## 1 Introduction  
Over the last years, retrieving information about the behavior of civil structures under different scenarios is a task of paramount importance, since an inadequate design may not be able to stand the normal vibrations produced by the operational conditions, causing economic, structural, and human catastrophes. In this regard, the estimation of modal parameters, such as natural frequencies and damping ratios, of civil structures has become an important tool because they can reflect their dynamic behavior under natural and artificial excitations. Modal parameters are widely used in applications such as seismic design for civil and earthquake engineering [1; 2; 3], vibration control [4], condition and damage assessment [5; 6; 7], and development or update of analytical models [8; 9]. Hence, their accurate estimation allows providing more reliable and suitable information of the structure, improving the performance and results of the aforementioned applications. In order to obtain the modal parameters of a civil structure, it must be excited either using human-made equipment or naturally-produced vibrations. The former involves the utilization of hammers, drop weight or shakers; however, the in-test structure must be closed in order to allow the realization of the tests. Moreover, the energy available from these sources limits their utilization in real-life structures. For these reasons, the utilization of natural sources (e.g., vibrations produced by wind, traffic loadings, and small seismic waves) is usually preferred because: (i) they can properly excite a civil structure regardless of its size; (ii) the normal operation is not interrupted; and (iii) they are the typical vibrations that every civil structure is subjected during its service period [10]. In this regard, as these types of sources are of a non-stationary nature, producing a noisier response than the one obtained using human-made equipment, the modal parameter identification (MPI) becomes a challenging task, since the noise levels compromise the results accuracy [11] if classical algorithms are employed. During the last two decades, a branch of algorithms have been specifically designed for performing the MPI of civil structures using ambient vibrations, including fast Fourier transform (FFT)-based methods [12; 13], the autoregressive model-based models [14; 15], random decrement technique (RDT) [16], natural excitation technique combined with the eigensystem realization algorithm (NExT-ERA) [17; 18], and stochastic subspace identification (SSI) methods [19; 20], among others. Despite the excellent results obtained using the abovementioned techniques, some difficulties remain unsolved. For example, FFT-based methods have problems when they process non-stationary and noisy signals; further, the detection of closely-spaced modes is troublesome. The use of autoregressive models requires an appropriate selection of the algorithm to estimate the model coefficients, since the accuracy of the estimated results relies on the coefficients [21]. Regarding RDT, the accuracy of the technique might be affected when non-stationary signals are processed [22]. On the other hand, although SSI and NExT-ERA can deal better with noise-corrupted signals, the detection of closely-spaced modes usually requires selecting a higher order in the estimated model, which might generate false or spurious frequency components [23; 24], making necessary the utilization of further stages to detect and separate the real frequencies from the fictitious ones. In order to lessen the abovementioned difficulties, other advanced approaches such as the wavelet transform (WT) and the empirical mode decomposition (EMD) have been explored to perform the MPI of civil structures. WT has become an important tool in MPI, because it can be used to analyze nonlinear and non-stationary signals, characteristics found in the measured signals [25; 26]; however, a carefully selection of the mother wavelet and its parameters must be done in order to ensure a good time-frequency resolution.