## Materials and methods
### Standard Machine Learning  
We used the following standard ML methods as comparison benchmarks for ConvNets: (M1) logistic regression [41]; (M2) k-nearest neighbors (KNN) [42]; (M3) support vector machine with a linear kernel one vs. rest (SVM OVR) classification [43]; and (M4) random forests [44]. These methods are supervised classification models that are trained on labeled data to classify new observations into one of the predefined classes. The M1 model (logistic regression) is used for predicting dependent categorical variables that belong to one of a limited number of categories and assigns a class with the highest probability for each input sample. The M2 model (KNN) is a supervised learning model that makes no a priori assumptions about the probability distribution of feature vector values. The training samples are converted into feature vectors in the feature vector space with their class labels. Given an input sample, M2 computes the distances between the feature vector of an input sample and each feature vector in the feature vector space, and the input sample is classified by a majority vote of the classes of its \(k\) nearest neighbors, where \(k\) is a parameter. The M3 model (SVM OVR) builds a linear binary classifier for each class where \(n\)-dimensional feature vectors are separated by a hyperplane boundary into positive and negative samples, where negative samples are samples from other classes. Given an input sample, each classifier produces a positive probability of the input sample's feature vector belonging to its class. The input sample is classified with the class of the classifier that produces the maximum positive probability. The M4 model (random forests) is an ensemble of decision trees where each decision tree is used to classify an input sample, and a majority vote is taken to predict the best class of the input sample. We trained all four models on the same feature vectors automatically extracted from the raw audio files in BUZZI. We used the following features: (F1) mel frequency cepstral coefficients (MFCC) [45]; (F2) chroma short term Fourier transform (STFT) [5]; (F3) melspectrogram [46]; (F4) STFT spectral contrast [47]; and (F5) tonnetz [48], a lattice representing the planar representations of pitch relations. Our methodology in using these features was based on the bag-of-features approach used in musical information retrieval [49]. Our objective was to create redundant feature vectors with as many potentially useful audio features as possible. For example, our inclusion of chroma and tonnetz features was based on our hypothesis that bee buzzing can be construed as musical melodies and mapped to note sequences [13]. Our inclusion of MFCC features, frequently used in speech processing, was motivated by the fact that some ambient sounds, especially around beehives in more urban environments, include human speech. Consequently, MFCCs, at least in theory, should be useful in classifying ambient sounds of human speech as noise [50]. Each raw audio sample was turned into a feature vector of 193 elements: 40 MFCC's, 12 chroma coefficients, 128 melspectrogram coefficients, seven spectral contrast coefficients, and six tonnetz coefficients.