Table 1.
Taxonomic diversity of sonicating bees recorded visiting tomato flowers and the corresponding higher taxonomic group (according to [44]).
(N recordings) denotes the number of individuals with buzzing-sounds recorded; (AF) average frequency ± standard deviation; (Flight segments) the total number of flight segments per species; (Sonication segments) the total number of sonication segments per species.
Fig 1.
Overview of the approach adopted for the acoustic classification of bees buzzing-sounds and machine learning workflow.
The original audio files (.wav format) containing recordings of bees buzzing-sounds during visits to tomato flowers were manually classified into sonication or flight segments. Then, the Mel Frequency Cepstral Coefficients method (MFCC) was used to extract the audio features. After, the resulting data set was split into 50% for the training/development set (delimited by the red dashed line) and 50% for the testing data set. The GridSearchCV method was used to tune the hyperparameters of the training set (using 5-cross validations). The test data set was used to evaluate the performance of the Machine-Learning classifiers in correctly assigning the buzzing sound to the respective bee taxa.
Fig 2.
Spectrograms of different types of buzzing (sonication and flight) for two visiting-bees species of tomato flowers (Melipona bicolor and Exomalopsis analis).
Note that the duration and amplitude and frequency of the buzzing-sounds vary between the species and among the type of buzzing.
Fig 3.
Overview of the steps for audio feature extraction by Mel Frequency Cepstral Coefficients Method (MFCC)
Pre-emphasis, framing, windowing, Discrete Fourier Transform (DFT), and filter bank (applying Discrete Cosine Transform—DCT).
Table 2.
Predictive performance of different Machine-Learning algorithms on acoustic recognition of bee species based on the type of buzzing-sound (flight, sonication, and flight+sonication) during visits to tomato flowers.
The performance of the ML algorithms was measured by Accuracy (Acc), Macro-Precision (MacPred), Macro-Recall (MacRec) and Macro-F1 (MacF1) and compared with three baselines scenarios: (1) Majority class: assigning all the classes to the majority class; (2) Fundamental frequency: bees recognition based solely on the average frequency of the sonication, as performed by [43]; (3) Fundamental frequency (SVM): bees recognition based fundamental frequency and using the SVM algorithm, classifier with the best performance (based on the MacF1-score). Bold numbers represent the best results per evaluation metric within buzz-sound; Different upper side letters denote significant differences in the F1-score among the algorithms of the same buzzing-behavioral (p ≤ 0.05, T-test); (**) denotes that the performance of the algorithm is higher than the baselines (based on the MacF1 measure; p ≤ 0.05, T-test).
Table 3.
Predictive performance of different Machine-Learning algorithms on acoustic recognition of bee genera based on the type of buzzing-sound (flight, sonication, and flight+sonication) during visits to tomato flowers.
The performance of the ML algorithms was measured by Accuracy (Acc), Macro-Precision (MacPrec), Macro-Recall (MacRec) and Macro-F1 (MacF1) and compared with three baseline scenarios: (1) Majority class: assigning all the classes to the majority class; (2) Fundamental frequency: bee recognition based solely on the average frequency of the sonication, as performed by [43]; (3) Fundamental frequency (SVM): bee recognition based fundamental frequency and using the SVM algorithm, classifier with the best performance (based on the MacF1 score). Bold numbers represent the best results per evaluation metric within buzz-sound; Different upper side letters denote significant differences in the MacF1 scores among the algorithms of the same buzzing-behavioral (p ≤ 0.05, T-test); (**) denotes that the performance of the algorithm is higher than the baselines (based on the MacF1 measure; p ≤ 0.05, T-test).
Table 4.
Confusion matrix with the best performance for bee buzzing-sounds classification at genus-level using MFCC features (flight with SVM classifier, MacF1 = 60.20% and Acc = 64.15%).
The numbers in the matrix correspond to correctly (diagonal elements, bold) and incorrectly (out-of-diagonal elements) recognized samples in the data set. The best parameters of this classification were C = 10, decision_function_shape = “ovo”, gamma = 0.01, kernel = “rbf”.
Table 5.
Confusion matrix with the best performance for bees buzzing-sounds classification at species-level using MFCC features (sonication with SVM classifier, MacF1 = 59.06% and Acc = 73.39%).
The numbers in the matrix correspond to correctly (diagonal elements, bold) and incorrectly (out-of-diagonal elements) recognized samples in the data set. The best parameters of this classification were C = 10, decision_function_shape = “ovo”, gamma = 0.01, kernel = “rbf”.