Deep recurrent neural networks for audio classification in construction sites

Document Type

Conference Proceeding

Publication Date

1-24-2021

Abstract

In this paper, we propose a Deep Recurrent Neural Network (DRNN) approach based on Long-Short Term Memory (LSTM) units for the classification of audio signals recorded in construction sites. Five classes of multiple vehicles and tools, normally used in construction sites, have been considered. The input provided to the DRNN consists in the concatenation of several spectral features, like MFCCs, mel-scaled spectrogram, chroma and spectral contrast. The proposed architecture and the feature extraction have been described. Some experimental results, obtained by using real-world recordings, demonstrate the effectiveness of the proposed idea. The final overall accuracy on the test set is up to 97% and overcomes other state-of-the-art approaches.

Publication Source (Journal or Book title)

European Signal Processing Conference

First Page

810

Last Page

814

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