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

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Název: Sound Classification and Processing of Urban Environments: A Systematic Literature Review.
Autoři: Nogueira, Ana Filipa Rodrigues, Oliveira, Hugo S., Machado, José J. M., Tavares, João Manuel R. S.
Zdroj: Sensors (14248220); Nov2022, Vol. 22 Issue 22, p8608, 30p
Témata: DATA augmentation, DEEP learning, ECOLOGY, SMART cities, SOUNDS
Abstrakt: 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 Deep Learning (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. [ABSTRACT FROM AUTHOR]
Copyright of Sensors (14248220) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Sound Classification and Processing of Urban Environments: A Systematic Literature Review.
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  Data: <searchLink fieldCode="AR" term="%22Nogueira%2C+Ana+Filipa+Rodrigues%22">Nogueira, Ana Filipa Rodrigues</searchLink><br /><searchLink fieldCode="AR" term="%22Oliveira%2C+Hugo+S%2E%22">Oliveira, Hugo S.</searchLink><br /><searchLink fieldCode="AR" term="%22Machado%2C+José+J%2E+M%2E%22">Machado, José J. M.</searchLink><br /><searchLink fieldCode="AR" term="%22Tavares%2C+João+Manuel+R%2E+S%2E%22">Tavares, João Manuel R. S.</searchLink>
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  Data: Sensors (14248220); Nov2022, Vol. 22 Issue 22, p8608, 30p
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  Data: 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 Deep Learning (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. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Sensors (14248220) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Nov2022
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