Recognition of bumblebee species by their buzzing sound Mukhiddin Yusupov, Mitja Luštrek, Janez Grad and Matjaž Gams Jožef Stefan Institute Department of Intelligent Systems

Bumblebees 35 species in Slovenia Important pollinators, faster than honeybees, fly even in bad weather Sometimes bred in captivity for pollination in greenhouses One queen, a few hundred workers in a nest Endangered by intensive farming resulting in lack of flowers and safe nesting places

Our task Build a classifier: Input: recording of bumblebee sound Output: species and queen/worker type

Presentation structure Related work Dataset Preprocessing Feature extraction Classification Experimental results

Related work No work on classification of bumblebees

Related work No work on classification of bumblebees Some work on: General insect recognition for pest control A small competition for the best distance metric for insect sounds Birds Frogs ...

Dataset 71 recordings of bumblebees in the wild Queens of 8 species Workes of 7 species 15 classes in total

Preprocessing Manual segmentation Easy to see 3 relevant segments Not so easy to see that only 20% buzzing

Preprocessing Manual segmentation Volume normalization Pre-emphasis: boost high frequencies to emphasize buzzing over environmental noise Easy to see 3 relevant segments Not so easy to see that only 20% buzzing

Feature extraction (1) Segment the signal into short windows

Feature extraction (1) Segment the signal into short windows Mel-frequency cepstral coefficients (MFCC): Fourier transform: time → frequency domain Psycho-acoustic transformation of the frequency-domain data (appropriate binning etc.) Use 13 coefficients (bins)

Feature extraction (2) Linear predictive coefficients (LPC): Signal at a given time expressed as a linear combination of signals at previous times Select coefficients so that the difference between the signal expressed this way and the real signal within the window is minimized Use 10 coefficients

Feature extraction (2) Linear predictive coefficients (LPC): Signal at a given time expressed as a linear combination of signals at previous times Select coefficients so that the difference between the signal expressed this way and the real signal within the window is minimized Use 10 coefficients Compute mean coefficient values over all the windows in a recording

Classification Three-fold cross validation (too few recordings per class for more folds)

Classification Three-fold cross validation (too few recordings per class for more folds) Three machine-learning algorithms: J48 decision trees Multi-layer perceptron (MLP) Support vector machine (SVM)

Experimental results (1) 15 classes (species and queen/worker type) Two classes > 95 %, five > 80 % Algorithm \ Features MFCC LPC J48 56 % MLP 60 % SVM 64 % 57 %

Experimental results (2) 8 classes (species) Algorithm \ Features MFCC LPC J48 51 % 48 % MLP 50 % 49 % SVM 52 %

Experimental results (2) 8 classes (species) 2 classes (queen/worker type) Algorithm \ Features MFCC LPC J48 51 % 48 % MLP 50 % 49 % SVM 52 % Algorithm \ Features MFCC LPC J48 90 % 88 % MLP 93 % 87 % SVM 96 % 91 %

Conclusion Recognition of bumblebees by their buzzing sound with accuracy 64 % More data needed

Conclusion In the future: Recognition of bumblebees by their buzzing sound with accuracy 64 % More data needed In the future: Web application to classify unlabeled data Accept submissions of new labeled data