common deep learning implementations use 32-bit inputs, we propose to evaluate the necessity of the conversion to integers for this encoding.

Spectrogram (SP) and Scalogram (SC)
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To complement the encodings described above, we propose to add to the study the more traditional time-frequency analysis. Since the spectro-temporal description of a time series provides 2-dimensional representations, it is customary in the literature to interpret these representations directly as images [@verstraete2017deep]. In this work, we propose two popular transformations: the spectrogram, resulting from the short-time Fourier Transform (STFT), and the scalogram, resulting from the Discrete Wavelet Transform (DWT). For more information on these transformations, the reader is referred to the book from [@boashash2015time].

Both the STFT and the CWT rely on the principle of convolving the time series with a windowing function. The difference between the two approaches is that for STFT, the window is fixed and the Fourier Transfrom is applied to the convolved signal, while for the CWT, the window is based on a wavelet function, whose scaling enables the exploration of the spectrum at different levels. For the STFT, it is therefore required to set three hyperparameters: the window type, its size and its temporal stride. The DWT 