AI-Based Mobile Application for Real-Time Criminal Event Recognition and Classification

Rafael Cachique; Diego Gutierrez; Pedro Castaneda; Sandra Wong-Durand; Roberto Carlos Santa Cruz Acosta; Alberto Daniel Garcia-Nunez

doi:10.48084/etasr.17841

Authors

Rafael Cachique Faculty of Information Systems Engineering, Universidad Peruana de Ciencias Aplicadas, Lima, Peru
Diego Gutierrez Faculty of Information Systems Engineering, Universidad Peruana de Ciencias Aplicadas, Lima, Peru
Pedro Castaneda Faculty of Information Systems Engineering, Universidad Peruana de Ciencias Aplicadas, San Isidro, Lima, Peru https://orcid.org/0000-0003-1865-1293
Sandra Wong-Durand Faculty of Information Systems Engineering, Universidad Peruana de Ciencias Aplicadas, San Isidro, Lima, Peru https://orcid.org/0000-0002-6154-2124
Roberto Carlos Santa Cruz Acosta Faculty of Systems Engineering and Electrical Mechanics, Universidad Nacional Toribio Rodriguez de Mendoza, Amazonas, Peru https://orcid.org/0000-0001-8802-9083
Alberto Daniel Garcia-Nunez Universidad Pontificia Bolivariana, Medellin, Antioquia, Colombia https://orcid.org/0000-0002-9402-3785

Volume: 16 | Issue: 3 | Pages: 35950-35961 | June 2026 | https://doi.org/10.48084/etasr.17841

Received: 30 January 2026 | Revised: 13 March 2026, 7 April 2026, 20 April 2026, and 22 April 2026 | Accepted: 23 April 2026 | Online: 6 June 2026

Corresponding author: Sandra Wong-Durand

Abstract

This paper proposes an Artificial Intelligence (AI)-based mobile application that identifies and categorizes crime and emergency events utilizing an acoustic and linguistic signals mechanism. The system combines Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) models with sound event detection architectures to process emergency call recordings and identify incidents such as robberies, assaults, and riots. Data training consisted of preprocessing more than 530 audio recordings of emergency calls made by the Peruvian National Police (PNP), in anonymized formats, and training deep learning models based on convolutional and transformer-based networks. The performance was evaluated using metrics such as precision, accuracy, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The multimodal fusion model achieved high performance, demonstrating an accuracy of 86.79% and an AUC-ROC of 87.14%, strongly distinguishing between emergency and non-emergency conditions in various fields and noisy environments. The results of the study show that the proposed solution is highly reliable and responsive, presenting an opportunity to enhance urban security and inform public safety system decision-making.

Keywords:

Artificial Intelligence (AI), sound event detection, Natural Language Processing (NLP), deep learning, public safety, real-time classification, emergency calls, acoustic analysis, mobile application

References

Instituto Nacional de Estadística e Informática (INEI), "Estadísticas de Criminalidad, Seguridad Ciudadana y Violencia, Julio–Setiembre 2024." Lima, Peru, 2024, [Online]. Available: https://m.inei.gob.pe/media/MenuRecursivo/boletines/boletin_seguridad_jul_set24.pdf.

Instituto Nacional de Estadística e Informática (INEI). "Sistema Integrado de Estadísticas de la Criminalidad y Seguridad Ciudadana (Datacrim) – Mapa interactivo." Datacrim. https://datacrim.inei.gob.pe/panel/mapa.

"Defensoría del Pueblo: es necesario evaluar funcionamiento del Sistema Nacional de Seguridad ciudadana ante aumento de delincuencia." Defensoria del Pueblo - Perú. https://www.defensoria.gob.pe/defensoria-del-pueblo-es-necesario-evaluar-funcionamiento-del-sistema-nacional-de-seguridad-ciudadana-ante-aumento-de-delincuencia/.

Instituto de Estudios Peruanos (IEP), "Informe de opinión de enero 2025 (Informe completo)." Lima, Peru, 2025, [Online]. Available: https://estudiosdeopinion.iep.org.pe/wp-content/uploads/2025/02/IEP-Informe-de-opinion-enero-2025-informe-completo.pdf.

