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G. Bolaños Rodríguez,
"When Light Meets Sound: Signal Analysis of Black Holes",
Latin-American Journal of Computing (LAJC), vol. 12, no. 2, 2025.
When Light Meets
Sound: Signal Analysis of
Black Holes
ARTICLE HISTORY
Received 25 March 2025
Accepted 7 May 2025
Published 7 July 2025
Guillermo Bolaños Rodríguez
Arqcustic
Acoustic engineer
Quito, Ecuador
arqcustic@gmail.com
ORCID: 0009-0002-0182-6675
ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025
This work is licensed under a Creative Commons
Attribution-NonCommercial-ShareAlike 4.0 International License.
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When Light Meets Sound: Signal Analysis of Black
Holes
Cuando la Luz Encuentra el Sonido: Análisis de
Señales de Agujeros Negros
Guillermo Bolaños Rodríguez
Arqcustic
Acoustic engineer
Quito, Ecuador
arqcustic@gmail.com
Abstract—
When light meets sound, a new dimension of
analysis unfolds. This work explores black hole observations
through the lens of signal theory and acoustic wave mechanics,
revealing a resonant bridge between electromagnetic and
mechanical waves. Using Event Horizon Telescope EHT data, black
hole imagery is treated as a three-dimensional digital signal, where
the analytic Hilbert envelope and normalized Discrete Fourier
Transform DFT expose hidden structures.
The gravitational shadow is interpreted not as silence, but as a
measurable energy dip—an imprint of absorption rather than
absence. Euler’s identity is employed to map signal phase and
symmetry into polar and complex domains, providing an intuitive
mathematical pathway toward the event horizon.
By applying foundational acoustic concepts such as resonance,
interference, and entropy, the field surrounding the black hole is
reinterpreted as a complex communication signal. This
interdisciplinary framework unifies digital signal processing,
electromagnetic theory, and acoustics into a novel methodology for
astronomical analysis. Notably, when a full noise assessment is
conducted, EHT images exhibit a significant enhancement in
resolution and information transmission.
Keywords—
black hole imaging, digital signal processing,
Hilbert analytic envelope, frequency-domain analysis
Resumen—
Cuando la luz se encuentra con el sonido, emerge
una nueva dimensión de análisis. Este trabajo examina las
observaciones de agujeros negros a través de la teoría de señales y
la mecánica de ondas acústicas. Utilizando datos del Telescopio del
Horizonte de Sucesos EHT, las imágenes de agujeros negros se
tratan como señales digitales tridimensionales, donde la envolvente
analítica de Hilbert y la Transformada Discreta de Fourier (DFT,
sigla en inglés) normalizada revelan estructuras y simetrías ocultas.
La sombra gravitacional se interpreta no como silencio, sino
como una caída medible de energía—una huella de absorción en
lugar de una simple ausencia. La identidad de Euler se emplea para
mapear la fase y la simetría de la señal en planos polares y
complejos, ofreciendo un camino matemático intuitivo hacia el
horizonte de eventos.
Al aplicar conceptos acústicos fundamentales como la
resonancia, la interferencia y la entropía, el campo que rodea al
agujero negro se convierte en una señal comunicativa. Este enfoque
interdisciplinario unifica el procesamiento digital de señales, la
teoría electromagnética y la acústica en una metodología innovadora
para el análisis astronómico. Cabe destacar que, al realizar una
evaluación completa del ruido, se logra una mejora significativa en
la resolución y transmisión de información de las imágenes
publicadas por el EHT.
Palabras clave— imágenes de agujeros negros, procesamiento
digital de señales, envolvente de Hilbert, análisis acústico, análisis
en frecuencia
I. INTRODUCTION
Information transfer is a universal principle bridging
quantum mechanics and classical communication, where all
signals carry energy, meaning, and noise encapsulated in
discrete packages such as numbers, letters, bits or waveforms.
Sonic language emerges when wave patterns become
mutually intelligible between two or more individuals,
enabling them to convey meaning. Written language, in turn,
is a system of abstract symbols that represent the sound of the
waveform; in this sense, a text functions like a musical score.
For example, vowels represent the shape the mouth must
take to produce a given tone, while consonants define the
articulation and structural rhythm of pronunciation. Although
the combination of these symbols is remarkably expressive,
different languages that share the same basic alphabet often
act as acoustic privacy filters, where unfamiliar waveforms or
combinations may be heard but not understood or readable.
