Effects of emotional speech on forensic voice comparison using deep speaker embeddings

Emotional conditions play a significant role in forensic voice comparison and speaker verification systems. When emotion is present in speech, the verification's performance will deteriorate. In this paper, speaker verification has been investigated and analyzed in the case of emotional speech using metrics evaluating the performance of forensic voice comparison using pre-trained speaker embedding models: x-vector and ECAPA-TDNN for embedded feature extraction. This study investigates whether emotional content affects the forensic voice comparison and verification performance evaluated on a Hungarian speech dataset. The speaker verification performance has been assessed using the likelihood-ratio framework using Cllr and Cllrmin and Equal Error Rate. The ECAPATDNN achieved higher performance than the x-vector. In the same emotion scenario, the best EERs were 2.6% and 7.7% for ECAPA-TDNN and x-vector. Both models are sensitive to the emotional content of the speech samples

Architectural heritage images classification using deep learning with CNN

© 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). Digital documentation of cultural heritage images has emerged as an important topic in data analysis. Increasing the size and number of images to be processed making the task of categorizing them a challenging task and may take an inordinate amount of time. This research paper proposes a solution to the mentioned challenges by classifying the subject of the image of the study using Convolutional Neural Network. Classification of available images leads to improve the management of the images dataset and enhance the search of a specific item, which helps in the tasks of studying and analysis the proper heritage object. Deep learning for architectural heritage images classification has been employed during the course of this study. The pre-trained convolutional neural networks GoogLeNet, resnet18 and resnet50 proposed to be applied on public dataset Cultural Heritage images. Experimental results have shown promising outcomes with an accuracy of “87.91”, “95.47” and “95.57” respectively

Cluster-based information retrieval by using (K-means)-hierarchical parallel genetic algorithms approach

Cluster-based information retrieval is one of the information retrieval (IR) tools that organize, extract features and categorize the web documents according to their similarity. Unlike traditional approaches, cluster-based IR is fast in processing large datasets of document. To improve the quality of retrieved documents, increase the efficiency of IR and reduce irrelevant documents from user search. In this paper, we proposed a (K-means)-hierarchical parallel genetic algorithms approach (HPGA) that combines the K-means clustering algorithm with hybrid PG of multi-deme and master/slave PG algorithms. K-means uses to cluster the population to k subpopulations then take most clusters relevant to the query to manipulate in a parallel way by the two levels of genetic parallelism, thus, irrelevant documents will not be included in subpopulations, as a way to improve the quality of results. Three common datasets (NLP, CISI, and CACM) are used to compute the recall, precision, and F-measure averages. Finally, we compared the precision values of three datasets with Genetic-IR and classic-IR. The proposed approach precision improvements with IR-GA were 45% in the CACM, 27% in the CISI, and 25% in the NLP. While, by comparing with Classic-IR, (K-means)-HPGA got 47% in CACM, 28% in CISI, and 34% in NLP

Building a general concept of analytical services for analysis of structured data

In this paper, “Building a common concept of analytical services for analyzing structured data” was proposed to build an analytical service to provide forecasts, descriptive and comparative data summaries using modern Microsoft technologies. This service will allow users to perform flexible viewing of information, receive arbitrary data slices and perform analytical operations of drill-down, convolution, pass-through distribution, the comparison in time. With the help of data mining, it is possible to detect previously unknown, non-trivial, practically useful and accessible interpretations of knowledge that are necessary for the organization's decision-making. Also, each client can interact with the service and thus monitor the displayed analytical information. In the process of work the following tasks were solved: investigated the subject area; studied materials relating to systems and technologies for their implementation; designed service architecture and applications to configure the service; selected technologies and tools for the implementation of the system; implemented the main frame of the system; modules for interaction with analysis services, data mining (a priori algorithm) and partially a module of neural networks; a report was written and a presentation of the results was prepared; The developed service will be useful to all organizations that are interested in obtaining analytical reports and other previously unknown information on their accumulated data. For example, organizations can analyze the impact of advertising, customer segmentation, search for signs of profitable customers, analyze product preferences, forecast sales volumes, and more

The effect of Laser Shock Peening on Fatigue Life Using Pure Water and Hydrofluoric Acid As a Confining Layer of Al – Alloy 7075-T6

Laser shock peening (LSP) is deemed as a deep-rooted technology for stimulating compressive residual stresses below the surface of metallic elements. As a result, fatigue lifespan is improved, and the substance properties become further resistant to wear and corrosion. The LSP provides more unfailing surface treatment and a potential decrease in microstructural damage. Laser shock peening is a well-organized method measured up to the mechanical shoot peening. This kind of surface handling can be fulfilled via an intense laser pulse focused on a substantial surface in extremely shorter intervals. In this work, Hydrofluoric Acid (HF) and pure water as a coating layer were utilized as a new technique to improve the properties and to harden the treated surface of the Al -alloy 7075-T6. Fatigue life by means of laser peened workpieces was improved to 154.3%, 9.78%, respectively, for Hydrofluoric (HF) and pure water compared to un-peened specimens. And the outcomes of Vickers hardness test for laser shock peening with acid and pure water as well as un-peened specimens were 165.2HV30, 143.95HV30 and 134.7HV30, respectively showed a significant improvement in the hardness property