# A Scalogram-Based CNN Approach for Audio Classification in Construction Sites
## 2 Related Work
Similarly, [PERSON]. [39] exploited visual and kinematic features, while [40] utilized location data from a GPS and a vision-based model to detect construction equipment. Finally, a multimodal audio-video approachwas presented in [41], based on the use of different correlations of visual and auditory features, which has shown an overall improvement in detection performance. In addition, [PERSON] et al. in [42] exploited CNN architectures to automatically detect collision hazards between construction equipment. Similarly, the work in [43] presented a critical review of recent DL approaches for fully embracing construction workers' awareness of hazardous situations in construction sites by the employment of auditory systems. Most of the DL approaches described in this section work on the spectrogram extracted from audio signals or some variants, such as the mel-scaled spectrogram. However, the idea of exploiting different time scales (which is an intrinsic property of audio signals) can be used to improve the overall accuracy of such methodologies. For this purpose, the use of scalograms can be recommended. In fact, while spectrograms are suitable for the analysis of stationary signals providing a uniform resolution, the scalogram is able to localize transients in non-stationary signals. Recently, in fact, Ref. [44] introduced a wavelet filter bank for the audio scene modeling task. A deep CNN fed by the scalogram of data outperformed the results provided by the mel spectrogram. However, differently from our approach, the work in [44] considers a scalogram of smaller size and a simpler CNN architecture. The work in [45] adopted scalograms for removing background noise in the fault diagnosis of rotating machinery, obtaining excellent experimental results. However, differently from our approach, given the specific nature of the considered sounds, the authors used a low sampling frequency and frame size, resulting in a very small scalogram size (\(64\times 64\) pixels). Interestingly enough, [45] considers three different methods to obtain the scalograms, including the CWT. No significant statistical differences have been observed between such methods. In addition, a couple of papers used scalograms also for audio scene classification purposes [46; 47]. Both of these works showed very good results when compared to previous solutions. As a matter of fact, the use of the scalogram results in a general improvement in performance as highlighted in all these works. Specifically, the work in [46] exploits a pre-trained CNN to extract, at a specific architecture-dependent layer, useful features to be used by a subsequent linear SVM classifier for the identification of ten environmental categories. This work also uses AlexNet but, differently from our approach, it does not train the CNN layers and does not adopt fully connected layers as a classifier. The work in [47] again uses a pre-trained AlexNet or VGG16/19 nets to extract meaningful features, but, differently, it exploits a Bidirectional Gated Recurrent Neural Network followed by a highway layer to classify fifteen classes. Differently from our approach, the authors of [47] adopt an early data fusion technique by feeding the proposed model with a three-channel image composed of a spectrogram, a scalogram extracted with the Bump wavelet, and a scalogram obtained with the Morse wavelet. However, the high computational cost of this approach, compared with the proposed one, makes it not very suitable for working with the construction site sounds, where only a small number of classes are present.