Architectural acoustics addresses how signals travel from
emitter to receiver, aiming to reduce distortion and preserve
intelligibility particularly in the articulation of consonants,
which are most susceptible to masking. In this analogy,
amplitude corresponds to font size or pixel intensity, noise to
typographic clutter or distortion, and reflections to the
overlapping of tints and shadows that blur the visual message.
Epistemology refers to the study of knowledge itself: how
we know what we know, what counts as valid information,
and what frameworks we use to extract meaning from
observation, therefore visual images from black holes can be
studied like waves composed of discrete points or pixels.
In this study, images from EHT and PRIMO will serve as
reference benchmarks to test the hypothesis that, by
approaching the problem as an acoustic engineering task with
the application of digital signal processing, it is possible to
improve information transfer from black holes observations.
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G. Bolaños Rodríguez,
"When Light Meets Sound: Signal Analysis of Black Holes",
Latin-American Journal of Computing (LAJC), vol. 12, no. 2, 2025.
II. D
IGITAL DOMAIN WAVES
In sound engineering, natural signals such as speech or the
sound produced by a musical instrument are identified as
unique, non-repeatable events that exist only once in life.
Information transfer between source and receiver occurs
within fractional time intervals, relative to a synchronization
function. If a message is not understood, the receiver may
request repetition from the emitter.
In other cases, such as in noisy classrooms or Moiré-style
animations, the brain demonstrates the ability to reconstruct
incomplete signals. This occurs because familiar waveforms
are stored within a perceptual database, allowing the cognitive
system to interpolate missing information and preserve
semantic meaning.
Digital audio, image and video work with this cognitive
behavior, where a continuous data frame of discrete voltage
values creates the illusion of sound or motion perception,
known as sampling frequency or frames per second fs.
To encode an analog waveform with minimal information
loss, the Nyquist–Shannon sampling theorem must be
satisfied, the sampling frequency fs must be at least twice the
highest frequency component present in the signal to ensure
accurate reconstruction [1]. In architectural practice, this
concept finds a parallel: defining a distance or magnitude
requires at least two reference points, from L
0
to L
x
. Once
these boundaries are established, the midpoint can be
calculated as the arithmetic mean.
To increase resolution, the interval between limits can be
subdivided by increasing the fs, however, this is constrained
by both the capabilities of the measurement tools and the
limits of human perception. For instance, a one-meter length
can be divided into 1.000 millimeters, approaching the
practical threshold of visual or mechanical resolution, for
greater precision, microscopy techniques become necessary.
Moreover, the meter itself is an abstract standard of length
defined independently of individual perception. This is
precisely why standardization is essential in commercial
transactions, where relying solely on human perception could
lead to misinterpretation or dispute.
A. Bit Depth
In binary encoding, the number of bits determines the
possible amplitude levels that a digital signal can adopt (1).
This relationship follows natural binary reproduction growth.
=2
,
(1)
For an 8-bit signal, this results in 256 levels ranging from
N
0
to N
n-1
, and a 32-bit audio signal has over 4 billion possible
discrete states, providing extremely fine resolution as
represented in Fig. 1.
Fig. 1. Bit depth influence in waveform amplitud representation
In Python, tools from matplotlib library such as histogram,
contour maps, 3D surface plots and colormaps are useful to
analyze the distribution of amplitude across the pixel
intensities in each image channel.
Fig. 2 presents the channel analysis for the M87 EHT
image. On screen pixel intensity often results in perceptual
masking of lower levels. To enhance the visual interpretation
of channel information, an HSV colormap has been applied on
the 3D surface plot.
Fig. 2. Amplitude distribution in M87 EHT in RGB channels M87 EHT.
By applying a simple level adjustment in Photoshop to the
M87 images presented in the work of Lia Medeiros et al.
which used the PRIMO algorithm on the original 2019 EHT
data [2], sharp transitions in brightness become immediately
visible in Fig. 3. These features are not apparent in the original
images due to perceptual masking, much like how background
noise blurs speech intelligibility in architectural spaces.
Fig. 3. Level adjustment in Photoshop applied to M87 black hole images
from Lia Medeiros et al 2023 ApJL 947 L7.
In image analysis, the histogram allows study the
distribution of pixel intensity values across discrete levels.
Applying the Discrete Fourier Transform (DFT) to the
histogram allows the pixel intensity distribution to be treated
as a finite signal, enabling the analysis of tonal structure in
terms of frequency components [3].
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The histogram DFT is defined as (2):
()
Where x[n] is the histogram value at intensity level n, N
n
is the number of bins, k is the frequency index, and X[k] are
the DFT coefficients representing the frequency content of
the histogram.
