Machine learning methods for individual acoustic recognition in a species of field cricket - Dlamini, Gciniwe (Department of Statistical Sciences, 2019)
Crickets, like other insects, play a vital role in maintaining a balance in the ecosystem. Therefore, the ability to identify individual crickets is crucial as it enables ecologists to estimate important population metrics such as population densities, which in turn are used to investigate ecological questions pertaining to these insects. In this research, classification models were developed to recognise individual field crickets of the species Plebeiogryllus guttiventris based solely on the audio recordings of their calls. Recent advances in technology have made data collection easier, and consequently, large volumes of data, including acoustic data, have become available to ecologists. The task of acoustic animal identifications thus requires the utilisation of models that are well suited for training large datasets. It is for this very reason that convolutional neural networks (CNN) and recurrent neural networks (RNN) were utilised in this research. The results of these models were compared to results of a baseline random forest (RF) model as RFs can also be used to make acoustic classifications. Mel-frequency cepstral coefficients (MFCC), raw acoustic samples as well as two temporal features were extracted from each chirp in the cricket recordings and used as inputs to train the machine learning models. The raw acoustic samples were only used in the deep neural network (DNN) models (CNNs and RNNs) as these models have been successful in training other raw forms of data such as images (for example, Krizhevsky et al. (2012)). Training on the MFCC features was conducted in two ways: the DNN models were trained on MFCC matrices that each spanned a chirp, whereas the RF models were trained on the MFCC frame vectors. This is because RF are only able to train on vector representations of observations, not matrices. The frame-level MFCC predictions obtained from the RF model were then aggregated into chirp-level predictions to facilitate the comparison with the other classification models. The best classification performance was achieved by the RF model trained on the MFCC features with a score of 99.67%. The worst performance was observed from the RF model trained upon the temporal features, which scored 67%. The DNN models attained on average 98.6% classification accuracies when trained on both MFCC features and the raw acoustic samples. These results show that individual recognition of the crickets using acoustics can be achieved with great success through the use of machine learning. Moreover, the performance of the deep learning models when trained upon the raw acoustic samples indicate that the feature (MFCC) extraction step can be bypassed; the deep learning machine algorithms can be trained directly on the raw acoustic data and still achieve great results.