# A Scalogram-Based CNN Approach for Audio Classification in Construction Sites
## 2 Related Work  
In the digital era, great and increasing attention has been devoted to research on automated methods for real-time monitoring of activities in construction sites [15; 24; 25]. These modern approaches are able to offer better performance with respect to the most traditional techniques, which are typically based on manual collection of on-site work data and human-based construction project monitoring. In fact, these activities are typically time-consuming, inaccurate, costly, and labor-intensive [13]. In the last years, the literature related to applications of deep learning techniques to the construction industry has been continuously increasing [26; 27]. In particular, many works have been published describing proper exploitation of audio data [5; 16]. The work of [PERSON] et al. in [28] was one of the first attempts in this direction. They introduced an algorithm based on the processing of acoustic data for the classification of four representative excavators. This approach is based on some acoustic statistical features. Namely, for the first time the short frame energy ratio, concentration of spectrum amplitude ratio, truncated energy range, and interval of pulse (i.e., the time interval between two consecutive peaks) were developed in order to characterize acoustic signals. The obtained results were quite effective for this kind of source; however, no other types of equipment were considered. Paper [29] proposed the construction of a dataset of four classes of equipment and tested several ML classifiers. The results obtained in this work were aligned to those shown in [5], which compared and assessed the accuracy of 17 classifiers on nine classes of equipment. These two papers work on both temporal and spectral features extracted from audio signals. Similarly, Ref. [30] compared some ML approaches on five input classes by using a single in-pocket smartphone, obtaining similar numerical results. [PERSON] et al. [14] proposed an SVM classifier. After an iterative neighborhood component analysis selector chooses the most significant features extracted from audio signals, this classifier produces an effective accuracy on two experimental scenarios. Moreover, [PERSON] et al. [7] proposed a sound localization framework for construction site monitoring able to work in both indoor and outdoor scenarios. [PERSON] et al. [31] proposed a deep CNN-based approach for the classification of five pieces of construction site machinery and equipment. This customized CNN is fed by the STFT spectrograms extracted from different-sized audio chunks. Similarly, [PERSON] et al. [32] proposed an approach for multiple-equipment activity recognition using CNNs, tested on both synthetic and real-world equipment sound mixtures. Different from [31], this work implements a data augmentation method to enlarge the used dataset. Moreover, this model uses a moving mode function to find the most frequent labels in a period ranging from 0.5 to 2 s, which generates an acceptable output accuracy. The idea to join different output labels inside a short time period was also exploited in [33; 34], which implement a Deep Belief Network (DBN) classifier and an Echo State Network (ESN), respectively. [PERSON] et al. in [35] applied CNNs and RNNs to spectrograms for monitoring concrete pouring work in construction sites, while [PERSON] et al. in [6] used a convolutional RNN (CRNN) for activity monitoring. Moreover, [PERSON] et al. in [36] used a similar DL approach for a denoising application in construction sites. On the other hand, [PERSON] et al. [37] proposed an approach, called DesPatNet25, which extracts 25 feature vectors from audio signals by using the data encryption standard cipher and adopts a k-NN and an SVM classifier to identify seven classes. Additionally, some other approaches also fused information from two different modalities. For example, the work in [38] used an SVM classifier by combining both auditory and kinematics features, showing an improvement of about 5% when compared to the use of only individual sources of data.