The Complementary Role of Artificial Intelligence to Traditional Teaching Methods in Music Education and Its Educational Effectiveness
Applied Mathematics and Nonlinear Sciences,
Год журнала:
2025,
Номер
10(1)
Опубликована: Янв. 1, 2025
Abstract
In
the
field
of
music
education,
application
artificial
intelligence
technology
is
gradually
changing
traditional
teaching
mode,
providing
new
opportunities
and
challenges
for
education.
this
paper,
we
use
to
build
a
smart
classroom
combine
it
with
user-based
collaborative
filtering
recommendation
algorithm
provide
students
personalized
learning
materials.
Moreover,
treble
feature
extraction
model
integrated
into
classroom,
DTW
improvement
used
match
students’
features,
student’s
mastery
skills
in
evaluated
through
sight-singing
scoring
technology.
Students’
overall
satisfaction
ratings
mode
designed
paper
were
4.35
4.60,
only
very
few
disliked
mode.
The
personalised
system
built
has
precision
rate,
recall
rate
F-value
0.50,
0.41
0.38,
respectively,
when
number
recommendations
50,
can
materials
suitable
them.
After
experiment,
average
scores
experimental
class
on
pitch,
rhythm,
sight-reading
ability,
notation,
polyphonic
perception
increased
by
7.72,
6.37,
7.82,
6.92,
8.16
points,
compared
control
class.
difference
between
intelligent
teacher’s
“pitch”
0.036~4.903.
Artificial
provides
an
effective
supplement
improves
personalization,
efficiency,
quality
teaching.
Язык: Английский
EEG-Based Music Emotion Prediction Using Supervised Feature Extraction for MIDI Generation
Sensors,
Год журнала:
2025,
Номер
25(5), С. 1471 - 1471
Опубликована: Фев. 27, 2025
Advancements
in
music
emotion
prediction
are
driving
AI-driven
algorithmic
composition,
enabling
the
generation
of
complex
melodies.
However,
bridging
neural
and
auditory
domains
remains
challenging
due
to
semantic
gap
between
brain-derived
low-level
features
high-level
musical
concepts,
making
alignment
computationally
demanding.
This
study
proposes
a
deep
learning
framework
for
generating
MIDI
sequences
aligned
with
labeled
predictions
through
supervised
feature
extraction
from
domains.
EEGNet
is
employed
process
data,
while
an
autoencoder-based
piano
algorithm
handles
data.
To
address
modality
heterogeneity,
Centered
Kernel
Alignment
incorporated
enhance
separation
emotional
states.
Furthermore,
regression
applied
reduce
intra-subject
variability
extracted
Electroencephalography
(EEG)
patterns,
followed
by
clustering
latent
representations
into
denser
partitions
improve
reconstruction
quality.
Using
metrics,
evaluation
on
real-world
data
shows
that
proposed
approach
improves
classification
(namely,
arousal
valence)
system’s
ability
produce
better
preserve
temporal
alignment,
tonal
consistency,
structural
integrity.
Subject-specific
analysis
reveals
subjects
stronger
imagery
paradigms
produced
higher-quality
outputs,
as
their
patterns
more
closely
training
In
contrast,
weaker
performance
exhibited
were
less
consistent.
Язык: Английский
Efficiency of AI Technology Application in Music Education - A Perspective Based on Deep Learning Model DLMM
Applied Mathematics and Nonlinear Sciences,
Год журнала:
2025,
Номер
10(1)
Опубликована: Янв. 1, 2025
Abstract
In
recent
years,
the
active
attempts
and
breakthroughs
of
artificial
intelligence
in
music
applications
education
have
been
amazing.
The
study
proposes
a
lightweight
score
recognition
method,
CRNN-lite,
which
achieves
both
improved
accuracy.
order
that
method
can
be
better
faster
migrated
to
applied
education,
article
designs
new
multimodal
domain
adaptation
algorithm
based
on
differential
learning,
effectively
utilizes
variability
different
modal
models
for
adaptation.
Finally,
performance
comparison
analysis
practical
application
effects
proposed
this
paper
are
discussed.
Comprehensive
experiments
show
DLMM
learning
achieve
results
than
other
methods,
compared
with
original
CRNN-Lite,
CRNN-Lite+DLMM
precision
rises
by
2.9%,
recall
rate
1.1%,
mAP@0.5
increased
1.3%.
Язык: Английский
Construction of Western Music Theory Teaching Model Based on Machine Learning
Applied Mathematics and Nonlinear Sciences,
Год журнала:
2025,
Номер
10(1)
Опубликована: Янв. 1, 2025
Abstract
Western
wind
music
is
an
important
content
for
majors
to
learn,
through
learning
western
can
improve
students’
expressive
ability,
enrich
content,
and
let
students
get
a
higher
level
of
development.
The
research
based
on
machine
mine
process
the
theory
teaching
data
performance,
13
feature
values
are
obtained
after
processing
features,
correlation
analysis
carried
out
by
collecting
related
594
majoring
in
university,
student’s
one
card,
library
behavioral
data,
then
logistic
regression
method
applied
obtain
coefficients
features
analyze
results.
characteristics
with
strong
were
analyzed.
study
shows
that
number
book
borrowing
significantly
correlated
average
course
grade,
followed
coefficient
results
show
weight
value
independent
ability
0.47,
which
characteristic
highest
force.
Based
blended
build
model,
helps
provide
better
personalized
services
effectiveness
quality
learning.
Язык: Английский