Trends of Sound Event Recognition in Audio Surveillance: A Recent Review and Study

N, Shreyas; Venkatraman, M.; Malini, S.; Chandrakala, S.

doi:10.1016/b978-0-12-816385-6.00007-6

Cited by 10 publications

(2 citation statements)

References 14 publications

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“…Urban Sound classification methods are very important for smart cities (Bello et al [2]), in particular, for audio surveillance (Shreyas et al [58], Laffitte et al [59]) and noise pollution mitigation (Arnault et al [60], Bello et al [61]). For example, Scarpiniti et al [62] implemented a method in construction sites to detect hazards and unmanned activity monitoring.…”

Section: Applicationsmentioning

confidence: 99%

Sound Classification and Processing of Urban Environments: A Systematic Literature Review

Nogueira

Oliveira

Machado

et al. 2022

Sensors

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Audio recognition can be used in smart cities for security, surveillance, manufacturing, autonomous vehicles, and noise mitigation, just to name a few. However, urban sounds are everyday audio events that occur daily, presenting unstructured characteristics containing different genres of noise and sounds unrelated to the sound event under study, making it a challenging problem. Therefore, the main objective of this literature review is to summarize the most recent works on this subject to understand the current approaches and identify their limitations. Based on the reviewed articles, it can be realized that dl architectures, attention mechanisms, data augmentation techniques, and pretraining are the most crucial factors to consider while creating an efficient sound classification model. The best-found results were obtained by Mushtaq and Su, in 2020, using a DenseNet-161 with pretrained weights from ImageNet, and NA-1 and NA-2 as augmentation techniques, which were of 97.98%, 98.52%, and 99.22% for UrbanSound8K, ESC-50, and ESC-10 datasets, respectively. Nonetheless, the use of these models in real-world scenarios has not been properly addressed, so their effectiveness is still questionable in such situations.

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Section: Applicationsmentioning

confidence: 99%

Sound Classification and Processing of Urban Environments: A Systematic Literature Review

Nogueira

Oliveira

Machado

et al. 2022

Sensors

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“…According to the source-filter acoustic model, MFCC technique provides a brief representation of source sound signal and the path it passes through [10]. Considering the location of heart valves and the direction of murmur propagation in the chest, this property of MFCC technique is employed in many studies, either for segmentation or classification algorithms [11].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Segmentation Algorithm Using Mel-Frequency Cepstrum and Wavelet Transform for Phonocardiography Records

Özkan

Yılmaz

Celebi³

2019

2019 27th Signal Processing and Communications Applications Conference (SIU)

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Cardiovascular system diseases can be identified by using a specialized diagnostic process utilizing a digital stethoscope. Digital stethoscopes provide phonocardiography (PCG) recordings for further inspection, besides filtering and amplification of heart sounds. In this paper, a framework that is useful to develop feature extraction and classification of PCG recordings is presented. This framework is built upon a previously proposed segmentation algorithm that processes a feature vector produced by the agglutinate application of Mel-frequency cepstrum and discrete wavelet transform (DWT). The performance of the segmentation algorithm is also tested on a new data set and compared to the previously reported results. After identifying the fundamental heart sounds and segmenting the PCG recordings, five principal features are extracted from the time domain signal and Mel-frequency cepstral coefficients (MFCC) of each cardiac cycle. Classification outcomes are reported for three nonlinear models: k nearest neighbor (k-NN), support vector machine (SVM), and multilayer perceptrons (MLP) classifiers in comparison with a linear approach, namely Mahalanobis distance linear classifier. The results underline that although neural networks and linear classifier show compatible performance in basic classification problems, with the increase in the nonlinearity of the classification problem their performance significantly vary.

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Exploring current research trends in sound event detection: a systematic literature review

Mohmmad,

Sanampudi

2024

Multimed Tools Appl

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Trends of Sound Event Recognition in Audio Surveillance: A Recent Review and Study

Cited by 10 publications

References 14 publications

Sound Classification and Processing of Urban Environments: A Systematic Literature Review

Sound Classification and Processing of Urban Environments: A Systematic Literature Review

Hybrid Segmentation Algorithm Using Mel-Frequency Cepstrum and Wavelet Transform for Phonocardiography Records

Exploring current research trends in sound event detection: a systematic literature review

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