With
the
development
of
society,
people
pay
more
attention
to
children's
brain
development.
Among
them,
electronic
music
is
an
important
part
optimizing
neural
networks,
so
classification
particularly
important,
ordinary
methods
can
not
solve
problem
in
and
accurate.
Therefore,
this
paper
proposes
a
machine
learning
algorithm
innovate
create
accurate
analysis.
First,
artificial
intelligence
used
analyze
content,
indicators
are
divided
according
requirements
reduce
interfering
factor.
The
influence
on
growth
education
has
gradually
attracted
attention.
However,
research
based
network
rare.
purpose
explore
how
use
classify
network,
as
improve
cognition
understanding
music.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Апрель 4, 2025
In
the
artificial
intelligence
(AI)
domain,
effectively
integrating
deep
learning
(DL)
technology
with
content,
teaching
methodologies,
and
processes
of
music
aesthetic
education
remains
a
subject
worthy
in-depth
exploration
discussion.
The
aim
is
to
meet
needs
students
across
different
age
groups
levels
musical
literacy.
this
paper,
concepts
AI
DL
algorithm
are
first
introduced,
their
principles
application
status
understood.
Then,
they
integrated
into
education,
running
codes
designed.
Finally,
experiments
carried
out
verify
accuracy
emotion
recognition
based
on
in
environment
effectiveness
method
DL.
results
show
that
proposed
paper
has
higher
accuracy,
which
combines
advantages
algorithm,
obtains
accuracy.
It
provides
more
possibilities
for
future
activities.
This
dedicated
investigating
feasibility
approach
optimizing
through
Its
objective
chart
new
developmental
direction
practical
pathway
era
AI.
PeerJ Computer Science,
Год журнала:
2025,
Номер
11, С. e2265 - e2265
Опубликована: Янв. 24, 2025
Artificial
intelligence
(AI)
in
music
improvisation
offers
promising
new
avenues
for
developing
human
creativity.
The
difficulty
of
writing
dynamic,
flexible
musical
compositions
real
time
is
discussed
this
article.
We
explore
using
reinforcement
learning
(RL)
techniques
to
create
more
interactive
and
responsive
creation
systems.
Here,
the
structures
train
an
RL
agent
navigate
complex
space
possibilities
provide
improvisations.
melodic
framework
input
data
initially
identified
bi-directional
gated
recurrent
units.
lyrical
concepts
such
as
notes,
chords,
rhythms
from
recognised
are
transformed
into
a
format
suitable
input.
deep
gradient-based
technique
used
research
formulates
reward
system
that
directs
compose
aesthetically
intriguing
harmonically
cohesive
improvised
further
rendered
MIDI
format.
Bach
Chorales
dataset
with
six
different
attributes
relevant
employed
implementing
present
research.
model
was
set
up
containerised
cloud
environment
controlled
smooth
load
distribution.
Five
parameters,
pitch
frequency
(PF),
standard
delay
(SPD),
average
distance
between
peaks
(ADP),
note
duration
gradient
(NDG)
class
(PCG),
leveraged
assess
quality
music.
proposed
obtains
+0.15
PF,
-0.43
SPD,
-0.07
ADP
0.0041
NDG,
which
better
value
than
other
methods.
IEEE Access,
Год журнала:
2024,
Номер
12, С. 110322 - 110330
Опубликована: Янв. 1, 2024
This
study
aims
to
explore
the
application
of
deep
learning
models
in
multi-track
music
generation
enhance
efficiency
and
quality
production.
Considering
limited
capability
traditional
methods
extracting
representing
audio
features,
a
model
based
on
Bidirectional
Encoder
Representations
from
Transformers
(BERT)
Transformer
network
is
proposed.
first
utilizes
BERT
encode
represent
data,
capturing
semantic
emotional
information
within
data.
Subsequently,
encoded
features
are
inputted
into
learn
temporal
relationships
structural
patterns
among
sequences,
thereby
generating
new
compositions.
The
performance
this
evaluated,
revealing
that
compared
other
algorithms,
proposed
achieves
an
accuracy
95.98%
prediction,
with
improvement
precision
by
4.77%.
Particularly,
demonstrates
significant
advantages
predicting
pitch
tracks.
Hence,
exhibits
excellent
offering
valuable
experimental
reference
for
research
practice
field
generation.
International Journal on Semantic Web and Information Systems,
Год журнала:
2024,
Номер
20(1), С. 1 - 19
Опубликована: Май 16, 2024
Music
generation
became
a
platform
for
creative
expression,
promoting
artistic
innovation,
personalized
experiences,
and
cultural
integration,
with
implications
education
industry
development.
But
generating
music
that
resonates
emotionally
is
challenge.
Therefore,
we
introduce
new
framework
called
the
Sequence-to-Music
Transformer
Framework
Generation.
This
employs
simple
encoder-decoder
to
model
by
transforming
its
fundamental
notes
into
sequence
of
discrete
tokens.
The
learns
generate
this
token
token.
encoder
extracts
melodic
features
music,
while
decoder
uses
these
extracted
sequence.
Generation
performed
in
an
auto-regressive
manner,
meaning
generates
tokens
based
on
previously
observed
are
integrated
through
cross-attention
layers,
process
concludes
when
“end”
generated.
experimental
results
achieve
state-of-the-art
performance
wide
range
datasets.
2022 4th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE),
Год журнала:
2024,
Номер
unknown, С. 1 - 6
Опубликована: Фев. 29, 2024
This
study
introduces
a
novel
approach
to
music
generation
using
Variational
Autoencoder
(VAE)
model,
which
incorporates
style
embeddings
for
enhanced
control
over
the
generated
music.
The
model
divides
latent
space
into
content
and
components,
allowing
users
specify
desired
musical
styles.
Experimentation
demonstrates
superior
performance
compared
traditional
methods,
with
effectively
capturing
stylistic
nuances
producing
diverse
compositions.
Methodologically,
VAE
employs
reparameterization
trick
μ-forcing
technique
ensure
effective
training
preservation
of
variables.
concludes
that
proposed
surpasses
baseline
offering
greater
flexibility
in
generating
tailored
specific
styles,
thereby
advancing
field
AI-driven
composition.
aim
work
is
develop
genre-specific
based
on
neural
networks.
Although
frequency
domain
analysis
theory
has
been
widely
used
in
many
signal
fields
and
some
practical
examples
have
reported,
the
traditional
automatic
test
systems
limited
ability,
thus
methods
not
for
engineering
practice.
Aiming
at
above
problems,
we
first
complete
function
requirement
system
to
integrate
into
practice,
then
determine
processing
tools
commonly
process.
After
that,
according
data
characteristics
of
all
kinds
signals
system,
structure
design
is
constructed.
Finally,
functional
principle
module
analyzed,
a
series
such
as
algorithm
compilation
are
designed.
The
ability
enhanced,
so
that
collected
can
be
processed
by
besides
driving
instrument
carry
out
test.
As
result,
tested
object
obtained,
which
provides
basis
fault
diagnosis
board-level
circuits.
