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Title	A Parametric Survey On Polyphonic Sound Event Detection And Localization
ID_Doc	3614
Authors	Mohmmad S.; Sanampudi S.K.
Year	2025
Published	Multimedia Tools and Applications, 84, 20
DOI	http://dx.doi.org/10.1007/s11042-024-19785-6
Abstract	With the rapid growth in technology, everything will become automatic. Automatic detection is needed in health, security, smart homes, smart cities, and various environments. Sound is an essential function of automatic detection, which can be created in different environmental situations. To handle this, various sound detection devices are incorporated that run alone or with a combination of video. This auditory detection system is becoming more emerging in research today. This study is a survey on sound event detection and localization-related models. This survey research included details about different datasets, feature extractions, and classification methods that are implemented in sound-related research. Distinct and typical environmental situations comprise multiple sound sources that generate different sounds and noise. Our research compares distinct algorithms to detect sound events and localization, such as HMM, CNN, SVM, Random Forest, NMF, CRNN, VGG16, etc. Sound-related feature extraction methods, such as MFCC, Mel-Spectrogram, Mel band elements, ZCR, and wavelet features, and their importance in sound classification are also explained with a comparative analysis approach. The sections end with challenges with the existing approaches in different environments and feature extraction combinations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Author Keywords	CNN; HMM; MFCC; Sound Event Detection; SVM

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