Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2309.02209

Edukoi: developing an interactive sonification tool for astronomy between entertainment and education

Lucrezia Guiotto Nai Fovino·Massimo Grassi·Anita Zanella·Luca Di Mascolo·Michele Ginolfi
View original source ↗Read source document ↗Export metadata ↓

Abstract

Edukoi is a software that aims to make interactive sonification suitable to convey and extract information. The program design is a modification of the software Herakoi, which sonifies images in real time mapping pitch to colour using a motion-aware approach for allowing users to interact with images through sound. The pitch-colour association of Hearkoi, albeit pleasing from the entertainment side, is not efficient for communicating specific information regarding colours and hues to listeners. Hence we modified it to create an instrument to be used by visually impaired and sighted children to explore images through sound and extract accurate information. We aim at building a flexible software that can be used in middle-schools for both art and science teaching. We tested its effectiveness using astronomical images, given the great fascination that astronomy always has on kids of all ages and backgrounds. Astronomy is also considered a very visual science, a characteristic that prevents students from learning this subject and having a related career. With this project we aim to challenge this belief and give to students the possibility to explore astronomical data through sound. Here we discuss our experiment, the choices we made regarding sound mappings, and what psychophysiological aspects we aim to evaluate to validate and improve Edukoi.

Explore related subjects

astro-ph.IMphysics.ed-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Lucrezia Guiotto Nai Fovino, Massimo Grassi, Anita Zanella, Luca Di Mascolo, Michele Ginolfi. 2023-09-05. Edukoi: developing an interactive sonification tool for astronomy between entertainment and education. https://arxiv.org/abs/2309.02209

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Observations of Binary Stars with the 1.3-m Devasthal Fast Optical Telescope Using Speckle Interferometry: An Attempt

We present a feasibility study of implementing optical interferometry and speckle techniques with the 1.3-m Devasthal Fast Optical Telescope (DFOT) at Aryabhatta Research Institute of Observational Sciences (ARIES), which is currently dedicated to photometric observations. Using the sCMOS camera at the DFOT backend, we perform interferometric speckle observations of six binary stars. Standard Speckle Interferometry algorithms are applied to analyze the recorded speckle images. While this study does not aim to achieve the diffraction limit of DFOT or address a full science-driven resolution case, it serves as a crucial testbed for instrumentation, data acquisition, and analysis of Speckles with DFOT. Although the individual speckle patterns exhibit significant positional variations from frame to frame, the Speckle Interferometry analysis successfully recovers the characteristic three-peak structure of the wide binaries, allowing their relative separations and position angles to be determined, demonstrating the viability of the approach. The measured angular relative separations of $γ$ Leo, $γ$ Vir, and $ζ$ Her are $4.744'' \pm 0.012''$, $3.387'' \pm 0.027''$, and $1.582'' \pm 0.026''$, respectively, with corresponding projected position angles of $126.94^{\circ} \pm 0.14^\circ$, $171.57^{\circ} \pm 0.33^\circ$, and $81.06^{\circ} \pm 0.91^\circ$. These measurements are consistent with previously reported values in the literature, which provides strong motivation for more systematic observations and future implementation of optical interferometry techniques at meter-class telescopes.

astro-ph.IM↗
arXiv2026

Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks

Gravitational-wave detectors such as LIGO, Virgo, and KAGRA are highly sensitive instruments susceptible to many noise sources. Short-duration transient noise events, known as glitches, pose a particular challenge for data analysis pipelines, as they can mimic or obscure astrophysical signals. We present GlitchGAN, a class-conditional generative model built on the Conditional Derivative GAN (cDVGAN) architecture, capable of synthesizing seven glitch types from LIGO's third observing run (O3) directly in the time domain. GlitchGAN generalizes effectively, learning to reproduce a diverse glitch space consistent with high-quality DeepExtractor reconstructions, and can generate hybrid glitch morphologies by interpolating across its class-conditioning vector. It generates 1000 glitches in under 22 seconds on a CPU, suitable for detector simulations, mock data challenges, and pipeline validation. Synthetic glitches are validated using the Gravity Spy classifier and UMAP embeddings, both showing strong agreement with real data. To probe residual distributional differences, we train a separate holdout GlitchGAN model and use a downstream CNN to distinguish held-out real glitches from synthetic ones: detectability is high in a clean representation but drops substantially once both populations are injected into realistic detector noise, the condition under which they would typically be used. Despite this, GlitchGAN-generated glitches remain practically useful: augmenting real training sets with synthetic samples matches simple duplication of real data when data is abundant, and increasingly outperforms it as real data becomes scarce. Finally, we highlight a limitation of magnitude-only spectrograms: magnitude Q-transform classifiers can confidently misclassify physically unrealistic glitches from less robust models, underscoring the need for validation methods that preserve phase information.

astro-ph.IM↗
arXiv2026

SGN: A python framework for stream-processing pipelines

We present the Stream Graph Navigator (SGN), a lightweight Python framework for building streaming data applications. In SGN, stream-processing pipelines are built by connecting computational components into directed acyclic graphs that run within an event loop. The time-series extension of the SGN library, SGN-TS, introduces signal processing methods to handle time series data. Together, SGN and SGN-TS provide the foundation for SGNL, a matched-filtering gravitational-wave search pipeline, and are being adopted by multiple projects across the low-latency gravitational-wave data analysis infrastructure as an extensible and maintainable framework for future gravitational-wave observations.

astro-ph.IM↗