Triangular, overlapping Mel-scaled filters ("f-banks") are the current standard input for acoustic models that exploit their input's time-frequency geometry, because they provide a psycho-acoustically motivated time-frequency geometry for a speech signal. F-bank coefficients are provably robust to small deformations in…
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48 results for “filter bank”
Data is said to follow the transform (or analysis) sparsity model if it becomes sparse when acted on by a linear operator called a sparsifying transform. Several algorithms have been designed to learn such a transform directly from data, and data-adaptive sparsifying transforms have demonstrated excellent performance i…
BankGCN improves graph convolution networks by handling multi-channel signals with adaptive filter banks.
problem Handling multi-channel graph signals with limited architectures.
method BankGCN decomposes multi-channel signals into subspaces and uses adapted filters for each subspace.
result BankGCN achieves excellent performance in graph classification on benchmark datasets.
Regularization techniques are widely used to improve the generality, robustness, and efficiency of deep convolutional neural networks (DCNNs). In this paper, we propose a novel approach of regulating DCNN convolutional kernels by a structured filter bank. Comparing with the existing regularization methods, such as $\el…
Automatic heart sound abnormality detection can play a vital role in the early diagnosis of heart diseases, particularly in low-resource settings. The state-of-the-art algorithms for this task utilize a set of Finite Impulse Response (FIR) band-pass filters as a front-end followed by a Convolutional Neural Network (CNN…
MobileNets family of computer vision neural networks have fueled tremendous progress in the design and organization of resource-efficient architectures in recent years. New applications with stringent real-time requirements on highly constrained devices require further compression of MobileNets-like already compute-eff…
New method estimates corporate default probabilities using indirect data.
problem Lack of direct default rate data for corporate companies.
method Modeling default probability dynamics using Bank of Russia overdue debt data.
result Validated method produces trustworthy default probability series.
ISVAE enhances interpretability in time series clustering using a novel filter bank.
problem Improving interpretability in time series clustering models.
method Integrates a Filter Bank (FB) into a Variational Autoencoder (VAE) to enhance interpretability and clusterability.
result ISVAE produces a more interpretable and separable encoding with enhanced clusterability.
New method relaxes spatial invariance in locally connected layers, improving accuracy.
problem Improving classification accuracy with locally connected layers.
method Designing a low-rank locally connected layer with varying spatially varying combining weights.
result Relaxing spatial invariance improves classification accuracy over convolution and locally connected layers.
GNNs improve graph signal discrimination by adding nonlinearities.
problem Improving graph signal discrimination in physical networks.
method Analyzing the discriminability of GNNs and their relation to graph filter banks.
result GNNs are at least as discriminative as linear graph filter banks.
GNNs generalize CNNs for graph data, showing equivariance and stability.
problem Processing signals on graphs.
method Graph convolutional filters, nonlinearities, stacked layers.
result GNNs converge to graphon neural networks under graph convergence.
HKF uses neural networks to adapt Kalman filters for dynamic channel tracking.
problem Tracking channels with varying dynamics and Doppler values.
method Combines Kalman filters with hypernetworks for dynamic adaptation.
result HKF achieves up to 2dB gain over Kalman filters at high Doppler values.
Feature extraction for automatic classification of EEG signals typically relies on time frequency representations of the signal. Techniques such as cepstral-based filter banks or wavelets are popular analysis techniques in many signal processing applications including EEG classification. In this paper, we present a com…
In this paper, we tackle the question of discovering an effective set of spatial filters to solve hyperspectral classification problems. Instead of fixing a priori the filters and their parameters using expert knowledge, we let the model find them within random draws in the (possibly infinite) space of possible filters…
We propose two sparsity-aware normalized subband adaptive filter (NSAF) algorithms by using the gradient descent method to minimize a combination of the original NSAF cost function and the l1-norm penalty function on the filter coefficients. This l1-norm penalty exploits the sparsity of a system in the coefficients upd…
A new algorithm adapts to changing user behaviors in finance.
problem Adapting to changing user behaviors in financial recommendations.
method History-Augmented Collaborative Filtering using a custom neural network.
result The algorithm provides dynamic financial recommendations.
