## 3 Experimental Methodology
### Synthetic Domestic Acoustic Database  
Synthesizing our own database allows the production of data that address issues commonly faced in a certain environment and recreates scenarios that could occur in real life. This includes noisy environments, as well as various source-to-receiver distances. Furthermore, this also provides the exact locations of the sound sources. For this work, the generation of the synthetic database was done based on a 92.81 m\({}^{2}\) one-bedroom apartment modelled after the Hebrew Senior Life Facility [47], illustrated in Figure 2. We assumed a 3 m height for the ceiling. Multi-channel recordings were aimed for; hence, microphone arrays were placed on each of the four corners of the six \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Year** & **Size (MB)** & **Input Size** & **Layers** & **Parameters** & **5\% ER** \\ \hline AlexNet [37] & 2012 & 227 & 227 \(\times\) 227 & 8 & 62.3 million & 16.4\% \\ GoogleNet [38] & 2014 & 27 & 224 \(\times\) 224 & 22 & 4 million & 6.70\% \\ ResNet [39] & 2015 & 167 & 224 \(\times\) 224 & 101 * & 25 million & 3.57\% \\ Inception-ResNet [40] & 2017 & 209 & 299 \(\times\) 299 & 164 * & 55.9 million & \\ Xception [41] & 2016 & 85 & 299 \(\times\) 299 & 71 & 22.9 million & \\ SqueezeNet [42] & 2016 & 5.2 & 227 \(\times\) 227 & 18 & 1.25 million & \\ VGGNet [43] & 2014 & 515 & 224 \(\times\) 224 & 41* & 138 million & 7.30\% \\ LeNet [44] & 1998 & & 7 & 60,000 & 28.2\% \\ \hline \hline \end{tabular}  
* Number of layers may vary depending on the version used.  
\end{table} 
Table 1: General Comparison Summary between Pre-trained CNN Models. rooms at 0.2 m below the ceiling. This produced four recordings, one from each of the receiver nodes. Accordingly, the microphone arrays were composed of four linearly arranged omnidirectional microphones with 5 cm inter-microphone spacing (\(n\)), as per the geometry provided in Figure 3, where \(d\) refers to the distance from the sound source to the microphones. Dry samples are taken from Freesound (FSD50K) [48], Kaggle [49], DESED Synthetic Soundscapes [50], and Open SLR [51], depending on the audio class. Due to the variations in sampling frequency, some of the audio signals were down sampled to 16 kHz for  
Figure 3: Microphone array geometry for a single node: four linearly spaced microphones. Figure 2: Floorplan of one-bedroom apartment used as acoustic environment for the synthetic database, dimensions in meters [47]. uniformity purposes. The room dimensions, source and receiver locations, wall reflectance, and other relevant information, were then used in order to calculate the impulse response for each room using the image method, incorporating source directivity [52]. This was then convolved with the sounds, specifying their location, in order to create the synthetic data. The data generated included clean signals, as well as different types of noisy signals, including: children playing, air conditioner, and street music, added at three different SNR levels: 15 dB, 20 dB, and 25 dB. The duration of each audio signal was uniformly kept at 5-s, as this was found to provide satisfactory time resolution for the sound scenes and events detected in this work. Table 2 describes this dataset. This data was curated such that the testing data consisted of one noise level for each node. Any instances of the data contained in the test set were then removed from the training data. The testing set content is summarized for a specific sound being recorded at four nodes:  
* Node 1: Clean Signal with 15 dB Noise
* Node 2: Clean Signal with 20 dB Noise
* Node 3: Clean Signal with 25 dB Noise
* Node 4: Clean Signal  
This ensures that even when the same sound is being recorded by the four nodes present, it reduces the chances of biasing through the addition of different types of noise at different SNR levels.