Convolutional Neural Networks (CNNs) have been widely used in the field of audio recognition and classification, since they often provide positive results. Motivated by the success of this kind of approach and the lack of practical methodologies for the monitoring of construction sites by using audio data, we developed an application for the classification of different types and brands of construction vehicles and tools, which operates on the emitted audio through a stack of convolutional layers. The proposed architecture works on the mel-spectrogram representation of the input audio frames and it demonstrates its effectiveness in environmental sound classification (ESC) achieving a high accuracy. In summary, our contribution shows that techniques employed for general ESC can be also successfully adapted to a more specific environmental sound classification task, such as event recognition in construction sites.

A CNN approach for audio classification in construction sites / Maccagno, Alessandro; Mastropietro, Andrea; Mazziotta, Umberto; Scarpiniti, Michele; Lee, Yong-Cheol; Uncini, Aurelio. - 184:(2020), pp. 371-381. (Intervento presentato al convegno The 29th Italian Workshop on Neural Networks (WIRN 2019) tenutosi a Vietri sul Mare (SA); Italy) [10.1007/978-981-15-5093-5_33].

A CNN approach for audio classification in construction sites

Mastropietro, Andrea;Scarpiniti, Michele
;
Uncini, Aurelio
2020

Abstract

Convolutional Neural Networks (CNNs) have been widely used in the field of audio recognition and classification, since they often provide positive results. Motivated by the success of this kind of approach and the lack of practical methodologies for the monitoring of construction sites by using audio data, we developed an application for the classification of different types and brands of construction vehicles and tools, which operates on the emitted audio through a stack of convolutional layers. The proposed architecture works on the mel-spectrogram representation of the input audio frames and it demonstrates its effectiveness in environmental sound classification (ESC) achieving a high accuracy. In summary, our contribution shows that techniques employed for general ESC can be also successfully adapted to a more specific environmental sound classification task, such as event recognition in construction sites.
2020
The 29th Italian Workshop on Neural Networks (WIRN 2019)
deep learning; convolutional neural networks; audio processing; environmental sound classification; construction sites
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A CNN approach for audio classification in construction sites / Maccagno, Alessandro; Mastropietro, Andrea; Mazziotta, Umberto; Scarpiniti, Michele; Lee, Yong-Cheol; Uncini, Aurelio. - 184:(2020), pp. 371-381. (Intervento presentato al convegno The 29th Italian Workshop on Neural Networks (WIRN 2019) tenutosi a Vietri sul Mare (SA); Italy) [10.1007/978-981-15-5093-5_33].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1444275
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