## 2 Materials and Methods
### Signal Transformation and Datasets Generation  
State of the art technology is based on converting an acoustic [38; 39] or vibration [40] signal into the spectrogram before feeding it into the neural network. While designing the CNN model for the classification of manufactured products based on their sound expression, various approaches should be considered with some options to begin with. One option is to use a sound recording as a vector input of the neural network. Since the spectrogram represents the signal in the frequency domain and takes an image format, currently, the best results in the field of the classification of sound recordings are achieved by networks working with input data in the form of images; thus, we decided to try another option to convert a sound recording into an image. The highest success rate of final classification is usually achieved by transforming the signal into a spectrogram by short-term Fourier transform [41], Mel spectrogram [42] or wavelet spectrogram [43]. Due to the high tack of the HVAC's quality control process, we used the Gabor transform to make spectrograms [44] as it is considerably faster than the comparable algorithm. The created spectrograms were then scaled according to the  
Figure 5: The ramp course of the testing frequencies during the measuring process. Mel scale, which is a scale of pitches that seem to listeners to have equal distance between pitches [45]. Among the transformation parameters, the window length is especially important. With use of this parameter, we can increase the time resolution of the spectrogram while decreasing its frequency resolution, or vice versa. A high value of the window length will highlight long-lasting sound expressions of the original sound signal in a spectrogram, while lower values tend to highlight quick impulses [46]. To avoid omitting any type of sound expression, we will transform the signal into two different spectrograms. Figure 6 shows a spectrogram for the nominal values of EN. The images, which will be processed in the neural network, contain only a spectrogram with no axes. Only two of the four aforementioned HVAC signal components (from Table 2) are barely visible, so we have outlined them using red circles. A complete list of transforming parameters is shown in Table 3.  
All created spectrograms are represented as images with a resolution of 512 \(\times\) 512 px. While the grey colour map was used for creating the spectrograms, the saved images have only a single channel. Using greyscale images requires only a quarter of memory compared to colour images; thus, further processing is considerably faster [47], which is important for the application. The right choice of using greyscale representation was verified with basic experiments, which showed that the use of colour spectrograms did not result in any increase in classification accuracy, but rather vice versa. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Spectrogram 1** & **Spectrogram 2** \\ \hline Window length [s] & 0.35 & 0.05 \\ \hline Oversampling rate & 4 & 4 \\ \hline Frequency bins & 512 & 512 \\ \hline Window type & Hanning & Hanning \\ \hline Min. frequency [Hz] & 20 & 20 \\ \hline Max. frequency [Hz] & 8000 & 8000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Input parameters for creating both spectrogram types by Gabor transform. Figure 6: A sample of sound signal with the nominal level of environmental noise (EN) transformed into spectrogram 1 (**a**) and spectrogram 2 (**b**) with pointed HVAC signal components. As we mentioned in the introduction, this paper is devoted to finding and describing the influence of EN on the classification accuracy of the decision-making algorithm based on the CNN. To fulfil the task, we simulated measurements inside superior anechoic chambers that damp more EN. This was done by generating another five datasets. Each dataset simulated a measurement inside an anechoic chamber that could attenuate 2 dB of EN more than the previous one.