Uniform Manifold Approximation and Projection for Clustering Taxa through Vocalizations in a Neotropical Passerine (Rough-Legged Tyrannulet, Phyllomyias burmeisteri)

Parra-Hernández, Ronald M; Posada-Quintero, Jorge I; Acevedo‐Charry, Orlando; Posada-Quintero, Hugo F.

doi:10.3390/ani10081406

Cited by 24 publications

(18 citation statements)

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“…UMAP [ 52 ] is an algorithm based on manifold learning techniques; this algorithm depends on the ideas obtained from topological data analysis for data reduction. In application and research, UMAP is superior to t-SNE because of its faster processing speed.…”

Section: Methodsmentioning

confidence: 99%

Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device

Mai

Lee

Chung

2021

Sensors

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In this research, we develop an affective computing method based on machine learning for emotion recognition using a wireless protocol and a wearable electroencephalography (EEG) custom-designed device. The system collects EEG signals using an eight-electrode placement on the scalp; two of these electrodes were placed in the frontal lobe, and the other six electrodes were placed in the temporal lobe. We performed experiments on eight subjects while they watched emotive videos. Six entropy measures were employed for extracting suitable features from the EEG signals. Next, we evaluated our proposed models using three popular classifiers: a support vector machine (SVM), multi-layer perceptron (MLP), and one-dimensional convolutional neural network (1D-CNN) for emotion classification; both subject-dependent and subject-independent strategies were used. Our experiment results showed that the highest average accuracies achieved in the subject-dependent and subject-independent cases were 85.81% and 78.52%, respectively; these accuracies were achieved using a combination of the sample entropy measure and 1D-CNN. Moreover, our study investigates the T8 position (above the right ear) in the temporal lobe as the most critical channel among the proposed measurement positions for emotion classification through electrode selection. Our results prove the feasibility and efficiency of our proposed EEG-based affective computing method for emotion recognition in real-world applications.

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Section: Methodsmentioning

confidence: 99%

Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device

Mai

Lee

Chung

2021

Sensors

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“…To visualize our results we used a uniform manifold learning technique (UMAP; McInnes et al., 2018) implemented in the r package ‘ umap ’ to embed the 177 features from each gibbon female call into a two‐dimensional space. UMAP is an effective dimensionality technique that has been used to visualize differences in forest soundscapes (Sethi et al., 2020) and two distinct taxonomic groups of a neotropical passerine (Parra‐Hernández et al., 2020). We used the package ‘ ggplot2 ’ (Wickham, 2016) to plot the UMAP projections.…”

Section: Methodsmentioning

confidence: 99%

Unsupervised acoustic classification of individual gibbon females and the implications for passive acoustic monitoring

Clink

Klinck

2020

Methods Ecol Evol

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Passive acoustic monitoring (PAM) has the potential to greatly improve our ability to monitor cryptic yet vocal animals. Advances in automated signal detection have increased the scope of PAM, but distinguishing between individuals—which is necessary for density estimation—remains a major challenge. When individual identity is known, supervised classification techniques can be used to distinguish between individuals. Supervised methods require labelled training data, whereas unsupervised techniques do not. If the acoustic signals of individuals are sufficiently different, the number of clusters might represent the number of individuals sampled. The majority of applications of unsupervised techniques in animal vocalizations have focused on quantifying species‐specific call repertoires. However, with increased interest in PAM applications, unsupervised methods that can distinguish between individuals are needed. Here we use an existing dataset of Bornean gibbon female calls with known identity from five sites on Malaysian Borneo to test the ability of three different unsupervised clustering algorithms (affinity propagation, K‐medoids and Gaussian mixture model‐based clustering) to distinguish between individuals. Calls from different gibbon females are readily distinguishable using supervised techniques. For internal validation of unsupervised cluster solutions, we calculated silhouette coefficients. For external validation, we compared clustering results with female identity labels using a standard metric: normalized mutual information. We also calculated classification accuracy by assigning unsupervised cluster solutions to females based on which cluster had the highest number of calls from a particular female. We found that affinity propagation clustering consistently outperformed the other algorithms for all metrics used. In particular, classification accuracy of affinity propagation clustering was more consistent as the number of females increased, and when we randomly sampled females across sites. We conclude that unsupervised techniques may be useful for providing additional information regarding individual identity for PAM applications. We stress that although we use gibbons as a case study, these methods will be applicable for any individually distinct vocal animal.

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“…Unsupervised approaches, such as t‐SNE and MDS, can also be used to visualize and explore relationships among variables in the data in a space with fewer dimensions than present in the input data (Ramasubramanian & Singh, 2016). Moreover, data visualization procedures, such as Uniform Manifold Approximation and Projection (UMAP) may also prove useful for assessing separation of variables in acoustic space (Parra‐Hernández et al ., 2020). When using such approaches for dimensionality reduction, however, careful attention should be paid that the method preserves between‐object distance, as data visualization methods such as t‐SNE and UMAP may sacrifice global structure in order to preserve local variance.…”

Section: Approaches For Quantifying Animal Soundsmentioning

confidence: 99%

Comparative bioacoustics: a roadmap for quantifying and comparing animal sounds across diverse taxa

et al. 2021

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Animals produce a wide array of sounds with highly variable acoustic structures. It is possible to understand the causes and consequences of this variation across taxa with phylogenetic comparative analyses. Acoustic and evolutionary analyses are rapidly increasing in sophistication such that choosing appropriate acoustic and evolutionary approaches is increasingly difficult. However, the correct choice of analysis can have profound effects on output and evolutionary inferences. Here, we identify and address some of the challenges for this growing field by providing a roadmap for quantifying and comparing sound in a phylogenetic context for researchers with a broad range of scientific backgrounds. Sound, as a continuous, multidimensional trait can be particularly challenging to measure because it can be hard to identify variables that can be compared across taxa and it is also no small feat to process and analyse the resulting high‐dimensional acoustic data using approaches that are appropriate for subsequent evolutionary analysis. Additionally, terminological inconsistencies and the role of learning in the development of acoustic traits need to be considered. Phylogenetic comparative analyses also have their own sets of caveats to consider. We provide a set of recommendations for delimiting acoustic signals into discrete, comparable acoustic units. We also present a three‐stage workflow for extracting relevant acoustic data, including options for multivariate analyses and dimensionality reduction that is compatible with phylogenetic comparative analysis. We then summarize available phylogenetic comparative approaches and how they have been used in comparative bioacoustics, and address the limitations of comparative analyses with behavioural data. Lastly, we recommend how to apply these methods to acoustic data across a range of study systems. In this way, we provide an integrated framework to aid in quantitative analysis of cross‐taxa variation in animal sounds for comparative phylogenetic analysis. In addition, we advocate the standardization of acoustic terminology across disciplines and taxa, adoption of automated methods for acoustic feature extraction, and establishment of strong data archival practices for acoustic recordings and data analyses. Combining such practices with our proposed workflow will greatly advance the reproducibility, biological interpretation, and longevity of comparative bioacoustic studies.

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Uniform Manifold Approximation and Projection for Clustering Taxa through Vocalizations in a Neotropical Passerine (Rough-Legged Tyrannulet, Phyllomyias burmeisteri)

Cited by 24 publications

References 50 publications

Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device

Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device

Unsupervised acoustic classification of individual gibbon females and the implications for passive acoustic monitoring

Comparative bioacoustics: a roadmap for quantifying and comparing animal sounds across diverse taxa

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