## 3 Results and Discussion
### Audio Analysis Method
#### 3.4.1 Features  
Mel-frequency cepstral coefficients (MFCCs) [111] are the most common set of features used in studies that exploited a machine learning framework. MFCCs mimic some of the processing performed by the human hearing system, such as the nonlinear perceptual mel-frequency scale of pitch. These features have been widely used in speech-processing applications, including speech/speaker/emotion recognition, to name a few. Around 27\(\%\) of studies analyzed MFCCs to extract information for bee detection, queen absence and swarming detection, and bee species identification, as well as environmental effects, with the three first coefficients showing the most discrimination. The authors in [51], used the so-called improved MFCC (IMFCC) proposed in [112] to capture additional information from the higher-frequency part of the spectrum that is typically ignored by the traditional MFCC; this was shown to improve classification accuracy for queenless hive detection tasks. In [61], the short-time Fourier transform (STFT) calculated with filterbank and overlap-add methods was used to detect swarming events. The STFT was calculated using 128, 256, 512, and 1024 bins to investigate the impact of window width. The best time localization and frequency resolution achieved was with 512 bins, thus inline with other studies [41; 53]. In [57], in turn, the performance of the fast Fourier transform (FFT), the S-transform (S-T), and the STFT were compared for the task of queenless hive detection. The S-T was shown \begin{table}
\begin{tabular}{c c} \hline \hline
**Microphone Frequency Range (Hz)** & **Articles** \\ \hline
6–20,000 & [64] \\
15–20,000 & [41] \\
20–20,000 & [55] \\
40–20,000 & [75] \\
50–10,000 & [88] \\
70–16,000 & [94] \\
100–15,000 & [39; 71] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Microphone characterization. to be very effective in identifying time-varying events, such as the \"warbles\" mentioned in [33] which are indicative of a queenless hive. STFT-based features were the second most used in the reviewed works. In [76], in turn, a spectrogram with a frequency range of 0-512 Hz and resolution of 8 Hz was used to visualize bee behaviour before and during swarming. It was found that higher energy in the 400-500 Hz band could be an indicator of swarming activity. In [53], the authors used MFCCs, mel-scaled spectrogram, and the time-aggregated time-frequency spectrogram produced by Hilbert-Huang transform (HHT) [113] (a time-frequency signal analysis method based on empirical mode decomposition) with different classifiers to detect queenless hives in \"hive-dependent\" and \"hive-independent\" scenarios. The results indicated that all tested features could discriminate queenless hives, with the fusion of HHT-based features and MFCCs showing to improve accuracy for the \"hive-independent\" setup, thus being better suitable for applications that generalize to unseen hives. In comparison with the spectrogram, the HHT better captured the harmonic information, due to its higher resolution, relative to the MFCC, hence the observed gains. In a similar vein, the constant-Q transform (CQT), a time-frequency transform widely used in music processing, was used for bee detection in [47]. Experiments with a bee detection system showed that MFCCs could better distinguish bee sounds from noise, as compared to the mel-scaled spectrogram and the CQT. Another powerful time-frequency analysis method is the wavelet transform (WT). In [52], the discrete wavelet transform (DWT) was tested for queenless beehive detection. Results indicated that a DWT with a 10-level dyadic filterbank resulted in a clear distinction between queenless and queenright hives. The work in [54] showed that continuous wavelet transform (CWT) outperformed other time-frequency features such as MFCCs, spectrogram, HHT, and DWT in queenless beehive detection with \"hive-independent\" setup.