# A Scalogram-Based CNN Approach for Audio Classification in Construction Sites
###### Abstract  
The automatic monitoring of activities in construction sites through the proper use of acoustic signals is a recent field of research that is currently in continuous evolution. In particular, the use of techniques based on Convolutional Neural Networks (CNNs) working on the spectrogram of the signal or its mel-scale variants was demonstrated to be quite successful. Nevertheless, the spectrogram has some limitations, which are due to the intrinsic trade-off between temporal and spectral resolutions. In order to overcome these limitations, in this paper, we propose employing the scalogramas a proper time-frequency representation of the audio signal. The scalogram is defined as the square modulus of the Continuous Wavelet Transform (CWT) and is known as a powerful tool for analyzing real-world signals. Experimental results, obtained on real-world sounds recorded in construction sites, have demonstrated the effectiveness of the proposed approach, which is able to clearly outperform most state-of-the-art solutions. automatic construction site monitoring (ACSM); environmental sound classification (ESC); deep learning; convolutional neural network (CNN); continuous wavelet transform (CWT); scalogram; audio processing +
Footnote †: journal: Medical  
managers to promptly identify severe and urgent problems in fieldwork and quickly react to unexpected safety and hazard issues [12; 13; 14; 15; 16]. ACSM systems are usually implemented by exploiting both machine learning (ML) and deep learning (DL) techniques [17]. Specifically, several ML approaches, including Support Vector Machines (SVMs), the k-Nearest Neighbors (k-NN) algorithm, the Multilayer Perceptron (MLP), random forests, Echo State Networks (ESN), and others, have already demonstrated their effectiveness in properly performing activity identification and detection in a construction site [5; 16]. However, DL approaches generally outperform ML-based solutions providing much improved results [6]. We expect that DL techniques including CNNs, Deep Recurrent Neural Networks (DRNNs) implemented with the Long Short-Term Memory (LSTM) cell, Deep Belief Networks (DBNs), Deep ESNs, and others can produce more suitable and qualified performances than ML ones for robustly managing construction work and safety issues. Approaches based on CNNs have demonstrated good flexibility and considerably convincing performance in these applications. In fact, CNNs exhibit advanced accuracy in image classification [18]. In order to meet the bi-dimensional format of images, the audio waveform can be transformed into a bi-dimensional representation by a proper time-frequency transformation. The main time-frequency representation used in audio applications is the spectrogram, i.e., the squared magnitude of the Short Time Fourier Transform (STFT) [19; 20]. The spectrogram is very rich in peculiar information that can be successfully exploited by CNNs. Instead of using the STFT spectrogram, in audio processing, it is very common to use some well-known variants, such as the constant-Q spectrogram, which uses a log-frequency mapping, and the mel-scale spectrogram, which uses the mel-scale of frequency to better capture the intrinsic characteristic of the human ear. Similarly, the Bark and/or ERB scales can be used, producing other variants of the spectrogram [21]. Although the spectrogram representation and its variants provide an effective way to extract features from audio signals, they entail some limitations due to the unavoidable trade-off between the time and frequency resolutions. Unfortunately, it is hard to provide an adequate resolution in both domains: a shorter time window provides a better time resolution, but it reduces the frequency resolution, while using longer time windows improves the frequency resolution but obtains a worse time resolution. Even if some solutions have been proposed to mitigate such an unwanted effect (such as the time-frequency reassignment and synchrosqueezing approach [22]), the problem can still affect the performance of deep learning methods.