Deep Recurrent Neural Networks for Audio Classification in Construction Sites
Michele Scarpiniti, Danilo Comminiello, Aurelio Uncini, Yong-Cheol Lee
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.