Fig. 4 shows a comparison between different black holes
available images including M87 and Sagittarius A*. A
consistent spectral pattern can be observed: the red channel
exhibits a higher concentration of high-frequency
components, while the green and blue channels are
dominated by lower-frequency content, that tends to
resemble a modal shape present in room acoustics response.
Fig. 4. Histogram DFT comparision of black holes images.
B. Entropy
To relate how bit depth links amplitude to discrete values
through quantization, it is worth taking a quick detour into
quantum mechanics. In his foundational paper [4], Max
Planck introduced a statistical interpretation of entropy in
systems of electromagnetic resonators, he stated that “Entropy
implies disorder, and this disorder, according to the theory of
electromagnetic radiation, arises in monochromatic
oscillations of a resonator”.
In an RGB image, each channel can be thought of as an
independent monochromatic resonator as considered before.
Planck mentions that entropy of such a system is related
to the Boltzmann constant k
b
and the number of microstates
N
n
(3):
(3)
Harry Nyquist, on the other hand [5], considered that the
maximum speed W of transmission is related to the number of
current values m, where in a circuit a line speed (4) is:
(4)
Where K is a constant and m represents the number of
characters, in this case the number of available levels, the
higher the number of signal elements, the higher the amount
of intelligence. Nyquist explains that if n is the number of
signal elements per character, the total number of characters
that can be constructed is m
n
, given binary code has two
characters 2
n
levels can be generated, reinforcing the
connection between entropy, information capacity, and bit
depth.
Claude Shannon, in his foundational work A
Mathematical Theory of Communication , formalized this link
by defining entropy H as a measure of uncertainty in the
information source. The Shannon entropy (5) is given by:
(5)
Where p
i
is the probability of observing state i, and K is a
constant depending on the system that may be taken as the
Boltzmann constant, as Planck suggested. The probability pi
is a function of the available states (6):
(6)
In Fig. 5, using the entropy filter from scikit-image, with a
5-pixel moving window is used to extract hidden
informational patterns, then a denoised gaussian filter was
applied to achieve a more stable image from grayscale data.
Fig. 5. Shannon`s entropy analysis of M87 PRIMO and EHT.
Fig. 6 shows entropy profiles for each monochromatic
channel considered independently as well as for DFT. In the
green and blue channels, informational content that was
previously hidden in the lower tonal levels of these systems is
now perceptual. In contrast, the red channel exhibits a DFT
response that strongly resembles a sinc function, a behavior
well-known in the electroacoustic of multiple speaker arrays.
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"When Light Meets Sound: Signal Analysis of Black Holes",
Latin-American Journal of Computing (LAJC), vol. 12, no. 2, 2025.
This pattern implies the presence of coherent constructive
interference and well-defined transitions between ordered and
disordered zones within the red entropy field.
Fig. 6. Entropy comparison between M87 EHT, M87 PRIMO and Sag A*
and DFT histogram analysis
If each state is equally probable in a uniform quantization
system (7), and the number of possible states at level 2
n
:
(7)
Increasing bit depth increases quantization states, raising
entropy and information resolution. However, this depends on
the sample rate, while post-production quantization or
posterizing is possible, lost information can't be restored
unless artificial data is added adding uncertainty.
Interestingly, this growth in informational potential can be
likened to Hooke’s law (8), where the restoring force in a
spring is proportional to its displacement:
(8)
In this analogy, K represents the stiffness of the system or
its informational elasticity, and Δx corresponds to the
amplitude of motion, which can relate to Nyquist speed
information or intelligence transmission.
Fig. 7. Nyquist transmission speed distribution
John William Strutt also known as Lord Rayleigh, in The
Theory of Sound [6] explains that stiffness constants K
governs the propagation speed of wavefront in stretched string
and can be derived from Hooke`s Law, where K as in entropy
depends on the material properties as the Young’s modulus E
and the cross-sectional area A.
In elastic theory, the tension T (9) stored in a string of
natural length L when stretched to a length L′ is given by:
(9)
The wavefront speed c (10) is related to the tension and the
linear mass density μ, or as in special relativity theory to the
energy e and the mass of the particle m:
(10)
As in quantum mechanics, standing waves in strings
respond to discrete modes. Their natural frequencies (11) are
harmonically related and constrained by the length of Lx,
where the fundamental mode corresponds to a wavelength that
is twice the length λ = 2Lx:
(11)
The greater the stretch of a string, the greater the tension,
the greater wavefront speed c, where higher elastic energy
storage state leads to higher frequency oscillations. This
behavior mirrors Planck’s quantum energy relationship (11):
(11)
Where h is the Planck`s constant, that is the linear
expression of the reduce Planck’s constat , that
represent the angular momentum of the electron in Bohr radius
of the hydrogen atom where
(kgm
2
s
-1
).
