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We present a convolutional neural network-and long short-term memory-based method to classify the valence level of a computer user based on functional near infrared spectroscopy data. Convolutional neural networks are well suited for capturing the spatial characteristics of functional near infrared spectroscopy data. And long short-term memories are demonstrated to be good at learning temporal patterns of unknown length in time series data. We explore these methods in a combined layered architecture in order to improve classification accuracy. We conducted an experiment with 20 participants, wherein they were subjected to emotion inducing stimuli while their brain activity was measured using functional near infrared spectroscopy. Self-report surveys were administered after each stimulus to gauge participants' self-assessment of their valence. The resulting classification using these survey labels as ground truth provided a three-class classification accuracy 77.89% in across subject cross-validation. This method also shows promise for generalization to other classification tasks using functional near infrared spectroscopy data.

The cat family Felidae is one of the most successful carnivore lineages today. However, the study of the evolution of acoustic communication between felids remains a challenge due to the lack of fossils, the limited availability of audio recordings because of their largely solitary and secretive behavior, and the underdevelopment of computational models and methods needed to address acoustic evolutionary questions. This study is a first attempt at developing a machine learning-based approach to the classification of felid calls as well as the identification of acoustic features that distinguish felid call types and species from one another. A felid call dataset was developed by extracting audio clips from diverse sources. The audio clips were manually annotated for call type and species. Due to the limited availability of samples, this study focused on the Pantherinae subfamily. Time-frequency features were then extracted from the Pantherinae dataset. Finally, several classification algorithms were applied to the resulting data. We achieved 91% accuracy for this Pantherinae call type classification. For the species classification, we obtained 86% accuracy. We also obtained the most predictive features for each of the classifications performed. These features can inform future research into the evolutionary acoustic analysis of the felid group.