## 3 Results and Discussion
### Audio Analysis Method
#### 3.4.2 Machine Learning Algorithm  
A typical machine learning framework encompasses signal measurement, preprocessing, feature extraction, and, lastly, classification. In the area of bee acoustic analysis, many different classifiers have been explored. Table 7 summarizes the different classification algorithms used. As can be seen, the most common classifier is a support vector machine (SVM) [116], a kernel-based method that projects data into higher dimensions in which a hyperplane can separate the classes. Authors in [87] used an SVM to predict the pollination efficacy of bumblebees. The performance of the SVM using different input features (e.g., MFCC, HHT, mel-scaled spectrogram), on the other hand, was explored for queenless hive detection in [53]. The impact of different kernels, including linear, quadratic, and radial basis function, was compared in [68] against random forest classifier [117] and K-nearest neighbours method for detecting common air pollution chemicals such as acetone, trichloromethane, glutaratic dialdehyde, and ethyl ether; the SVM with radial basis function as the kernel showed the best accuracy. To detect parasite infestation, authors in [28] compared linear discriminant analysis (LDA) with SVM and found that while LDA outperformed the SVM in most cases, the SVM had lower computational cost and complexity. Logistic regression has also been explored intensively, especially for queenless hive detection [31; 50; 55]. Decision trees have been widely used for classification as they are easy to interpret [118]. Decision trees have been used to classify bee species [81; 82; 83]. A performance comparison between decision tree and other classifiers such as SVM, logistic regression, and random forest showed that they are not usually the most accurate one. Naive Bayes has also been used in classifying bee species [81; 82]. Authors in [82] showed that random forest, logistic regression, and K-nearest neighbours outperformed naive Bayes in detecting bee species. The abovementioned methods have been termed \"classical\" or \"conventional\" machine learning paradigms, to separate them from the more recent and burgeoning deep-learning neural-network-based methods. The work in [50], for example, proposed the use of so-called long short-term memory (LSTM) recurrent neural networks (RNN) for queen bee presence detection. A comparison between an LSTM, a multi-layer perceptron (MLP) neural network, and logistic regression was made, and it showed the power of the LSTM for the task at hand. The MLP was again explored in [51] for queen bee presence detection and showed to outperform classical SVM and KNN classifiers. It was also tested for swarming detection in [63; 64] and compared to traditional methods, such as Gaussian mixture models (GMM) and hidden Markov models (HMM), showing its superiority. Recently, convolutional neural networks (CNN) have gained popularity, especially within computer vision tasks. To make them directly applicable to bee acoustics analysis, researchers have relied on image-like inputs, such as spectrograms, mel-scaled spectrogram, \begin{table}
\begin{tabular}{l c} \hline \hline
**Feature** & **Articles** \\ \hline Fast Fourier transform and s-transform & [57] \\ Spectrogram (STFT) & [28; 41; 45; 54; 57; 61; 62; 69; 75; 76; 77; 93] \\ Mel-scaled spectrogram & [41; 47; 48; 53; 54] \\ Mel-frequency cepstral coefficients & [21; 31; 47; 48; 50; 53; 55; 62; 64; 67; 68; 80; 83; 89] \\ Improved mel-frequency cepstral coefficients & [51] \\ Discrete wavelet transform & [52; 54; 62] \\ Continuous wavelet transform & [54; 62] \\ Hilbert–Huang transform & [52; 53; 54; 62] \\ Linear predictive coding & [56; 64] \\ Power spectral density & [20; 78] \\ Constant-Q transform & [47] \\ Temporal features & [71; 79; 90; 91] \\ Spectral features & [28; 84; 99] \\ Computational auditory scene analysis & [46; 85; 88] \\ Sound indices & [67; 96] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Feature extraction methods explored. or other two-dimensional time-frequency representations of the audio.