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The rest of the energy is stored as passive energy in a
system due to its configuration. For example, tension in a
stretched string allows the system to oscillate, where the
potential energy is at equilibrium (12), in relativistic terms:
(12)
In a stretched string or elastic system, the static
displacement x
0
(13) due to pre-tension increase speed:
(13)
Dynamic energy is the energy fluctuating around the
equilibrium due to a disturbance (14), where the acoustic
pressure emitted by a string instrument if proportional to the
initial input force or the amplitude Δx of motion, which relates
to mass times acceleration, as in Newton’s second law:
(14)
Without a triggering force, the system remains at rest, but
already energized, like a compressed spring or a charged field,
the higher the tension the lower the amplitude of motion
available thus less string is available to oscillate (15):
(15)
This principle has analogies in electronic and acoustic
systems, for instance, a camera shutter requires a baseline
voltage to operate or rest energy, generating a background
noise floor, especially evident at high ISO settings.
Fig. 8 completes the entropy analysis, where the
histograms are flattened and smoothed using a Gaussian filter.
The first and second derivatives are then calculated using
NumPy.Gradient. The relative mass is defined as the
normalized pixel intensity x
i
relative to the entropy scale 8-bit
scale where m = x
i
/8.
Fig. 8. Comparison between entropy-based and original histograms of the
M87 PRIMO image
C. Histogram equalization
In digital image processing, histogram equalization is a
contrast enhancement technique that redistributes pixel
intensities to span the full dynamic range. This prevents the
clustering of values within a narrow band, as seen in earlier
unprocessed images.
This implementation, shown in Fig. 9, applies OpenCV’s
cv2.equalizeHist function, followed by a Gaussian blur to
reduce noise and improve local uniformity. The resulting
intensity distribution is visualized both as a 2D heatmap and
as a 3D surface using Axes 3D
Fig. 9. Visualization of histogram equalization and 3D Surface
For a more detailed 3D representation, in Fig. 10 Plotly
was used to generate interactive surfaces with real-time
control over lighting, angle, and zoom. This not only enhances
visual analysis but also enables the export of the surface as a
3D printable heightmap, bridging digital signal processing and
physical modeling.
Fig. 10. M87 PRIMO Plotly 3D histogram equalization
From this analysis PRIMO-enhanced image reveals a
lower rest energy, evidenced by its reduced background
entropy and more efficient use of dynamic range.
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G. Bolaños Rodríguez,
"When Light Meets Sound: Signal Analysis of Black Holes",
Latin-American Journal of Computing (LAJC), vol. 12, no. 2, 2025.
D. Bit planes
Bit plane decomposition is a technique used in digital
image processing to analyze the contribution of each
individual bit in the binary representation of pixel intensity
values [7]. In an 8-bit RGB image, each channel contains 8
binary layers ranging from the most significant bit MSB or
bit 7 to the least significant bit LSB or bit 0.
Applying this analysis to M87 PRIMO in Fig. 11, it reveals
that in the green and blue channels, most of the structural
information is concentrated at the LSB level.
Fig. 11. Bit plane descomposition of M87 PRIMO
In bit 2 and bit 1, spherical or radial surfaces are notable in
the area near the central body, this reveals that coherent
information of low order is present in Fig. 12, while on M87
and Sag A* EHT background noise and distortion begin to be
predominant in this area.
Fig. 12. Bit plane 3,2,1 closeup for M87 PRIMO, EHT and Sag A*.
In acoustics, sound diffusers are based on the quadratic
residue series discovered by Carl Friederich Gauss, which
were later explored by Manfred Schroeder in his book
Number Theory in Science and Communication [8]. These
sequencies have been used from signals analysis to acoustic
design of concert halls.
Bit-plane’s phase level is determined by the modulus of
the prime number (16) of the MSB plane.
(16)
By analogy, this principle can be applied to bit planes in
digital images or signals, such that the bit index n[0,7] acts
as the spatial sample, a prime modulus p defines the residue
cycle, the resulting d
n
acts as a phase level assigned to each
plane as in Fig. 13.
