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

• Tipo: Outra Publicação em Revista Científica Internacional
• Data: 2022-11

Autores

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

Revista

Título: Sensors

Vol. 22 Nº 4

Páginas: 8608-8608

ISSN: 1424-3210

Editora: MDPI

Indexação

ISI Web of Knowledge - 0 Citações

ISI Web of Science

Scopus

Clarivate Analytics

Classificação Científica

FOS: Ciências da engenharia e tecnologias

CORDIS: Ciências Tecnológicas

Projetos Associados

Safe Cities - Safe Cities - Inovação para Construir Cidades Seguras

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 30