Constructing an Audio Dataset of Construction Equipment from Online Sources for Audio-Based Recognition

Jeong, Gilsu; Park, Moonseo

doi:10.1109/wsc57314.2022.10015388

Cited by 2 publications

(2 citation statements)

References 38 publications

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“…Paper [29] proposed the construction of a dataset of four classes of equipment and tested several ML classifiers. The results obtained in this work were aligned to those shown in [5], which compared and assessed the accuracy of 17 classifiers on nine classes of equipment.…”

Section: Related Workmentioning

confidence: 99%

A Scalogram-Based CNN Approach for Audio Classification in Construction Sites

Scarpiniti,

Parisi,

Lee

2023

Applied Sciences

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The automatic monitoring of activities in construction sites through the proper use of acoustic signals is a recent field of research that is currently in continuous evolution. In particular, the use of techniques based on Convolutional Neural Networks (CNNs) working on the spectrogram of the signal or its mel-scale variants was demonstrated to be quite successful. Nevertheless, the spectrogram has some limitations, which are due to the intrinsic trade-off between temporal and spectral resolutions. In order to overcome these limitations, in this paper, we propose employing the scalogramas a proper time–frequency representation of the audio signal. The scalogram is defined as the square modulus of the Continuous Wavelet Transform (CWT) and is known as a powerful tool for analyzing real-world signals. Experimental results, obtained on real-world sounds recorded in construction sites, have demonstrated the effectiveness of the proposed approach, which is able to clearly outperform most state-of-the-art solutions.

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Section: Related Workmentioning

confidence: 99%

A Scalogram-Based CNN Approach for Audio Classification in Construction Sites

Scarpiniti,

Parisi,

Lee

2023

Applied Sciences

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“…In the literature, there is a growing number of examples of such approaches, mainly in the construction industry. It most commonly concerns excavators [4,10,11,12], loaders [5,12,13], or compactors [8]. In the mining industry, such analyses primarily pertain to the operation of loaders (openpit mining) or shearers [6,16].…”

Section: Introductionmentioning

confidence: 99%

Rozpoznawanie czynności maszyn z użyciem metody grupowania

KĘSEK,

OGRODNIK,

PODOBIŃSKA-STANIEC

2023

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Rozpoznawanie czynności realizowanych przez maszyny jest bardzo istotne dla porównywania i analizy wydajności poszczególnych maszyn, potrzeb konserwacji maszyn oraz automatycznego monitorowania postępu prac. Dodatkowo, może być ono podstawą do optymalizacji realizowanych procesów produkcyjnych. W niniejszym artykule przedstawiono próbę wykorzystania algorytmów grupowania obiektów do rozpoznawania rodzaju aktywności kompleksu urabiającego. Do tego celu użyto danych pochodzących z procesu produkcyjnego oraz algorytmu k-means. Przybliżono także najpowszechniejsze algorytmy grupowania obiektów. Wyniki oraz zaprezentowany sposób przeprowazania analizy pokazują, że taki sposób postępowania może być z powodzeniem wykorzystywany w praktyce.

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Constructing an Audio Dataset of Construction Equipment from Online Sources for Audio-Based Recognition

Cited by 2 publications

References 38 publications

A Scalogram-Based CNN Approach for Audio Classification in Construction Sites

A Scalogram-Based CNN Approach for Audio Classification in Construction Sites

Rozpoznawanie czynności maszyn z użyciem metody grupowania

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