## 1 Introduction  
Damage detection and Structural Health Monitoring (SHM) indicate the field of engineering that is involved in the assessment of the structural integrity for civil, mechanical, and aerospace applications, among others [1]. More specifically, vibration-based SHM [2] deals with the structural response, as recorded by means of a sensor network, to extract damage-related features from its free or forced oscillations. In this context, recent years have seen a continuous surge of interest in Machine Learning (ML) algorithms, as they are perfectly suited for damage detection [3], if this is seen from a Pattern Recognition/Novelty Detection standpoint [4]. The concept is simple: by knowing the current state of a system (which is usually undamaged, but can be indistinctly previously damaged, as long as it is stable and not dramatically changing under \"normal\" conditions), a baseline can be defined. This is performed by training a \"normality\" model via ML techniques over the features that were extracted from the known set of data. Subsequently, the structural response from other scenarios can be labelled as divergent from this model or not by regarding the value of a distance metric between its current response and the baseline model. One can refer to the previously cited book of [PERSON] for a detailed description of the basics of ML for SHM [1]. Some damage-related features (also known as damage sensitive features, DSFs) are needed for training and testing a Machine Learning algorithm. Eventually, DSFs need to be as low in dimensionality as possible, since a very large number of cases is required for the training phase, preserving the relevant information while discarding unrelated effects. However, feature selection is a concern much broader than its SHM applications. One of the fields where it has seen a major development is in speech, speaker, and language recognition. Virtual assistants, human-like speech synthesis, and audio indexing are all examples of ML applied to Speech Processing for Artificial Intelligence (AI) purposes. Other applications are countless: automatic transcription to text and conversational human-machine interfaces are the most obvious uses of speech recognition. Speaker recognition, i.e., the process of automatically predicting the identity of the speaker by a given utterance, is used for voice biometric authentication, telephone-based services, speaker diarisation, and forensic activities. Finally, language recognition finds its main applications in emergency call routing, spoken language translation, and audio surveillance, to cite a few. Speaker and language recognition rely on similar techniques, as they mostly share the same problems: preprocessing the signal, extracting its relevant features, and representing them by means of statistical models. There are no conceptual impediments for the application of the related techniques for vibration-based SHM if acceleration time histories (THs) from the structure of interest are considered instead of audio samples. As for speaker recognition, distinctive traits, related to specific phenomena of interest, have to be spotted and discerned from uninteresting effects. On the one hand, mechanical vibrations and biomedical signals show the same issues, such as changing content along time (both frequency- and amplitude-wise) during dynamic excitation, which cannot be properly depicted by means of Discrete Fourier Transform (DFT) [5], and thus require joint time-frequency analysis techniques for a two-dimensional, time-vs-frequency representation [6; 7], such as the one obtained via wavelet analysis [8; 9; 10; 11]. On the other hand, speech signals present some difficulties--such as different duration between samples, unknown input, and high level of non-stationarity--which are generally much less impelling for civil and mechanical structures rather than in biological systems [12].