Simone Di Tanna

Bio: Simone Di Tanna is an academic researcher from Sapienza University of Rome. The author has contributed to research in topics: Test set & Spectrogram. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Deep Belief Network based audio classification for construction sites monitoring

Abstract: In this paper, we propose a Deep Belief Network (DBN) based approach for the classification of audio signals to improve work activity identification and remote surveillance of construction projects. The aim of the work is to obtain an accurate and flexible tool for consistently executing and managing the unmanned monitoring of construction sites by using distributed acoustic sensors. In this paper, ten classes of multiple construction equipment and tools, frequently and broadly used in construction sites, have been collected and examined to conduct and validate the proposed approach. The input provided to the DBN consists in the concatenation of several statistics evaluated by a set of spectral features, like MFCCs and mel-scaled spectrogram. The proposed architecture, along with the preprocessing and the feature extraction steps, has been described in details while the effectiveness of the proposed idea has been demonstrated by some numerical results, evaluated by using real-world recordings. The final overall accuracy on the test set is up to 98% and is a significantly improved performance compared to other state-of-the-are approaches. A practical and real-time application of the presented method has been also proposed in order to apply the classification scheme to sound data recorded in different environmental scenarios.

Classification of steel using laser-induced breakdown spectroscopy combined with deep belief network

Abstract: The identification of steels is a crucial step in the process of recycling and reusing steel waste. Laser-induced breakdown spectroscopy (LIBS) coupled with machine learning is a convenient method to classify the types of materials. LIBS can generate characteristic spectra of various samples as input variable for steel classification in real time. However, the performance of classification model is limited to the complex input due to similar chemical composition in samples and nonlinearity problems between spectral intensities and elemental concentrations. In this study, we developed a method of LIBS coupled with deep belief network (DBN), which is suitable to deal with a nonlinear problem, to classify 13 brands of special steels. The performance of the training and validation sets were used as the standard to optimize the structure of DBN. For different input, such as the intensities of full-spectra signals and characteristic spectra lines, the accuracies of the optimized DBN model in the training, validation, and test set are all over 98%. Moreover, compared with the self-organizing maps, linear discriminant analysis (LDA), k-nearest neighbor (KNN) and back-propagation artificial neural networks (BPANN), the result of the test set showed that the optimized DBN model performed second best (98.46%) in all methods using characteristic spectra lines as input. The test accuracy of the DBN model could reach 100% and the maximum accuracy of other methods ranged from 62.31% to 96.16% using full-spectra signals as input. This study demonstrates that DBN can extract representative feature information from high-dimensional input, and that LIBS coupled with DBN has great potential for steel classification.