Lima Cómo Vamos, "Reporte urbano de percepción ciudadana 2023." Lima, Peru, 2024. [Online]. Available: https://www.limacomovamos.org/wp-content/uploads/2024/01/EncuestaLCV2023.pdf.

Banco Central de Reserva del Perú (BCRP), "La inseguridad ciudadana y su impacto en la economía." 2024. [Online]. Available: https://www.bcrp.gob.pe/docs/Publicaciones/Reporte-Inflacion/2024/setiembre/reporte-de-inflacion-setiembre-2024-recuadro-2.pdf.

"Lomas de Lima son amenazadas frecuentemente por traficantes de terrenos." Gob. https://www.gob.pe/institucion/minam/noticias/642743-lomas-de-lima-son-amenazadas-frecuentemente-por-traficantes-de-terrenosv.

Eagle Eye Networks, "2023 Trends in Video Surveillance." 2023. [Online]. Available: https://www.een.com/wp-content/uploads/2022/12/2023-Trends-Report-20221128.pdf.

M. Burgess, "London Underground Is Testing Real-Time AI Surveillance Tools to Spot Crime," Wired, Feb. 08, 2024.

M. Neri, F. Battisti, A. Neri, and M. Carli, "Sound Event Detection for Human Safety and Security in Noisy Environments," IEEE Access, vol. 10, pp. 134230–134240, 2022.

A. Mukhamadiyev, I. Khujayarov, D. Nabieva, and J. Cho, "An Ensemble of Convolutional Neural Networks for Sound Event Detection," Mathematics, vol. 13, no. 9, May 2025, Art. no. 1502.

Y. Zhou and H. Wan, "Dual-branch attention module-based network with parameter sharing for joint sound event detection and localization," EURASIP Journal on Audio, Speech, and Music Processing, vol. 2023, no. 1, June 2023, Art. no. 27.

M. Y. Shams, T. Abd El-Hafeez, and E. Hassan, "Acoustic data detection in large-scale emergency vehicle sirens and road noise dataset," Expert Systems with Applications, vol. 249, Sept. 2024, Art. no. 123608.

A. Raza, F. Rustam, B. Mallampati, P. Gali, and I. Ashraf, "Preventing Crimes Through Gunshots Recognition Using Novel Feature Engineering and Meta-Learning Approach," IEEE Access, vol. 11, pp. 103115–103131, 2023.

W. Mansouri, M. A. Alohali, H. Alqahtani, N. Alruwais, M. Alshammeri, and A. Mahmud, "Deep convolutional neural network-based enhanced crowd density monitoring for intelligent urban planning on smart cities," Scientific Reports, vol. 15, no. 1, Feb. 2025, Art. no. 5759.

J. Gao, H. Yang, M. Gong, and X. Li, "Audio–visual representation learning for anomaly events detection in crowds," Neurocomputing, vol. 582, May 2024, Art. no. 127489.

M. Bhat, S. Paul, U. K. Sahu, and U. K. Yadav, "Revolutionizing crowd surveillance through voice-driven face recognition empowering rapid identification: towards development of sustainable smart cities," Engineering Research Express, vol. 6, no. 2, May 2024, Art. no. 025219.

D. A. H. Ollachica, B. K. A. Asante, and H. Imamura, "Autonomous UAV Implementation for Facial Recognition and Tracking in GPS-Denied Environments," IEEE Access, vol. 12, pp. 119464–119487, 2024.

D. Tsiktsiris, A. Lalas, M. Dasygenis, and K. Votis, "Multimodal Abnormal Event Detection in Public Transportation," IEEE Access, vol. 12, pp. 133469–133480, 2024.

E. Bifari, A. Basbrain, R. Mirza, A. Bafail, S. Albaradei, and W. Alhalabi, "Text mining and machine learning for crime classification: using unstructured narrative court documents in police academic," Cogent Engineering, vol. 11, no. 1, Dec. 2024, Art. no. 2359850.

Y. Liu, Q. Guo, C. Yang, and Y. Liao, "TIPS: Tailored Information Extraction in Public Security Using Domain-Enhanced Large Language Model," Computers, Materials & Continua, vol. 83, no. 2, pp. 2555–2572, 2025.

