SPEAKER IDENTIFICATION MODEL BASED ON DEEP NURAL NETWOKS

This study aims is to establish a small system of text-independent recognition of speakers for a relatively small group of speakers at a sound stage. The fascinating justification for the International Space Station (ISS) to detect if the astronauts are speaking at a specific time has influenced the difficulty. In this work, we employed Machine Learning Applications. Accordingly, we used the Direct Deep Neural Network (DNN)-based approach, in which the posterior opportunities of the output layer are utilized to determine the speaker’s presence. In line with the small footprint design objective, a simple DNN model with only sufficient hidden units or sufficient hidden units per layer was designed, thereby reducing the cost of parameters through intentional preparation to avoid the normal overfitting problem and optimize the algorithmic aspects, such as context-based training, activation functions, validation, and learning rate. Two commercially available databases, namely, TIMIT clean speech and HTIMIT multihandset communication database and TIMIT noise-added data framework, were tested for this reference model that we developed using four sound categories at three distinct signal-to-noise ratios. Briefly, we used a dynamic pruning method in which the conditions of all layers are simultaneously pruned, and the pruning mechanism is reassigned. The usefulness of this approach was evaluated on all the above contact databases

Efficient coding of natural scenes improves neural system identification

Neural system identification aims at learning the response function of neurons to arbitrary stimuli using experimentally recorded data, but typically does not leverage coding principles such as efficient coding of natural environments. Visual systems, however, have evolved to efficiently process input from the natural environment. Here, we present a normative network regularization for system identification models by incorporating, as a regularizer, the efficient coding hypothesis, which states that neural response properties of sensory representations are strongly shaped by the need to preserve most of the stimulus information with limited resources. Using this approach, we explored if a system identification model can be improved by sharing its convolutional filters with those of an autoencoder which aims to efficiently encode natural stimuli. To this end, we built a hybrid model to predict the responses of retinal neurons to noise stimuli. This approach did not only yield a higher performance than the stand-alone system identification model, it also produced more biologically-plausible filters. We found these results to be consistent for retinal responses to different stimuli and across model architectures. Moreover, our normatively regularized model performed particularly well in predicting responses of direction-of-motion sensitive retinal neurons. In summary, our results support the hypothesis that efficiently encoding environmental inputs can improve system identification models of early visual processing.

APPLICATION OF THE ATTITUDE IDENTIFICATION MODEL TO THE OBJECT BASED ON THE SOCIAL MEDIA PUBLICATIONS ANALYSIS FOR SOLVING APPLIED PROBLEMS IN ASSESSING THE EFFECTIVENESS OF THE ACTIVITIES OF SENIOR RUSSIAN FEDERATION SUBJECTS OFFICIALS

В статье изложена оценка эффективности модели идентификации отношения к целевому объекту на основе публикаций в социальных сетях, использующей искусственные показатели публикаций социальных сетей, полученные на основе структурно-временных характеристик последних. The article evaluates the effectiveness of the attitude identification model to the target object based on social networks publications, using social network publications artificial indicators obtained on their structural and temporal characteristics basis.

Developing Green Tourism-Based Model of Information Technology Utilization in Tourism Villages

The purpose of this study is to develop a model for the use of information technology in Tourism Villages by mapping the use of information technology on all aspects of tourism villages based on the concept of green tourism that focuses on environmental preservation and community welfare. The method used in this research is an information research framework consisting of stages of literature review and environmental aspects, analysis, construct identification, model development, model evaluation, and model application methods. The results of the research are in the form of a model for the use of information technology in green tourism-based tourism villages along with the method of applying the model. This model consists of two connected constructs, namely the use of information technology and green tourism. The implementation of this model in tourist villages is explained in the method of applying the model which contains a mapping of information technology needs and implementation steps based on the classification of tourist villages, namely pioneering, developing, advanced and independent. This model can be used as a reference for tourism village managers in utilizing information technology according to their needs.

Creating a corpus of historical documents for emotions identification

The aim of this article is to contextualize and describe the gathering and annotation of a conventual Hispanic and Novo Hispanic texts corpus for emotions identification. Such corpus will be the dataset for an emotions identification model based on machine learning ∖ deep learning techniques. Furthermore, this document describes several exploratory experiments carried out on the corpus. Within these experiments, it is described how the corpus is also used to obtain a lexicon mapped to polarities and emotions, and how some of the documents are hand-labeled by experts for the evaluation of the Machine Learning ∖ Deep learning -based emotion classification model. Finally, the future uses and experiments with said corpus are described.

Core looseness fault identification model based on Mel spectrogram-CNN

In order to realize transformer voiceprint recognition, a transformer voiceprint recognition model based on Mel spectrum convolution neural network is proposed. Firstly, the transformer core looseness fault is simulated by setting different preloads, and the sound signals under different preloads are collected; Secondly, the sound signal is converted into a spectrogram that can be trained by convolutional neural network, and then the dimension is reduced by Mel filter bank to draw Mel spectrogram, which can generate spectrogram data sets under different preloads in batch; Finally, the data set is introduced into convolutional neural network for training, and the transformer voiceprint fault recognition model is obtained. The results show that the training accuracy of the proposed Mel spectrum convolution neural network transformer identification model is 99.91%, which can well identify the core loosening faults.