Fig. 13. QRD mod 7 of bit plane descomposition of M87 PRIMO and EHT
In binary encoding, the number of bits determines the
possible frequencies or energy levels that can be represented
inside the Nyquist theorem, where 1-bit or binary ground state
can hold two digits like the number of electrons in Bohr
radius. The Sommerfeld and Pauli exclusion model establish
that an electron can occupy a specific energy level, determined
by the possible combinations of the quantum numbers n, l, m,
and the electron spin up and down.
The quadratic value then represents a sum of the odd
numbers (17), that can start in 0 like in binary planes or in 1
like in quantum mechanics.
(17)
This can be extended to Girolamo Cardano theorem for the
cubic root or the basic of complex numbers, where nonlinear
combinations of bit layers reconstruct coherent energy.
(18)
Fig. 14. DFT of centered frequency and pixel intensity values
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III. I
NTERFERANCE PATTERNS
Digital images behave much like an audio signal, a
composition of discrete values to storage a mix of oscillations,
harmonics, and amplitude. The key distinction lies in the
domain; these modulations are distributed across a two-
dimensional spatial plane rather than time.
Considering the Nyquist-Shannon theorem, a pixel with
intensity 1 can be represented at the other side of the central
or neutral value 127 or fs/2. This means that when the image
is inverted in phase, the new pixel values is 254 and vice versa.
Fig. 15. DFT of centered frequency and pixel intensity values
This interpretation allows us to understand a digital image
as a frozen waveform, where each pixel captures a discrete
amplitude value. In the RGB color model, its inverse
corresponds to the CYM (Cyan–Yellow–Magenta) color
complement. Thus, both representations are mathematically
linked, forming a dual system where color and anti-color are
symmetrically encoded, or energy and dark energy.
By combining the original image with its inverted version,
various interference patterns can be generated using simple
mathematical operations—such as addition, subtraction,
multiplication, division, mean, or root sum of squares. These
operations simulate constructive or destructive interference,
which can be used to enhance color contrast, suppress noise,
or reveal latent geometric structures within the image.
Fig. 16 shows that for M87 PRIMO the sum of squares
improves image quality notable; it tends to be the same
representation of the bit plane 2 with the difference that the
radial shapes are not present. This is a simpler process that can
be performed in any digital image software but in python,
mathematics between signals can represent physics equations.
Fig. 16. Interference patterns for M87 PRIMO
This process behaves like a quick tool allowing
comparative evaluations and signal chain optimizations, that
can be conceptualized like auxiliary channels or parallel
image processing, side-chain compression or frequency-space
filtering.
Fig. 17. Interference patterns for M87 EHT
Each image behaves like a reverberant space, analogous
to an acoustic room, where every geometry responds
uniquely. Therefore, image fusion becomes site-specific
engineering, requiring tailored mathematical approaches for
each context.
For example, Fig. 18 shows the mantissa fusion method for
M87 EHT, this allows the increase of detail, where around
the accretion disk radial bodies are present.
Fig. 18. Mantissa fusion for M87 PRIMO with color filters
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"When Light Meets Sound: Signal Analysis of Black Holes",
Latin-American Journal of Computing (LAJC), vol. 12, no. 2, 2025.
IV. C
OMPLEX SIGNAL ANALYSIS
Every signal carries more than just what is visible or
audible, it encodes phase, rotation, frequency content, and
damping. In the case of naturally damped waves, these
properties define how energy propagates and fades. All
systems inherently possess a natural reverberation time,
dictated by their boundary conditions, constructive
interference from wave superposition and energy dissipation.
Emerging from Cardano's solutions to cubic equations,
later formalized by Gauss and explored by Euler, complex
numbers have become indispensable in signal analysis, they
allow to model phase, represent signal rotation in the complex
plane, and analyze spectral components with far greater
precision.
For any real-value wave, the Hilbert function form SciPy
provides a powerful tool for constructing its complex analytic
signal. This transformation enables the creation of a Hilbert
analytic envelope (19), which reveals the instantaneous
amplitude and phase of the original waveform.
(
)
=
(
)
+()
(19)
Fig. 19 shows the basic script to get the complex signal
that can be decomposed on positive and negative phases, that
can be useful to study interferometric asymmetries, signal
polarity, and resonance structure, which are crucial for
analyzing wave behavior, modulated textures, and perceptual
balance in images and acoustic fields.
Fig. 19. DFT of centered frequency and pixel intensity values
Fig. 20 shows that black hole observations carry phase
information, when processed through a Hilbert-based
algorithm directional interference patterns emerge.