A. Hodorog, I. Petri, and Y. Rezgui, "Machine learning and Natural Language Processing of social media data for event detection in smart cities," Sustainable Cities and Society, vol. 85, Oct. 2022, Art. no. 104026.

G. Papageorgiou, S. Bersimis, and P. Economou, "Real-time monitoring of streaming text data by integrating text visualization techniques and natural language processing," International Journal of Data Science and Analytics, vol. 20, no. 5, pp. 4757–4776, Oct. 2025.

H. Wang, H. Shi, and J. Duan, "Few-shot Incremental Event Detection," ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 23, no. 2, Feb. 2024, Art. no. 34.

A. Sen et al., "Live Event Detection for People’s Safety Using NLP and Deep Learning," IEEE Access, vol. 12, pp. 6455–6472, 2024.

A. Dong et al., "Predicting the locations of missing persons in China by using NGO data and deep learning techniques," International Journal of Digital Earth, vol. 17, no. 1, Dec. 2024, Art. no. 2304076.

Y. Deng, R. He, and Y. Liu, "Crime risk prediction incorporating geographical spatiotemporal dependency into machine learning models," Information Sciences, vol. 646, Oct. 2023, Art. no. 119414.

L. Xu, A. M. Yildiz, I. Tuncer, F. Ozyurt, S. Dogan, and T. Tuncer, "Detection of community emotions through Sound: An Investigation using the FF-Orbital Chaos-Based feature extraction model," Ain Shams Engineering Journal, vol. 16, no. 2, Feb. 2025, Art. no. 103248.

R. C. Sadler, C. Melde, A. Zeoli, S. Wolfe, and M. O’Brien, "Characterizing Spatio-Temporal Differences in Homicides and Non-Fatal Shootings in Milwaukee, Wisconsin, 2006–2015," Applied Spatial Analysis and Policy, vol. 15, no. 1, pp. 117–142, Mar. 2022.

E. Mancini, A. Galassi, F. Ruggeri, and P. Torroni, "Disruptive situation detection on public transport through speech emotion recognition," Intelligent Systems with Applications, vol. 21, Mar. 2024, Art. no. 200305.

S. Mihalache and D. Burileanu, "Speech Emotion Recognition Using Deep Neural Networks, Transfer Learning, and Ensemble Classification Techniques," Romanian Journal of Information Science and Technology, vol. 2023, no. 3–4, pp. 375–387, Sept. 2023.

S. Gharib, M. Tran, D. Luong, K. Drossos, and T. Virtanen, "Adversarial Representation Learning for Robust Privacy Preservation in Audio," IEEE Open Journal of Signal Processing, vol. 5, pp. 294–302, 2024.

P. Li, L. M. Hoi, Y. Wang, X. Yang, and S. K. Im, "Enhancing Speaker Recognition with CRET Model: a fusion of CONV2D, RESNET and ECAPA-TDNN," EURASIP Journal on Audio, Speech, and Music Processing, vol. 2025, no. 1, Feb. 2025, Art. no. 9.

S. Tam and Ö. Ö. Tanriöver, "Multimodal Deep Learning Crime Prediction Using Tweets," IEEE Access, vol. 11, pp. 93204–93214, 2023.

A. A. Alharbi, "DeepCAMS: A Deep Learning Approach for Real-Time Crowd Monitoring and Suspicious Behavior Detection Using Spatial-Temporal Analysis," Engineering, Technology & Applied Science Research, vol. 15, no. 4, pp. 26113–26119, Aug. 2025.

H. M. Rohini and S. Prabhavathi, "Automated Poultry Health Monitoring through Acoustic Analysis Using Convolutional Neural Networks," Engineering, Technology & Applied Science Research, vol. 15, no. 5, pp. 26339–26343, Oct. 2025.

M. Belhadj, M. Mazouz, and D. Djeridi, "EmotionNet: A Novel Hybrid Deep Learning Model for Arabic Speech Emotion Recognition," Engineering, Technology & Applied Science Research, vol. 15, no. 5, pp. 26619–26625, Oct. 2025.