kinds
of
instruments.
We
may
have
five
students
in
a
particular
pool
that
know
how
to
play
piano,
we
may
have
two
that
know
how
to
play
violin,
and
we
may
want
to
sample
smartly
so
that
we
only
pick
a
certain
number
within
each
strata
of
players,
people
who
play
particular
instruments.
So
we
may
pick
randomly
within
each
of
these
strata
in
a
certain
sampling
rate.
And
again,
with
respect
to
clustering,
we
can
think
of
location
in
terms
of
practice
location
or
geography
as
a
selection
from
many
different
geographies.
In
a
sense,
we
cluster
and
limit
our
selection
criteria
to
only
the
San
Francisco
Bay
area,
because
practicing
in
person
is
much
easier
than
practicing
virtually.
Okay,
so
really
the
point
is
that
this
survey
dissemination
and
survey
data
collection
processes
is
very
holistic
and
increases
our
chances
of
producing
an
effective
test
set,
if
you
will,
of
evaluators
to
help
us
form
the
most
high-
performing
orchestra.
Okay, so
quickly,
to
wrap
everything
up,
we
studied
the
human
hearing
frequency
range,
the
instrument
frequency
spectrum,
the
music
frequency
formula,
and
we
designed
an
innovative
music
melody
hearing
test
using
DSD.
We
also
implemented
two
interesting
approaches
to
increase
the
difficulty
of
the
test,
hierarchical
clustering,
as
well
as
rescaling
the
levels
of
the
most
important
predictors
on
our
responses
for
the
test
answers.
And
we
use
the
music
synthesizer
software
to
basically
disseminate
the
hearing
test
across
the
six
music
melody
variables.
And
in
our
strategy
for
dissemination,
we
use
the
holistic
sampling
methodology.
So
this
in
closing,
some
of
the
approaches
that
we
use
and
the
science
that
we
developed
could
be
used
to
develop
a
hearing
aid,
a music
melody
hearing
aid.
And
in
our
current
market
that
we're
aware
of,
hearing
aids are
really
specially
designed
for
people
with
hearing
loss,
but
the
idea
here
would
be,
how
about
making
a
hearing
aid
that's
about
amplifying
a
certain
signal
from
noise,
right?
And
that
would,
in
effect,
increase
music
melody
hearing
and
detection,
right?
And so the
main
objective
here
would
be
to
block
out
noise
that's
extraneous,
for
example,
noise
from
the
audience,
and
then
amplify
the
signal
portion
for
the
particular
frequencies
that
are
important
for
playing
a
particular
instrument,
or
even
using
this
type
of
technology
to
even
out
the
pitch,
to
amplify
the
transition
between
melodies.
And
so
in
future
work,
a
similar
DSD
design
can
be
implemented
in
terms of
developing
this
kind
of
technology.
So
thank
you
very
much
for
listening
and
let
us
know
if
you
have
any
questions.