Fig. 20. Filtered phase decomposition with Hilbert-based environment
Applying a Gaussian high-pass filter before computing the
Hilbert envelope to reduce bass helps enhance fine detail. This
procedure, commonly used in audio processing, shows that
each phase carries complete information. A standing wave
pattern simulates resonance within a bounded system, like a
membrane or room, as a synthetic interferogram, where
positive and negative phases are spatially modulated to
simulate real wave superposition. What is interesting about
this procedure is that rarefaction and compression is visible on
the accretion disk.
Fig. 21. Filtered phase with interferogram standing wave M87 PRIMO
Applying cosine and sine mask to the instantaneous phase
a complex angular wave can be obtained; this shows that more
information is present on the background noise.
Fig. 22. DFT of centered frequency and pixel intensity values
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Another type of analysis derived from Hilbert space
representations includes the construction of vector fields using
quiver plots, 3D point cloud visualizations for modeling,
phase histograms, polar representations of the complex
waveform, and even streamlines of the electric field. In this
case, the electric field is estimated by applying a Sobel
operator to the cosine component. For this approach, sine and
cosine masks were first applied to the image, and then the
Hilbert analytic envelope was computed separately.
Fig. 23. Quiver, streamlines, and polar representation derived from analytic
Hilbert envelope of sine and cosine mask of M87 EHT, PRIMO and
Sagittarius A*
V. CONCLUSSIONS
By analyzing data from the EHT and PRIMO
reconstructions, digital images were treated as
multidimensional signals within a finite boundary space
analogous to a reverberant room.
Although the event horizon is considered the theoretical
boundary beyond which light cannot escape, and the
accretion ring marks this luminous frontier, this study shows
that valuable structural and energetic information can still be
extracted both within and around that limit. Radial features
previously obscured become perceptible, which may suggest
the presence of multi-orbital structures or, alternatively,
highlight artifacts introduced by the image reconstruction
algorithms themselves. More profound research is needed to
confirm the nature of these structures to get a valid scientific
affirmation.
The PRIMO algorithm yields a more stable and coherent
representation of the M87 black hole with a lower
background level. However, to ensure greater data fidelity
and structural insight, it is recommended that the Event
Horizon Telescope Collaboration apply the same analytical
approach to the Sagittarius A* dataset to compare results.
All the signal and image analysis presented in this work
has been conducted using Python-based open-source tools
and exclusively using publicly available images released by
EHT Collaboration. Due to file compression, noise artifacts,
and potential preprocessing steps applied during data
publication, RAW datasets could bring more valid scientific
conclusions.
A
CKNOWLEDGMENT
The author wish to thank the comments and feedback from
the anonymous reviewer for giving this paper a more coherent
form.
This research received no external funding and is a small
part of a broader research effort carried out over the past two
years. All findings result from mathematical operations rooted
in signal theory and acoustics, although artificial intelligence
provided coding assistance, the true spark of understanding
stems from human connection. A book, in this sense, is not
just a container of information, it is a short circuit between
minds, bridging time, space, and lived experience.
As Marshall Long points out in Architectural Acoustics,
“Houtgast, Steeneken, and Plomp reasoned that stars are the
spatial equivalent of an acoustical impulse source” idea that
eventually led to the Speech Transmission Index STI, derived
from the Modulation Transfer Function MTF. In that same
spirit, this research proposes that when light meets sound,
acoustic engineering can provide a robust framework for
analyzing astrophysical phenomena.
R
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85297-1
ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025
59
LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 2, July 2025
AUTHORS
Guillermo Bolaños Rodríguez is an acoustic engineer and signal analyst
specializing in the intersection of quantum physics, architectural
acoustics, and digital signal processing. His work explores how
concepts like resonance, energy dissipation, and modal behavior can
be unified across physical and astrophysical systems. He develops
computational models that treat digital images and acoustic spaces
as energy landscapes, applying techniques such as Fourier analysis,
Hilbert transforms, and phase-based visualization to uncover hidden
structures in both architectural environments and astronomical
observations. His experience includes the acoustic design of theaters
and recording studios in several cities across Ecuador, where he
integrates theoretical frameworks with real-world applications.
Currently, he is seeking to develop an educational initiative to address
the widespread lack of acoustic awareness in Ecuadorian society,
aiming to bridge scientific knowledge with public understanding.
Guillermo Bolaños Rodríguez
G. Bolaños Rodríguez,
"When Light Meets Sound: Signal Analysis of Black Holes",
Latin-American Journal of Computing (LAJC), vol. 12, no. 2, 2025.
