Using bioacoustic signals and Support Vector Machine for automatic classification of insects

Noda, Juan J.; Travieso, Carlos M.; Sánchez-Rodríguez, David; Dutta, Malay Kishore; Singh, Anushikha

doi:10.1109/spin.2016.7566778

Cited by 22 publications

(16 citation statements)

References 12 publications

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“…Data are o en not linearly separable (Roma et al 2010), so we experimentally tested which of the four different kernel types: 'linear' , 'polynomial' , 'radial basis' or 'sigmoid' , resulted in the best ability to predict the test data from the training data. We randomly assigned approximately 80% of our original data-set as training data, and the remaining 20% of our data-set to the test data-set (Murphy 2012;Fedurek et al 2016;Turesson et al 2016). e 'svm' function in the 'e1071' R package allows the user to set the k-fold cross-validation parameter, wherein the training data is divided into k-folds, with one fold being used to validate the model and the rest used to train the model (Meyer et al 2017).…”

Section: Svm Training and Validationmentioning

confidence: 99%

“…Multi-class classification can be done using SVM with a variety of different strategies; one of the more successful approaches is 'one-against-one' , where a binary classifier is trained for each pair of classes in the data-set (Hsu and Lin 2002). SVMs have been used to effectively classify bird songs (Cheng et al 2010;Dufour et al 2014), dolphin whistles (Esfahanian et al 2014), 88 different insect species (Noda et al 2016) and primates (chimpanzees, Fedurek et al 2016;marmosets, Turesson et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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Application of a semi-automated vocal fingerprinting approach to monitor Bornean gibbon females in an experimentally fragmented landscape in Sabah, Malaysia

2018

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Vocal individuality has been documented in a variety of mammalian species and it has been proposed that this individuality can be used as a vocal fingerprint to monitor individuals. Here we provide and test a classification method using Mel-frequency cepstral coefficients (MFCCs) to extract features from Bornean gibbon female calls. Our method is semi-automated as it requires manual pre-processing to identify and extract calls from the original recordings. We compared two methods of MFCC feature extraction: (1) averaging across all time windows and (2) creating a standardized number of time windows for each call. We analysed 376 calls from 33 gibbon females and, using linear discriminant analysis, found that we were able to improve female identification accuracy from 95.7% with spectrogram features to 98.4% accuracy when averaging MFCCs across time windows, and 98.9% accuracy when using a standardized number of windows. We divided our data randomly into training and test data-sets, and tested the accuracy of support vector machine (SVM) predictions over 100 iterations. We found that we could predict female identity in the test data-set with a 98.8% accuracy. Using SVM on our entire data-set, we were able to predict female identity with 99.5% accuracy (validated by leave-one-out cross-validation). Lastly, we used the method presented here to classify four females recorded during three or more recording seasons using SVM with limited success. We provide evidence that MFCC feature extraction is effective for distinguishing between female Bornean gibbons, and make suggestions for future vocal fingerprinting applications.

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Section: Svm Training and Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of a semi-automated vocal fingerprinting approach to monitor Bornean gibbon females in an experimentally fragmented landscape in Sabah, Malaysia

2018

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“…Several studies [6][7][8][9][10][11][12] have focused on bird sound identification, classification, and its challenges. The study in [13] has performed the insect species classification based on their sound signals. The prediction of unusual animal sound behavior during the earthquake and natural disaster has been studied using machine learning techniques in [14].…”

Section: Introductionmentioning

confidence: 99%

Domestic Cat Sound Classification Using Learned Features from Deep Neural Nets

2018

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The domestic cat (Feliscatus) is one of the most attractive pets in the world, and it generates mysterious kinds of sound according to its mood and situation. In this paper, we deal with the automatic classification of cat sounds using machine learning. Machine learning approach for the classification requires class labeled data, so our work starts with building a small dataset named CatSound across 10 categories. Along with the original dataset, we increase the amount of data with various audio data augmentation methods to help our classification task. In this study, we use two types of learned features from deep neural networks; one from a pre-trained convolutional neural net (CNN) on music data by transfer learning and the other from unsupervised convolutional deep belief network that is (CDBN) solely trained on a collected set of cat sounds. In addition to conventional GAP, we propose an effective pooling method called FDAP to explore a number of meaningful features. In FDAP, the frequency dimension is roughly divided and then the average pooling is applied in each division. For the classification, we exploited five different machine learning algorithms and an ensemble of them. We compare the classification performances with respect following factors: the amount of data increased by augmentation, the learned features from pre-trained CNN or unsupervised CDBN, conventional GAP or FDAP, and the machine learning algorithms used for the classification. As expected, the proposed FDAP features with larger amount of data increased by augmentation combined with the ensemble approach have produced the best accuracy. Moreover, both learned features from pre-trained CNN and unsupervised CDBN produce good results in the experiment. Therefore, with the combination of all those positive factors, we obtained the best result of 91.13% in accuracy, 0.91 in f1-score, and 0.995 in area under the curve (AUC) score.

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“…Moreover, Zamanian and Pourghassem [12] fed 29 features extracted from both the temporal and spectral domains into a multi-layer perceptron (MLP) model to classify insects and achieved an average of 99% accuracy. The first limitation of the previous works is that they focused only on classifying a specific animal group, such as a bird species [7,10], insect species [13,14], etc. However, it is also meaningful to study mixed category classifications, such as bird chirping and human activities.…”

Section: Introductionmentioning

confidence: 99%

Identifying Patterns of Human and Bird Activities Using Bioacoustic Data

Garg

Brown

2019

Forests

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In general, humans and animals often interact within the same environment at the same time. Human activities may disturb or affect some bird activities. Therefore, it is important to monitor and study the relationships between human and animal activities. This paper proposed a system able not only to automatically classify human and bird activities using bioacoustic data, but also to automatically summarize patterns of events over time. To perform automatic summarization of acoustic events, a frequency–duration graph (FDG) framework was proposed to summarize the patterns of human and bird activities. This system first performs data pre-processing work on raw bioacoustic data and then applies a support vector machine (SVM) model and a multi-layer perceptron (MLP) model to classify human and bird chirping activities before using the FDG framework to summarize results. The SVM model achieved 98% accuracy on average and the MLP model achieved 98% accuracy on average across several day-long recordings. Three case studies with real data show that the FDG framework correctly determined the patterns of human and bird activities over time and provided both statistical and graphical insight into the relationships between these two events.

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Using bioacoustic signals and Support Vector Machine for automatic classification of insects

Cited by 22 publications

References 12 publications

Application of a semi-automated vocal fingerprinting approach to monitor Bornean gibbon females in an experimentally fragmented landscape in Sabah, Malaysia

Application of a semi-automated vocal fingerprinting approach to monitor Bornean gibbon females in an experimentally fragmented landscape in Sabah, Malaysia

Domestic Cat Sound Classification Using Learned Features from Deep Neural Nets

Identifying Patterns of Human and Bird Activities Using Bioacoustic Data

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