## 1 Introduction
Indeed, it is possible to figure the human speech production system out as a control system, where the plant dynamics are related to the vocal tract characteristics, while the constriction and airflow mechanisms are the biological counterparts of the controller. In this sense, the analogy with structural vibrations is straightforward. Some other examples of contamination between the speech processing techniques and structural health monitoring include some recent works on Wavelet Levels (WLs), on Hilbert-Huang Transform (HHT), and on Empirical Mode Decomposition (EMD) [13; 14]. Other proposals include well-known techniques, such as the Continuous Wavelet Transform (CWT), the Unscented Kalman Filter (UKF), and the Blind Source Separation (BSS) [5]. By far, the cepstrum [15] has been the main contribution to the field, being often coupled with other techniques, such as autoregressive moving-average (ARMA) models [16] or directly applied for the cepstral editing of signals [17; 18]; an extensive review of the uses of cepstral features for the monitoring of mechanical (non-biological) vibrations can be found in [19]. Mel-Frequency Cepstral Coefficients (MFCCs) are an example of low-dimensional, fixed dimension feature vectors defined into a cepstral framework, which are very effective in speech processing and already successfully tested as DSFs for mechanical systems [20; 21] and subsequently refined [22]. MFCCs proved to be apt in detecting damage in experimental and numerically simulated data for simpler structural elements and in more complex cases, such as historic masonry buildings. However, this feature is not free of issues; one of the major problems of MFCCs is how they can be swayed by additive noise with relative ease. Indeed, log-mel-amplitudes are influenced by the low-energy component of the signal and this is more strongly evident in the first MF cepstral coefficient. Similarly to the MFCCs, the Teager-Kaiser Energy Cepstral Coefficients (TECCs) were first introduced in the ambit of Speech Recognition in 2005 by [PERSON] et al. [23], relying on the [PERSON] definition of the Teager Energy Operator [24]. The process for their extraction is two-stepped. Firstly, the signal is filtered through a dense non-constant-Q Gammatone filterbank (details will follow shortly), which replaces the triangular filters utilised in the standard MFCCs approach. This initial step is shared with other closely related alternatives, such as, specifically, the Gammatone Cepstral Coefficients (GTCCs) [25], which have also been tested here in this work. Secondly, the short-time average of the output of the previously mentioned Teager-Kaiser Energy (TKE) Operator is computed. A deeper discussion for both MFCCs and TECCs is postposed to the respective subsections; it is important to remark here how the proposed TECC-based method relies on the TKE operator, which, in turn, is the basis of several time-frequency analysis approaches for the estimation of instantaneous amplitude and frequency [26]. These techniques already proved to be efficient in other fields of signal processing apart speech analysis and very recently they have been successfully applied to Structural Health Monitoring. For instance, the TKE has been combined with deep belief networks for the fault diagnosis of reciprocating compressor valves [27] and with least-squares support vector machine (LS-SVM) classifiers to detect bearing fault [28]. Nevertheless, its use is still very limited and few applications are reported in the literature. The proposed TECC-based approach has been tested on two numerical and one experimental case studies. The first numerical case is a Finite Element (FE) model that represents the Santa Maria and San Giovenale Cathedral bell tower in Fossano, Italy, which is a structure of relevant importance for cultural heritage and it has been deeply investigated in the literature (for instance, see [29]).