Live Event Detection For Public Safety Using Sparse LSTM Networks In Hazard Monitoring Systems

Author(s)	Ms. Pooja Popat Barve, Dr. Sachin Sukhadeo Bere, Dr. Dinesh Bhagwan Hanchate
Country	India
Abstract	A Sparse Long Short-Term Memory (LSTM) network is used in this study's real-time audio categorization system to detect potentially harmful noises. Both live-recorded audio and pre-labeled datasets with different sound classes are processed by the system. To guarantee high-quality input for the model's training and real-time predictions, both kinds of data are cleaned and preprocessed. By extracting temporal elements from the audio input, the Sparse LSTM network—which was created to reduce computing costs—allows for accurate categorization. The technology analyzes incoming audio during live prediction, and if it detects "Danger," it sounds an alert. The system terminates the procedure without taking any further action if no threat is recognized. This framework is perfect for use in safety monitoring and alarm systems since it offers a quick and effective solution for audio-based hazard identification.
Keywords	Real-time audio classification; Sparse LSTM; Hazard detection; Temporal feature extraction; Live prediction; Data preprocessing; Audio-based safety monitoring.
Field	Engineering
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-04-22
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.42448
Short DOI	https://doi.org/g9gdqt

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International Journal For Multidisciplinary Research