Authors : M. Bhanu Prakash Reddy; S. Mano Venkat

Volume/Issue : Volume 11 - 2026, Issue 10 - October

Google Scholar : https://tinyurl.com/3xbhfru4

DOI : https://doi.org/10.38124/ijisrt/26Oct296

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The groundwork for event detection in football match analysis using audio. It describes the structure, annotated event categories and preparation of synchronized audio data for the Soccer Action Compilation (SADC) dataset. The chapter outlines the major steps in the pre-processing such as segmentation, noise reduction and feature extraction of matchsounds using Mel-Frequency Cepstral Coefficients (MFCCs), which are important for the temporal and spectral properties. The design and implementation of the audio event detection model are explained and the key event detection such as goal, goal attempt, penalty and yellow card is explained with the use of deep learning architectures. Training configurations, parameter selection and evaluation strategies are discussed to make sure that the models are reliable and reproducible. Results of the model are presented using quantitative measures and confusion matrices, and are followed by a discussion on important results, strengths and limitations that were observed. In general, this chapter provides the methodological and analytical framework for the subsequent visual and multimodal event detection in football videos, while showing the effectiveness of using audio cues in understanding the dynamics of a game and summarising the key moments. The topics are related to Multimodal Deep Learning, Skeletal Motion Analysis, Cross-Cultural Dance Recognition and Cultural Heritage Informatics and Explainable AI (XAI).

Keywords : Learning Architecture, MFCC, SADC, Skeletal Motion Analysis, Cross-Cultural Dance Recognition.

References :

1. Singh,  K.,  Yadav,  M.,  Singh,  Y.,  &  Barak,  D. (2024).  Finding  security  gaps  and  vulnerabilities in IoT devices. In Revolutionizing Automated Waste Treatment Systems: IoT and Bioelectronics (pp.  379-395).  IGI  Global  Scientific  Publishing. https://doi.org/10.4018/979-8-3693-6016-3.ch023
2. Hajimahmud,  V. A.,  Singh,  Y.,  &  Yadav,  M. (2024).  Using  a  smart  trash  can  sensor  for  trash disposal. In Revolutionizing Automated Waste Treatment  Systems:  IoT  and  Bioelectronics (pp. 311-319). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-6016-3.ch020
3. Singh,  K.,  Yadav,  M.,  &  Yadav,  R.  K.  (2024). IoT-Based automated dust bins and improved waste  optimization  techniques  for  smart  City.  In Revolutionizing Automated Waste Treatment Systems:  IoT  and  Bioelectronics  (pp.  167-194). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-6016-3.ch012
4. Yadav,  M.,  Hajimahmud,  V.  A.,  Singh,  K.,  & Singh, Y. (2024). Convert waste into energy using a low capacity igniter. In Revolutionizing Automated Waste Treatment Systems: IoT and Bioelectronics (pp. 301-310). IGI Global. https://doi.org/10.4018/979-8-3693-6016-3.ch019
5. Khwaldeh, S., Mohit, Y., & Khushwant, S. (2024, May). Defensive auto-updatable and adaptable bot recommender system (DAABRS): a new architecture approach in cloud computing systems. In 2024 International Congress on Human-Computer  Interaction,  Optimization  and Robotic  Applications  (HORA)  (pp.  1-6).  IEEE. https://doi.org/10.1109/HORA61326.2024.10550519
6. Singh,  K.,  Yadav,  M.,  Singh,  Y.,  &  Moreira,  F. (2025).  Techniques  in  reliability  of  internet  of things (IoT). Procedia Computer Science, 256, 55-62. https://doi.org/10.1016/j.procs.2025.02.095
7. Singh,  K.,  Yadav,  M.,  Barak,  D.,  Bansal,  S.,  & Moreira, F. (2025). Machine-Learning-Based Frameworks  for  Reliable  and  Sustainable  Crop Forecasting. Sustainability, 17(10), 4711. https://doi.org/10.3390/su17104711
8. Singh,  K.,  &  Yadav,  M.  (2025).  Prognosis  of artificial  intelligence  in  education. LatIA, 3,  107-107. https://doi.org/10.62486/latia2025107
9. Yadav, M., Singh, K., Thukral, K., Kwatra, S., & Barak, D. (2025). Intelligent Electronic Ticketing Platform in Smart Transportation Ecosystem. In Driving Green Transportation System Through Artificial Intelligence   and Automation: Approaches, Technologies and Applications (pp. 581-601). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-72617-0_30
10. Singh, K., Yadav, M., & Abdullayev, V. H. (2024). Prediction of Flight Areas using Machine Learning Algorithm. LatIA, (2), 13. https://doi.org/10.62486/latia202493
11. Singh,  K.,  Yadav,  M.,  Singh,  Y.,  Malik,  P.  S., Siwach,  V.,  Khurana,  D.,  ...  &  Elngar,                 A.  A. (2024).  IoT  Networks:  Integrated  Learning  for Privacy-Preserving Machine Learning. In Artificial Intelligence Using Federated Learning (pp. 250-275). CRC Press. https://doi.org/10.1201/978100348200013?urlappend=%3Futm_source%3Dresearchgate. net%26utm_medium%3Darticle
12. Singh, K., Yadav, M., Yadav, R. K., Naib, B. B., Bhatia,  S.,  &  Panda,  D.  K.  (2024,  December). Prognosis of Relocation Disease in Animals using Aggregation                Method with        Optimization Techniques. In 2024 Eighth     International Conference on Parallel, Distributed and Grid Computing (PDGC) (pp. 15-20). IEEE. https://doi.org/10.1109/PDGC64653.2024.10984354
13. Khang, A., Singh, K., & Yadav, M. (2026). Anomaly Detection Driven by Machine Learning: Enhancing SIEM Tools for Sturdy Cyber Defense. In AI-Powered Cybersecurity for Banking and Finance (pp. 338-361). Productivity Press. https://doi.org/10.4324/978100359687514?urlappend=%3Futm_source%3Dresearchgate. net%26utm_medium%3Darticle
14. Ngoc, A. P. T., Abdullayev, V., Khang, A., Ragimova, N., & Yadav, M. (2026). Identify and classify brain tumors using deep learning techniques and magnetic resonance imaging. In Revolutionizing Digital Healthcare Through Artificial  Intelligence  and  Automation (pp.  333- 346). Academic Press. https://doi.org/10.1016/C2024-0-02016-18
15. Ngoc,  A. P. T., Singh, K.,  & Yadav, M. (2026). New neural network methods for the detection of heart diseases. Revolutionizing Digital Healthcare Through  Artificial  Intelligence  and  Automation, 289-298. https://doi.org/10.1016/B978-0-443-36434-1.00034-3