# MFCC Selection by LASSO for Honey Bee Classification
## 4 Discussion  
The paper analyzes the statistical feature selection method based on the least absolute and selection operator (LASSO) for the popular form of the parametrization of audio recordings in the frequency domain by Mel-frequency cepstral coefficients (MFCCs) and their first and second derivatives. LASSO regularization is a forward stepwise sequential technique, adding new non-zero coefficients to a feature vector while decreasing the penalty on that feature vector in the \(\ell^{1}\)-norm. The resulting reduced feature vector is fed to the autoencoder neural network for honey bee classification, with two signal classes: worker bees and drones. The chosen subset of 69 coefficients from the set **MFCC+Delta+DeltaDelta** performed with the highest quality in the honey bee recognition task based on audio recordings. In the particular case of 69 selected coefficients, the LASSO selector chose 31/40 **MFCC** coefficients, 26/40 first-derivative **Delta** coefficients and 12/40 second-derivative **Delta** coefficients from the original set of 120 cepstral features. The frequency bands of the filter banks related to those selected MFCCs cannot give any clear information about the most significant frequency bands for the bee audio recordings due to covering most of the frequency spectrum--compare with Figure 7 for \(\alpha=0.0065\). However, it can be concluded that the first derivatives have more than twice as much influence on the results as the second derivatives. The main goal was to apply those feature extraction and selection methods for the honey bee classification task with worker bee and drone classes, helping to detect higher activity of male drone bees close to the entrances of hives. The paper shows that by applying LASSO selection to MFCC features, the overall two-class classification problem attains higher accuracy, compared to the original set of MFCC coefficients. In addition, the detection quality of the drone alone is increased as well. The accuracy for the original set **MFCC** of 40 coefficients is equal to 90.6%. For the sets with derivatives, the set **MFCC+Delta** of 80 coefficients and the set **MFCC+Delta+DeltaDelta** of 120 coefficients, the accuracy is decreased significantly to 82.3% and 81.2%, respectively. The highest accuracy, around 95.3%, is obtained for the feature sets containing 69 and 80 of the coefficients selected by LASSO, which were chosen from the original set **MFCC+Delta+DeltaDelta** of 120 coefficients, giving 4.7% improvement compared to the results achieved for the best case without LASSO feature selection. For those reduced models, the drone detection rate--measured by recall--also reached the highest value, equal to around 85.5%, which is presented in Table 2. The chosen subsets of features are linked to the information criteria, Akaike and Bayesian, which achieved low values, indicating that the selected feature sets represent the optimal set of features representing the training data. The quality of drone detection was improved by using the LASSO selection technique. The proposed approach can be applied in a smart beehive monitoring system for the purpose of the non-invasive detection of increased drone activity around beehives by fast audio processing, including the use of the trained autoencoder neural network, described in Section 2.4. The proposed solutions can also serve as a basis for constructing an early-stage alarm system to identify the swarming mood in a honey bee colony. Conceptualization, U.L. ; methodology, U.L. and P.B. ; software, U.L. and P.B. ; validation, U.L. and P.B. ; formal analysis, U.L. ; investigation, U.L. and P.B. ; resources, P.B. ; data curation, P.B. ; writing--original draft preparation, U.L. and P.B. ; writing--review and editing, U.L. and P.B. ; visualization, U.L. and P.B. ; supervision, U.L. ; project administration, U.L. ; funding acquisition. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.