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

• Type: Another Publication in an International Scientific Journal
• Date: 2022-11

Authors

Ana Filipa Rodrigues Nogueira (Author) Other; Hugo S. Oliveira (Author) Other; José J. M. Machado (Author) FEUP; João Manuel R. S. Tavares (Author) FEUP

Journal

Title: Sensors

Vol. 22 No. 4

Pages: 8608-8608

ISSN: 1424-3210

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

ISI Web of Science

Scopus

Clarivate Analytics

Scientific classification

FOS: Engineering and technology

CORDIS: Technological sciences

Associated Projects

Safe Cities - Safe Cities - Inovação para Construir Cidades Seguras

Other information

Authenticus ID: P-00X-JTR

DOI: 10.3390/s22228608

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 30