## 4 Training Data Format  
In coherence to use CNNs as the main form of our NN, the time series had to be converted into images. However, using images of time series in the time domain would be a bad decision, as the range of values in our dataset is large, characterized also by large outliers (maximum and minimum values) in comparison with the mean value of the time histories being around zero. This large range in scale can be seen in Figure 4, between (a) and (i). Moreover, the variation in values is also present between the different categories of the time series (Ambient Response, EQ excitation, Response under EQ). Specifically, the amplitude of time series used by the NN model varies as follows:  
* Ambient Response: \([-7.380213\times 10^{-5},7.189604\times 10^{-5}]\) (g)
* EQ excitation: \([-3.54141,5.57502]\) (m/sec\({}^{2}\))
* Response under EQ: \([-19.02244,20.79666]\) (m/sec\({}^{2}\))  
Consequently, all time histories undertook a transformation from the time domain to the frequency domain. To achieve this, spectrograms representing both amplitude and phase information were derived for each signal in the time domain. As a result, two images were generated for each signal type, shifting the problem from a two-image input--one-image output to a two-set of two-image input--one-set of two-image output scenario (illustrated in Figure 6). The parameters used for the short-time Fourier Transform across the entire dataset were as follows: (a) 400 discrete Fourier transform (DFT) points, (b) a sampling rate of 100 Hz, (c) a 6-sample overlapping window applied between adjacent segments, and (d) an 8-point symmetric Hann window used for segmenting the signal and applying windowing. Subsequently, the maximum and minimum values across the entire dataset were identified and employed to normalize all input and output categories uniformly. The magnitude spectrograms are presented in decibels (dB), ranging from \(-380\) to \(+40\) dB on a black-to-white color scale, while the phase spectrograms are represented in degrees, spanning from \(-\pi\) to \(+\pi\) degrees also on a black-to-white color scale. The training and validation datasets consist of images with dimensions \(2997\times 201\) pixels, saved in.png format. These images are grayscale, meaning that each pixel is represented  
Figure 6: A sample of input/output set of images for training, with (a) Ambient response phase (input), (b) Ambient response magnitude (input), (c) EQ excitation phase (input), (d) EQ excitation magnitude (input), (e) EQ response phase (output), and (f) EQ response magnitude (output). by a single integer within the range of [0, 255]. To optimize storage while preserving the necessary resolution, an 8-bit depth was selected. An 8-bit image offers a dynamic range of 48.13 dB, distinct from the time series data discussed later in the \"_Numerical investigation--results_\" section. TensorFlow neural network models typically employ 32-bit variables, occasionally dipping to 16-bit variables in mixed precision mode. For our experiments, we opted for the default 32-bit precision to ensure stability. Due to the dataset's size, direct RAM loading is unfeasible. Consequently, we converted the images into TFRecords, a binary file format designed for efficient storage and processing within TensorFlow. TFRecords serialize data, transforming them into a sequence of bytes that can be effortlessly transmitted over networks or stored on disks. This format proves invaluable when handling substantial datasets, enabling efficient data streaming, shuffling, and random access. TFRecords accommodate various data types, including images, audio, text, and numerical data. They find widespread applications in TensorFlow for data preprocessing, augmentation, and input pipeline optimization. In total, the training dataset occupies approximately 342 GB, while the validation dataset consumes around 114 GB. To prepare the data for neural network input, standard scaling procedures are performed.