Web-based
technologies
have
seen
rapid
technologi-cal
improvements
over
the
past
several
decades.
This
is
especially
true
as
it
relates
to
audio
applications,
with
specification
of
WebAudio
API
enabling
users
deploy
highly
performant
projects
web.
Existing
literature
describes
how
mappings
are
a
critical
component
end-to-end
projects,
including
Digital
Musical
Instruments,
Internet
Sounds
devices,
and
more.
Due
this,
years
research
efforts
produced
mapping
middleware
for
facilitation
establishing
between
sources
destinations.
paper
discusses
libmapper
[?]
ecosystem
extended
support
from
sandboxed
browser
environment.
Establishing
connectivity
on
web
achieved
through
websockets
backend
daemon.
In
this
paper,
we
discuss
implementation
details
binding
well
potential
use-cases
via
user-story
driven
scenarios.
SoundSignature
is
a
music
application
that
integrates
custom
OpenAI
Assistant
to
analyze
users'
favorite
songs.
The
system
incorporates
state-of-the-art
Music
Information
Retrieval
(MIR)
Python
packages
combine
extracted
acoustic/musical
features
with
the
assistant's
extensive
knowledge
of
artists
and
bands.
Capitalizing
on
this
combined
knowledge,
leverages
semantic
audio
principles
from
emerging
Internet
Sounds
(IoS)
ecosystem,
integrating
MIR
AI
provide
users
personalized
insights
into
acoustic
properties
their
music,
akin
musical
preference
personality
report.
Users
can
then
interact
chatbot
explore
deeper
inquiries
about
analyses
performed
how
they
relate
taste.
This
interactivity
transforms
application,
acting
not
only
as
an
informative
resource
familiar
and/or
songs,
but
also
educational
platform
enables
deepen
understanding
features,
theory,
commonly
used
in
signal
processing,
behind
music.
Beyond
general
usability,
several
well-established
open-source
musician-specific
tools,
such
chord
recognition
algorithm
(CREMA),
source
separation
(DEMUCS),
audio-to-MIDI
converter
(basic-pitch).
These
allow
without
coding
skills
access
advanced,
processing
algorithms
simply
by
interacting
(e.g.,
you
give
me
stems
song?).
In
paper,
we
highlight
application's
innovative
potential,
present
findings
pilot
user
study
evaluates
its
efficacy
usability.
Neural
Vocoders
convert
time-frequency
representations,
such
as
mel-spectrograms,
into
corresponding
time
representations.
are
essential
for
generative
applications
in
audio
(e.g.
text-to-speech
and
text-to-audio).
This
paper
presents
a
scalable
vocoder
architecture
small-footprint
edge
devices,
inspired
by
Vocos
adapted
with
XiNets
PhiNets.
We
test
the
developed
model
capabilities
qualitatively
quantitatively
on
single-speaker
multi-speaker
datasets
benchmark
inference
speed
memory
consumption
four
microcontrollers.
Additionally,
we
study
power
an
ARM
Cortex-M7-powered
board.
Our
results
demonstrate
feasibility
of
deploying
neural
vocoders
resource-constrained
potentially
enabling
new
Internet
Sounds
(IoS)
Embedded
Audio
scenarios.
best-performing
achieves
MOS
score
3.95/5
while
utilizing
1.5MiB
FLASH
517KiB
RAM
consuming
252
mW
1s
clip
inference.
This
paper
provides
an
overview
of
the
novel
metadata-assisted
spatial
audio
(MASA)
format
which
is
one
supported
input
formats
in
3GPP
IVAS
codec.
MASA
consists
a
transport
signal
with
or
two
channels
and
parametric
metadata
describing
dominant
directional
sound
diffuseness
coherence
properties
scene.
While
mainly
intended
for
acquisition
from
mobile
devices,
this
describes
way
to
determine
parameters
Ambisonics
as
example
using
widely-available
source
format.
Additionally,
file
ingested
by
codec
described.
These
descriptions
are
provided
software
tools,
consisting
C-Ianguage
implementation
described
analysis,
Python-language
library
reading
writing
files.
Uluslararası Anadolu Sosyal Bilimler Dergisi,
Journal Year:
2023,
Volume and Issue:
7(3), P. 752 - 773
Published: Sept. 19, 2023
The
incorporation
of
artificial
intelligence
and
machine
learning
into
intelligent
music
applications
presents
fresh
avenues
for
musical
expression.
