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Smart audio signal classification for tracking of construction tasks
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Orientador(es)
Resumo(s)
This paper presents a model for sound classification in construction that leverages a unique combination of Mel spectrograms and Mel-Frequency Cepstral Coefficient (MFCC) values. This model combines deep neural networks like Convolution Neural Networks (CNN) and Long short-term memory (LSTM) to create CNN-LSTM and MFCCs-LSTM architectures, enabling the extraction of spectral and temporal features from audio data. The audio data, generated from construction activities in a real-time closed environment is used to evaluate the proposed model and resulted in an overall Precision, Recall, and F1-score of 91%, 89%, and 91%, respectively. This performance surpasses other established models, including Deep Neural Networks (DNN), CNN, and Recurrent Neural Networks (RNN), as well as a combination of these models as CNN-DNN, CNN-RNN, and CNN-LSTM. These results underscore the potential of combining Mel spectrograms and MFCC values to provide a more informative representation of sound data, thereby enhancing sound classification in noisy environments.
Descrição
Mannem, K. R., Mengiste, E., Hasan, S., Soto, B. G. D., & Sacks, R. (2024). Smart audio signal classification for tracking of construction tasks. Automation In Construction, 165, 1-13. Article 105485. https://doi.org/10.1016/j.autcon.2024.105485 --- This research benefited from utilizing resources available at the Core Technology Platforms (CTP) of New York University Abu Dhabi (NYUAD). In particular, the algorithms developed in this study used the research computing services at NYUAD's Center for Research Computing and High-Performance Computing (HPC). This work has benefited from the collaboration with the NYUAD Center for Interacting Urban Networks (CITIES), funded by Tamkeen under the NYUAD Research Institute Award CG001 and the Sand Hazards and Opportunities for Resilience, Energy, and Sustainability (SHORES) Center, funded by Tamkeen under the NYUAD Research Institute Award CG013.
Palavras-chave
Activity tracking Audio CNN LSTM Mel spectrograms MFCC Sound Control and Systems Engineering Civil and Structural Engineering Building and Construction SDG 9 - Industry, Innovation, and Infrastructure