## 4 Machine Learning (ML) Algorithms and Results
### Discription of Considered Set of Classical ML Algorithms  
In this paper, a wide variety of classifiers were employed in the task, and several versions were evaluated in each case to provide the possibility of broad comparisons of performance between classical ML and DL approaches. Considering the properties discussed in the main section of the paper, frequently applied types of classifiers have been used to conduct experiments, that is, decision trees, discriminant analysis, SVM, naive Bayes, kNNs, ensemble classifiers and ANNs. The decision trees and discriminant analysis are relatively fast and are not characterized by high complexity of interpretation. kNN has similar properties. On the other hand, they may not provide satisfactory efficiency of the prediction process. The naive Bayes classifier, although it is relatively simple, often outperforms more sophisticated classifiers. Models based on SVM and ANN allow for high accuracy, but they are much more complex. Ensemble learning combining multiple base classifiers (e.g., decisions tress or nearest neighbor) enables classification rules to be strengthened to achieve higher predictor accuracy. Thus, the wide experiment was introduced to search for a promising classifier type and its configuration. The following factors were taken into consideration while choosing the settings for each variant: training speed, memory utilization, prediction accuracy and response complexity. Table 1 lists seven classifier types considered during the experiment together with the most crucial variables for each of their particular versions. In connection with the theory about the lack of free lunches in machine learning [48] and the impact of multiple elements related to the dataset, multiple classifiers were analyzed in the context of the set of features created from the spectrograms. \begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline \hline Tree & Fine & Medium & Coarse \\ \hline \hline Maximum number of & \multirow{2}{*}{100} & \multirow{2}{*}{20} & \multirow{2}{*}{4} \\ splits & & & & \\ \hline Split criterion & Gini’s diversity & Gini’s diversity & Gini’s diversity index & \\  & index & index & \\ \hline
**Discriminant** & Linear & Quadratic & & \\ \hline Preset & Linear & Quadratic & & \\ \hline \hline
**SVM** & Linear & Quadratic & Cubic & Gaussian(with scaling) \\ \hline Kernel function & Linear & Quadratic & Cubic & 3 xGaussian(with scaling) Kernel \\  & & & & scale (1, 3.6, 14) \\ \hline Multi-class method & One vs One & One vs One & One vs One & One vs One \\ \hline
**KNN** & Fine & Medium & Coarse & Cosine & Cubic & Weighted \\ \hline Number of neighbours & 1 & 10 & 100 & 10 & 10 \\ \hline Distance metric & Euclidean & Euclidean & Cosine & Minkowski (cu-bic) & Euclidean \\ \hline Distance weight & Equal & Equal & Equal & Equal & Squared inverse \\ \hline
**Naïve Bayes** & Gaussian Naive & & & \\  & Bayes & & & \\ \hline Preset & Gaussian Naive & & & \\  & Bayes & & & \\ \hline
**Neural Network** & Narrow & Medium & Wide & Bilayered & Trilayered \\ \hline Preset & Narrow neural & Medium neural & Wide neural & Bilayered Neural & Trilayered Neu- \\  & network & network & network & Network & ral Network \\ \hline Number of fully connected layer & 1 & 1 & 1 & 2 & 3 \\ \hline First layer size & 10 & 25 & 100 & 10 & 10 \\ \hline Second Layer size & X & X & X & 10 & 10 \\ \hline Third Layer size & X & X & X & X & 10 \\ \hline Activation & ReLU & ReLU & ReLU & ReLU & ReLU \\ \hline Iteration limit & 1,000 & 1,000 & 1,000 & 1,000 & 1,000 \\ \hline Regularization strength & 0 & 0 & 0 & 0 & 0 \\ \hline Standardize data & Yes & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classifiers with selected hyperparameters. The experiments were realized using an application built into MATLAB software called Classification Learner [49].