## 1 Introduction
The latter are widely used in different acoustic applications, for example, audio engineering, vehicle industry, acoustic material, and environmental acoustics [39; 40; 41; 42]. In building acoustics, a convolutional neural networks model was used to classify inter-floor noise by recording different noise sources for 24 h in a household [43]. Moreover, a developed ANN model was applied on sandwich partition panels to forecast their sound insulation, weighted airborne sound reduction indices \(R_{w}\) and sound transmission class \(STC\) values [26]. The results were reasonable, and the model was able to predict \(R_{w}\) and \(STC\) within \(\pm 3\) dB differences. A similar study was conducted using measurements related to wooden windows to predict \(R_{w}\) using technical parameters [32]. However, the estimations in the two previous studies were only dealing with single-number quantities (SNQ) without estimation of full-spectrum insulation curves. Taking the estimations of ANN a step further, detailed predictions of airborne insulation in 1/3-octave bands were acquired for masonry walls [25]. The data consisted of laboratory measurements performed on 34 brick walls. Despite the estimated values agreed with measured ones, the study focused on one monolithic structure. To bridge this gap, previous research published by the authors [44] used ANN to estimate the sound insulation behavior of different lightweight wooden floors in frequency bands (50 Hz-5 kHz). 67 insulation measurements were used to develop the model by taking only two variables (material thickness and their installation order). The model demonstrated reasonable results regarding SNQ with a maximum difference of 1 dB in estimation of \(R_{w}\). However, the accuracy decreased with deviations up to 9 dB in prediction of \(Ln,w\) (normalized weighted impact sound pressure level). This study had the sample size limitation, but showed good potential for predicting sound insulation data. A complementary study was conducted [45] using 252 standardized laboratory measurements performed on lightweight wooden floors with construction parameters, such as material type, material thickness and density, area of the floor structure, etc. When more data are included, a better accuracy in predicting \(L_{n,w}\) is achieved, within 5 dB variations. However, the variance in forecasting of \(R_{w}\) increases up to 2 dB, but still represents reasonable results. The study then conducted an attribution analysis to find out the important parameters in forecasting sound insulation curves. However, the literature review at the time of this study did not find any study that attempted to develop an ANN model to estimate sound insulation curves of lightweight wooden facades. The scope of this paper is to develop an ANN model to forecast airborne insulation curves of facade walls. The isolation data comprises laboratory measurements that are implemented on various lightweight wooden facades. This study considers specific structures for external building partitions. The measurements used are relating to complete wall systems without considering the presence of doors, windows, and small openings in the facades. Finally, a sensitivity analysis is conducted to explore the influence and the significance of parameters on the prediction of airborne sound insulation.