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Generowanie wielogłosowej muzyki o określonej emocji przy użyciu wariacyjnego autoenkodera

Grekow Jacek

Przegląd Elektrotechniczny

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2023

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R. 99, nr 6

225--229

PL

Artykuł przedstawienia proces budowy modelu generującego wielogłosowe muzyczne sekwencje o określonej emocji. Opisano w nim proces przygotowania bazy przykładów uczących i budowę modelu generatywnego na bazie wariacyjnego autoenkodera. Przedstawiono eksperymenty implementacji warstw konwolucyjnych przeznaczonych do analizy wizualnej reprezentacji przykładów muzycznych. Wygenerowane pliki muzyczne poddano ewaluacji przez użycie metryk i porównanie ze zbiorem treningowym.

EN

This article presents the process of building a system generating polyphonic music content with a specified emotion. The process of preparing a training files and building a generative model based on a variational autoencoder was described. Experiments on the implementation of convolutional layers intended for analysis of the musical examples were presented. The generated examples were evaluated by using metrics and comparing them with the training set.

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Generative modelling of vibration signals in machine maintenance

Puchalski Andrzej Adam, Komorska Iwona

Eksploatacja i Niezawodność

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2023

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Vol. 25, no. 4

art. no. 173488

EN

The exponential development of technologies for the acquisition, collection, and processing of data from real-world objects is creating new perspectives in the field of machine maintenance. The Industrial Internet of Things is the source of a huge collection of measurement data. The performance of classification or regression algorithms needs to take into account the random nature of the process being modelled and any incomplete observability, especially in terms of failure states. The article highlights the practical possibilities of using generative artificial intelligence and deep machine learning systems to create synthetic measurement observations in monitoring the vibrations of rotating machinery to improve unbalanced databases. Variational Autoencoder VAE generative models with latent variables in the form of high-level input features of time-frequency spectra were studied. The mapping and generation algorithm was optimised and its effectiveness was tested in the practical solution of the task of diagnosing the three operating states of a demonstration gearbox.

3

Detecting anomalies in advertising web traffic with the use of the variational autoencoder

Gabryel Marcin, Lada Dawid, Filutowicz Zbigniew, Patora-Wysocka Zofia, Kisiel-Dorohinicki Marek, Chen Guang Yi

Journal of Artificial Intelligence and Soft Computing Research

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2022

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Vol. 12, No. 4

255--266

EN

This paper presents a neural network model for identifying non-human traffic to a website, which is significantly different from visits made by regular users. Such visits are undesirable from the point of view of the website owner as they are not human activity, and therefore do not bring any value, and, what is more, most often involve costs incurred in connection with the handling of advertising. They are made most often by dishonest publishers using special software (bots) to generate profits. Bots are also used in scraping, which is automatic scanning and downloading of website content, which actually is not in the interest of website authors. The model proposed in this work is learnt by data extracted directly from the web browser during website visits. This data is acquired by using a specially prepared JavaScript that monitors the behavior of the user or bot. The appearance of a bot on a website generates parameter values that are significantly different from those collected during typical visits made by human website users. It is not possible to learn more about the software controlling the bots and to know all the data generated by them. Therefore, this paper proposes a variational autoencoder (VAE) neural network model with modifications to detect the occurrence of abnormal parameter values that deviate from data obtained from human users’ Internet traffic. The algorithm works on the basis of a popular autoencoder method for detecting anomalies, however, a number of original improvements have been implemented. In the study we used authentic data extracted from several large online stores.

4

Proces rekonstrukcji obrazu tomograficznego w oparciu o sieć Variational Autoencoder

Podolszańska Jolanta

Inżynier i Fizyk Medyczny

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2021

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Vol. 10, nr 1

61--64

PL

Artykuł ma na celu zapoznanie się z rekonstrukcją i odszumianiem obrazu za pomocą sieci neuronowej typu VAE (Variational Auto-Encoder). W pracy zostanie dokonana analiza porównawcza pod kątem błędów rekonstrukcji i występujących na obrazie anomalii. Posłużono się zbiorem obrazów TK mózgu (Visible Female CT), aby pokazać, jak wygląda rekonstrukcja i odszumianie metodą Variational Autoencoder.

EN

This paper aims to learn about image reconstruction and de-noising using Variational Encoder (VAE) neural network. The paper will make a comparative analysis in terms of reconstruction errors and anomalies present in the image. A collection of brain CT images (Visible Female CT) is used to show how reconstruction and de-noising by Variational Autoencoder method.