Pan, ZhichengZahraoui, El MehdiMaturana-Russel, PatricioCabrera-Guerrero, Guillermo2026-03-302026-03-302026-03-10Sensors, ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI AG, 26(6), 1749-1749. doi: 10.3390/s260617491424-82201424-8220http://hdl.handle.net/10292/20829Core-collapse supernovae (CCSNe) remain a critical focus in the search for gravitational waves in modern astronomy. Their detection and subsequent analysis will enhance our understanding of the explosion mechanisms in massive stars. This paper investigates the use of convolutional neural networks (CNN) to enhance the detection of gravitational waves originating from CCSNe. We employ two time–frequency analysis techniques to generate spectrograms (training data): short-time Fourier transform (STFT) and Q-transform (QT). Two CNNs were trained independently on sets of spectrogram images of simulated CCSNe signals and advanced LIGO noise. The CNNs detect CCSNe signals based on their time–frequency representation. Both CNNs achieve a near 100% true positive rate for CCSNe GW events with a signal-to-noise ratio greater than 0.5 in our test set. Nevertheless, the CNN trained on the STFT spectrograms outperforms the one based on the Q-transform for SNRs below 0.5.© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.4606 Distributed Computing and Systems Software46 Information and Computing SciencesMachine Learning and Artificial Intelligence0301 Analytical Chemistry0502 Environmental Science and Management0602 Ecology0805 Distributed Computing0906 Electrical and Electronic EngineeringAnalytical Chemistry3103 Ecology4008 Electrical engineering4009 Electronics, sensors and digital hardware4104 Environmental management4606 Distributed computing and systems softwarecore-collapse supernovaeCNNQ-transformaLIGOJournal ArticleOpenAccess10.3390/s26061749