These
allow
the
production
emotionally
responsive
pieces
by
analysing
interpreting
emotions
conveyed
within
music.
Furthermore,
they
aid
collaborative
music-making
connecting
musicians
in
diverse
locations
enabling
real-time
collaboration
via
cloud-based
platforms.
objective
this
research
is
to
present
information
regarding
production,
distribution,
consumption
music,
which
has
a
close
association
with
technology.
Through
document
analysis,
prospective
advantages
incorporating
industry
are
assessed
from
vantage
points,
potential
models
areas
application.
It
also
proposes
further
enhance
algorithms,
guaranteeing
their
responsible
ethical
use,
unlocking
new
innovation.
For
decades
music
has
been
used
successfully
in
sports
and
especially
physical
rehabilitation
order
to
motivate
people
increase
satisfaction
the
actual
workout.
The
novel
principle
of
"Music
Feedback
Exercise
(MFE)"
allows
individually
influence
according
dynamics
a
person
generates
—
more
movement
leads
vice
versa.
Research
by
Max
Planck
Institute
for
Human
Cognitive
Brain
Sciences
gives
evidence
positive
arousal
effects
on
persons.
In
cooperation
with
Anhalt
University
Applied
technical
framework
developed
that
is
able
receive
analyze
respective
sensor
data
create
musical
output
generated
intensity.
This
paper
documents
under
which
assumptions
how
MFE
implemented
into
Soundjack
(also
known
as
fast-music)
core
technology
take
advantage
versatile
GUI
options,
low-latency
audio
streaming
also
networking
features.
In
this
paper,
we
present
a
light-weight
deep
learning
based
system
for
acoustic
scene
classification
(ASC),
which
is
armed
to
be
integrated
into
an
Internet
of
Sound
(IoS)
with
limitation
hardware
resource.
To
achieve
the
ASC
model,
develop
teacher-student
scheme
two-phase
training
strategy.
first
phase
(Phase
I),
Teacher
network
architecture,
shows
large
model
footprint,
proposed.
After
Teacher,
embeddings,
are
feature
map
teacher,
extracted.
second
II),
propose
Students
presents
architectures.
We
train
leveraging
embeddings
extracted
from
Teacher.
further
improve
accuracy
performance,
apply
ensemble
multiple
spectrograms
on
both
and
Students.
Our
experiments
conducted
DCASE
2023
Task
1
dataset
ten
target
classes
('Airport',
'Bus',
'Metro',
'Metro
station',
'Park',
'Public
square',
'Shopping
mall',
'Street
pedestrian',
traffic',
'Tram')
helps
best
Student
performance
57.4%
Development
set
55.6%
blind
Evaluation
set,
baseline
by
14.5%
10.8%,
respectively.
The
also
achieves
82.3%
three
('Indoor',
'Outdoor',
'Transportation')
88.7
KB
memory
occupation
29.27
M
MACs,
potential
wide
range
edge
devices.
This
paper
presents
a
comprehensive
study
of
automatic
performer
identification
in
expressive
piano
performances
using
convolutional
neural
networks
(CNNs)
and
features.
Our
work
addresses
the
challenging
multi-class
classification
task
identifying
virtuoso
pianists,
which
has
substantial
implications
for
building
dynamic
musical
instruments
with
intelligence
smart
systems.
Incorporating
recent
advancements,
we
leveraged
large-scale
performance
datasets
deep
learning
techniques.
We
refined
scores
by
expanding
repetitions
ornaments
more
accurate
feature
extraction.
demonstrated
capability
one-dimensional
CNNs
pianists
based
on
features
analyzed
impact
input
sequence
lengths
different
The
proposed
model
outperforms
baseline,
achieving
85.3%
accuracy
6-way
task.
dataset
proved
apt
training
robust
pianist
identifier,
making
contribution
to
field
identification.
codes
have
been
released
at
https://github.com/BetsyTang/PID-CNN.
This
short
paper
reports
on
recent
advances
in
the
development
of
an
open-source
prototyping
platform
for
creation
distributed
and
embedded
musical
systems
based
Web
technologies.
The
proposed
architecture
is
user-grade
hardware
software
aims
at
fostering
rapid-prototyping
experimentation
context
colocated
research,
performance.
After
a
review
related
works,
describes
general
design
different
building
blocks
system.
Then,
it
exposes
first
characterization
design,
concludes
with
description
prototype
that
highlights
features
platform.
