# Low Complexity Deep Learning Framework for Greek Orthodox Church Hymns Classification
## 4 Discussion  
DL has emerged as a powerful tool in audio signal processing problems. In this work, CV and DL approaches are explored for the task of Greek Orthodox Church hypms identification for mobile applications. Due to the restrictions that are imposed by the low complexity requirement, only lightweight DL models are considered. Three custom CNNs and five SOTA DL models are applied to the task of Greek Orthodox Church hypms identification. The first two custom CNNs employ a conventional structure with both convolution layers and fully-connected layers, while the third one, Micro VGG, is introduced as an improvement of the previous two, incorporating the VGG architecture at a smaller scale. The five pre-trained CNNs are chosen because, in the academic literature, they have been tested for similar problems to ours. The eight DL models are compared both in terms of accuracy and their computational cost. From the analysis of the results, it is evident that Micro VGG outperforms the other approaches in terms of performance, at a smaller computational cost. Batch normalization has improved our model's performance, while a constant learning rate proved more adequate. Since Micro VGG is considered the best candidate solution for our problem, its confusion matrix is provided in Figure 10. From the heatmap that is represented in the confusion matrix, it is proven that Micro VGG is not biased and classifies all the hypms uniformly. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameters Size** & **Number of** & **Number of** \\  & **(MB)** & **Parameters** & **Operations** \\ \hline VGGish & 220.48 & 55,119,447 & 28.45 G \\ ResNet18 & 44.75 & 11,188,311 & 148.07 M \\ MobileNetV3 & 6.14 & 1,542,765 & 240.93 M \\ SqueezeNet & 4.37 & 734,295 & 17.26 M \\ EfficientNet & 4.74 & 4,037,011 & 7 M \\ \hline \hline \end{tabular}
\end{table} 
Table 6: SOTA models’ computational cost for one forward pass. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Train Accuracy \%** & **Validation Accuracy \%** & **Test Accuracy \%** \\ \hline VGGish & 100 \(\pm\) 0 & 98.03 \(\pm\) 1.16 & 95.42 \\ ResNet18 & 96.18 \(\pm\) 1.21 & 97.27 \(\pm\) 1.08 & 96.11 \\ MobileNetV3 & 94.87 \(\pm\) 1.22 & 98.18 \(\pm\) 1.03 & 95.76 \\ SqueezeNet & 90.08 \(\pm\) 1.26 & 91.04 \(\pm\) 0.99 & 88.78 \\ EfficientNet & 95.99 \(\pm\) 1.19 & 97.14 \(\pm\) 0.91 & 96.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Models’ performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F\({}_{1}\)-Score** \\ \hline VGGish & 0.94 & 0.94 & 0.94 \\ ResNet18 & 0.97 & 0.98 & 0.97 \\ MobileNetV3 & 0.96 & 0.95 & 0.95 \\ SqueezeNet & 0.89 & 0.89 & 0.89 \\ EfficientNet & 0.96 & 0.96 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Models’ performance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Training Time (s)** & **Inference Time (s)** \\ \hline VGGish & 3054.258 & 4.73 \\ ResNet18 & 2811.133 & 4.11 \\ MobileNetV3 & 7813.502 & 5.89 \\ SqueezeNet & 2821.533 & 4.23 \\ EfficientNet & 3018.431 & 4.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Models’ performance: Training and inference time. The findings of our work suggest that it is rather possible to make a mobile app that can be used inside of a church for hymn identification and classification. Such a mobile app will be useful for both tourists and church believers. Although Micro VGG's performance is rather satisfactory, our work has certain limitations. First, the dataset is relatively small, affecting the model's accuracy. Although data augmentation methods are used, more samples per hymn could lead to better results. Furthermore, CNNs are only a class of DL models. Other architectures that are often utilized in audio processing are recurrent neural networks (RNNs) and attention-based models. RNNs are designed for data with sequential structure, like audio, while attention-based models like Transformers have achieved impressive results in fields like NLP and CV.