## 2 Methods
The quantitative analysis is implemented for validating research hypotheses (using statistical methodologies) aimed at determining the correlations between acoustic comfort and other working variables. Concurrently, the research focused on determining the best predictor for acoustic comfort and the decision-making factors for sustainable material selection for passive noise control. The hypotheses of this applied research were as follows:  
**H1. **: _There is a positive correlation between the acoustic absorption coefficient and the improvement in acoustic comfort;_
**H2. **: _Material properties influence the relationship between the acoustic absorption coefficient and acoustic comfort, having a significant impact on the outcomes;_
**H3. **: _There is a positive correlation between the material class and the improvement in the acoustic absorption class;_
**H4. **: _There are positive correlations between the testing frequency and the acoustic performance of the materials._  
The database acquired has facilitated the categorization of sound-absorption materials based on their efficacy in dissipating sound energy, as determined by their absorption coefficient across various sound frequencies or octave bands. For instance, a material displaying an absorption coefficient of 0.50 at the 1000 Hz 1/3 octave band implies that 50% of the incident energy was absorbed within that frequency range. The fundamental idea for enhancing the performance of optimal noise absorption involves achieving an absorption coefficient that approaches 1 across a broad frequency spectrum. Based on the absorption coefficient obtained from measurements at various frequencies within the human audible spectrum, materials are classified into distinct acoustic absorption classes according to their absorption coefficients, as detailed in Table 2.  
Starting with the abovementioned particularities and according to the aim and objectives of the paper, the variables used in the research are presented in Table 3, together with a short technical description taken into consideration for this research and SPSS 29.0 (licensed) software codification (Figure 1) for statistical analysis. The data were collected during the measurement process for each material sample, and the research database comprises 248 records from 51 types of materials grouped into 6 class categories. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Absorption Acoustic Class** & \(\alpha\)**-Sound Absorption Coefficient** & **Observations** \\ \hline A & 0.90–0.95; 1.00 & Very high-performance \\ \hline B & 0.80; 0.85 & absorbent materials \\ \hline C & 0.60; 0.65; 0.70; 0.75 & Absorbent materials \\ \hline D & 0.30; 0.35; 0.70; 0.75 & \\ \hline E & 0.15; 0.20; 0.25 & \\ \hline Other class & 0.00; 0.05; 0.10 & Nonabsorbent materials \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classes of sound absorption depending on the sound absorption coefficient. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Variable Name** & **The Type of the Variable** & **Measure Unit** & **Limits Provided** & **SPSS Codification** \\ \hline
1 & Material & Categorial & - & - & 1 = aluminium with a core–aluminum honeycomb.51 = wood with formaldehyde resin (Figure 1) \\ \hline
2 & Class materials & Categorial & & 4 = Plastic/polymer-based materials, 5 = Textile materials, 6 = Elastic polymers \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Working variables. Figure 1 systematically integrates the classes of materials and their components along with the associated codes for the purpose of conducting a preliminary analysis of acoustic performance. The analysis of the sound absorption performance of the composite materials was based on the criteria outlined in Table 4.  
The aim is to achieve the direct conversion of sound wave energy upon incidence with the material surface into heat, thereby absorbing the energy.