An analysis of a large set of biometric data obtained during the enrolment and the verification phase in an experimental biometric system installed in bank branches is presented. Subjective opinions of bank clients and of bank tellers were also surveyed concerning the studied biometric methods in order to discover and to explore relations emerging from the obtained multimodal dataset. First, data acquisition and identity verification methods are described in this study. Then, relationships between ratios of successful and failed verifications between pairs, triplets, and quartets of biometric modalities are studied. An analysis of the sentiment of clients and of banking tellers related to each identity verification attempt was performed based on linguistic methods. The data mining process is described, based on the rough sets methodology, aimed at deriving rules pertaining to consecutive identity verification attempts.

The paper presents an application for automatically classifying emotions in film music. A model of emotions is proposed, which is also associated with colors. The model created has nine emotional states, to which colors are assigned according to the color theory in film. Subjective tests are carried out to check the correctness of the assumptions behind the adopted emotion model. For that purpose, a statistical analysis of the subjective test results is performed. The application employs a deep convolutional neural network (CNN), which classifies emotions based on 30 s excerpts of music works presented to the CNN input using mel-spectrograms. Examples of classification results of the selected neural networks used to create the system are shown.

The purpose of this research is two-fold: (a) to explore the relationship between the listeners’ personality trait, i.e., extraverts and introverts and their preferred music genres, and (b) to predict the personality trait of potential listeners on the basis of a musical excerpt by employing several classification algorithms. We assume that this may help match songs according to the listener’s personality in social music networks. First, an Internet survey was built, in which the respondents identify themselves as extraverts or introverts according to the given definitions. Their task was to listen to music excerpts that belong to several music genres and choose the ones they like. Next, music samples were parameterized. Two parametrization schemes were employed for that purpose, i.e., low-level MIRtoolbox parameters (MIRTbx) and variational autoencoder neural network-based, which automatically extract parameters of musical excerpts. The prediction of a personality type was performed employing four baseline algorithms, i.e., support vector machine (SVM), k-nearest neighbors (k-NN), random forest (RF), and naïve Bayes (NB). The best results were obtained by the SVM classifier. The results of these analyses led to the conclusion that musical excerpt features derived from the autoencoder were, in general, more likely to carry useful information associated with the personality of the listeners than the low-level parameters derived from the signal analysis. We also found that training of the autoencoders on sets of musical pieces which contain genres other than ones employed in the subjective tests did not affect the accuracy of the classifiers predicting the personalities of the survey participants.

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch’s method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

A system setup for measurements of acoustic field, together with the results of 3D visualisations of acoustic energy flow are presented in the paper. Spatial sampling of the field is performed by a Cartesian robot. Automatization of the measurement process is achieved with the use of a specialized control system. The method is based on measuring the sound pressure (scalar) and particle velocity(vector) quantities. The aim of the system is to collect data with a high precision and repeatability. The system is employed for measurements of acoustic energy flow in the proximity of an artificial head in an anechoic chamber. In the measurement setup an algorithm for generation of the probe movement path is included. The algorithm finds the optimum path of the robot movement, taking into account a given 3D object shape present in the measurement space. The results are presented for two cases, first without any obstacle and the other - with an artificial head in the sound field.