Polyphonic Sound Event Detection by using Capsule Neural Networks

Artificial sound event detection (SED) has the aim to mimic the human ability to perceive and understand what is happening in the surroundings. Nowadays, Deep Learning offers valuable techniques for this goal such as Convolutional Neural Networks (CNNs). The Capsule Neural Network (CapsNet) architecture has been recently introduced in the image processing field with the intent to overcome some of the known limitations of CNNs, specifically regarding the scarce robustness to affine transformations (i.e., perspective, size, orientation) and the detection of overlapped images. This motivated the authors to employ CapsNets to deal with the polyphonic-SED task, in which multiple sound events occur simultaneously. Specifically, we propose to exploit the capsule units to represent a set of distinctive properties for each individual sound event. Capsule units are connected through a so-called "dynamic routing" that encourages learning part-whole relationships and improves the detection performance in a polyphonic context. This paper reports extensive evaluations carried out on three publicly available datasets, showing how the CapsNet-based algorithm not only outperforms standard CNNs but also allows to achieve the best results with respect to the state of the art algorithms

Adaptive Linear Prediction Filtering in DWT Domain for Real-Time Musical Onset Detection

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Real-time emulation of the Clavinet

none3siopenLeonardo Gabrielli, Vesa Välimäki, Stefan BilbaoGabrielli, Leonardo; Välimäki, Vesa; Bilbao, Stefa

Estimativa de Tempo de Vida de Gaxetas Hnbr e Nbr Utilizadas Em Trocadores de Calor do Tipo Placas Gaxetadas

TCC (graduação) - Universidade Federal de Santa Catarina, Campus Joinville, Engenharia Naval.Elastômeros utilizados na indústria de óleo e gás em trocadores de calor de placas gaxetadas são expostos a condições termo-oxidativas agressivas, onde a temperatura, a atmosfera corrosiva e a natureza dos fluidos envolvidos nos processos promovem a degradação dos materiais poliméricos utilizados para fazer a vedação das placas planas. Este estudo, então, busca prever o tempo de vida de gaxetas utilizadas em trocadores de calor pela extrapolação de Compression Set (CS) e dureza. Os materiais estudados foram borrachas do tipo HNBR (Acrilonitrilo butadieno hidrogenado) e NBR (Acrilonitrilo butadieno), as amostras foram separadas por tamanho, 10 mm e 70 mm e expostas a diferentes temperaturas enquanto comprimidas até 75% de sua altura média em diferentes períodos (3, 7, 15, 30, 60 e 180 dias). Ao final do tempo de envelhecimento, avaliou-se a dureza e CS das amostras. Os resultados foram extrapolados utilizando o método de Arrhenius utilizando-se como critério de falha o valor de 80% do compression set. Com os resultados e o método pode-se ter melhor compreensão do comportamento do material em operação e quando deve-se fazer a manutenção nos trocadores onde esses materiais são empregados. Pode-se observar que as amostras de 70 milímetros de NBR mostraram maior presença do efeito de difusão limitada do oxigênio (DLO), e também maior resistência aos efeitos de oxidação. Tais valores podem estar sendo influenciados pelo efeito DLO, visto que este efeito é proporcional ao comprimento da amostra. As amostras de 70 mm de HNBR se mostraram mais resistentes em temperaturas mais altas, demorando mais tempo para atingir o critério de falha. Enquanto entre as amostras de 10 mm, o HNBR se sobressaiu, mostrando maior resistência.Elastomers used in the oil and gas industry in gasketed plate heat exchangers are exposed to aggressive thermo-oxidative conditions, where the temperature, the corrosive atmosphere and the nature of the fluids involved in the processes promote the destruction of the polymeric materials used to make the seal. of flat plates. This study, then, seeks to predict the lifetime of gaskets used in heat exchangers by extrapolation of Compression Set (CS) and hardness. The materials studied were rubbers of the type HNBR (Hydrogenated Acrylonitrile Butadiene) and NBR (Acrylonitrile Butadiene), as samples they were separated by size, 10 mm and 70 mm and exposed to different temperatures while compressed from 75% and their average height in different periods (3, 7, 15, 30, 60 and 180 days). After removing the samples from different periods of the ovens used to maintain the temperature, evaluate the hardness, CS and mass of the samples. The results were extrapolated using the Arrhenius method using as a consequence of failure or value of 80% of the compression set. With the results and the method, it is possible to have a better understanding of the behavior of the material in operation and when maintenance must be carried out in the exchangers where these materials are used. The 70 mm samples of HNBR appreciated the unexpected results, where a defined compression curve of 110 °C reached values higher than the temperature of 140 °C, which may be associated with a greater presence of the DLO effect for the higher temperatures. high do experiment. The 70 mm HNBR samples were more resistant at higher temperatures, taking longer to reach the failure criterion. While among the 10 mm samples, HNBR stood out, showing greater resistance

A Statistical Framework for Automatic Leakage Detection in Smart Water and Gas Grids

In the last few years, due to the technological improvement of advanced metering infrastructures, water and natural gas grids can be regarded as smart-grids, similarly to power ones. However, considering the number of studies related to the application of computational intelligence to distribution grids, the gap between power grids and water/gas grids is notably wide. For this purpose, in this paper, a framework for leakage identification is presented. The framework is composed of three sections aimed at the extraction and the selection of features and at the detection of leakages. A variation of the Sequential Feature Selection (SFS) algorithm is used to select the best performing features within a set, including, also, innovative temporal ones. The leakage identification is based on novelty detection and exploits the characterization of a normality model. Three statistical approaches, The Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) and One-Class Support Vector Machine (OC-SVM), are adopted, under a comparative perspective. Both residential and office building environments are investigated by means of two datasets. One is the Almanac of Minutely Power dataset (AMPds), and it provides water and gas data consumption at 1, 10 and 30 min of time resolution; the other is the Department of International Development (DFID) dataset, and it provides water and gas data consumption at 30 min of time resolution. The achieved performance, computed by means of the Area Under the Curve (AUC), reaches 90 % in the office building case study, thus confirming the suitability of the proposed approach for applications in smart water and gas grids

Oral Antiplatelet Therapy for Secondary Prevention of Non-Cardioembolic Ischemic Cerebrovascular Events

Stroke is the leading cause of disability and mortality worldwide. After an acute cerebrovascular ischemia, recurrent vascular events, including recurrent stroke or transient ischemic accidents (TIA), occur in around 20% of cases within the first 3 months. In order to minimize this percentage, antiplatelet therapy may play a key role in the management of non-cardioembolic cerebrovascular events. This review will focus on the current evidence of antiplatelet therapies most commonly discussed in practice guidelines and used in clinical practice for the treatment of stroke/TIA complications. The antiplatelet therapies most commonly used and discussed are as follows: aspirin, clopidogrel, and ticagrelor