Classical models describe primary visual cortex (V1) as a filter bank of orientation-selective linear-nonlinear (LN) or energy models, but these models fail to predict neural responses to natural stimuli accurately. Recent work shows that models based on convolutional neural networks (CNNs) lead to much more accurate p…
Filter banks are a popular tool for the analysis of piecewise smooth signals such as natural images. Motivated by the empirically observed properties of scale and detail coefficients of images in the wavelet domain, we propose a hierarchical deep generative model of piecewise smooth signals that is a recursion across s…
Traditional automatic speech recognition (ASR) systems often use an acoustic model (AM) built on handcrafted acoustic features, such as log Mel-filter bank (FBANK) values. Recent studies found that AMs with convolutional neural networks (CNNs) can directly use the raw waveform signal as input. Given sufficient training…
Deep learning improves GW signal detection efficiency and robustness.
problem Traditional matched-filtering techniques are limited in detecting new GW signals.
method Optimized CNN models with techniques like batch normalization and dropout.
result CNN models are robust to the variation of GW waveform parameters.
An asset network systemic risk (ANWSER) model is presented to investigate the impact of how shadow banks are intermingled in a financial system on the severity of financial contagion. Particularly, the focus of this study is the impact of the following three representative topologies of an interbank loan network betwee…
Graph processes model a number of important problems such as identifying the epicenter of an earthquake or predicting weather. In this paper, we propose a Graph Convolutional Recurrent Neural Network (GCRNN) architecture specifically tailored to deal with these problems. GCRNNs use convolutional filter banks to keep th…
The study compares profitability of conventional and Islamic banks in Bangladesh.
problem Evaluating profitability of commercial banks in Bangladesh.
method Examined bank-specific, industry-specific, and banking system factors on profitability.
result Islamic banks consistently outperform conventional banks in profitability.
Study on electronic banking satisfaction in Nigeria.
problem Limited research on factors enhancing end users' satisfaction in electronic banking.
method Empirical analysis of factors influencing electronic banking user satisfaction.
result Factors influencing electronic banking user satisfaction and their relationship with satisfaction.
Study analyzes profitability and efficiency of Chinese banks, finding state-owned banks superior.
problem Analyzing efficiency and profitability of Chinese banks over time.
method Used Data envelopment analysis (Super-SBM-UND-VRS based DEA) model considering non-performing loans as undesired output.
result State-owned banks and Rural/City Commercial Banks have better profitability super-efficiency than Joint-stock Banks.
The European sovereign debt crisis has impaired many European banks. The distress on the European banks may transmit worldwide, and result in a large-scale knock-on default of financial institutions. This study presents a computer simulation model to analyze the risk of insolvency of banks and defaults in a bank credit…
This paper examines SVB's failure and its impact on bank stocks.
problem SVB failure and its contagion effects on bank stocks.
method Analyzed bank-specific vulnerabilities and stock performance.
result Uninsured deposits and unrealized losses were key factors in SVB's impact.
We report a study of a stylized banking cascade model investigating systemic risk caused by counter party failure using liabilities and assets to define banks' balance sheet. In our stylized system, banks can be in two states: normally operating or distressed and the state of a bank changes from normally operating to d…
We consider the problem of governing systemic risk in an assets-liabilities dynamical model of banking system. In the model considered each bank is represented by its assets and its liabilities.The capital reserves of a bank are the difference between assets and liabilities of the bank. A bank is solvent when its capit…
Unveiling Early Warning Signals of Systemic Risks in Banks: A Recurrence Network-Based Approachq-fin.RM
This study uses high-frequency data to identify early warning signals for bank crises.
problem Identifying early warning signals for impending bank crises.
method Constructing multiple recurrence networks (MRNs) based on high-frequency stock returns to monitor nonlinear dynamics.
result Key indicators of MRNs, particularly average mutual information, provide valuable insights into periods of extreme volatility.
Analysis finds no evidence of banks managing deposit run risk prior to 2023 Regional Banking Crisis.
problem Determining factors for deposit run risk management before a regional banking crisis.
method Cross-sectional analysis of interest rate and equity use by banks.
result No evidence of banks managing deposit run risk via their balance sheet.
Study examines factors influencing lending to SMEs by Kenyan banks.
problem Lack of creditworthiness makes SMEs difficult to finance by banks.
method Descriptive research design, census of 43 banks, secondary data analysis.
result Bank size and liquidity significantly influence lending to SMEs, while credit risk and interest rates do not.
Bangladesh's banking sector improved through financial reforms, but challenges remain.
problem Weak asset quality, inadequate provisioning, and negative capitalization of state-owned banks.
method Two phases of reforms: private ownership promotion and gradual deregulation.
result Significant improvements in asset quality and capitalization, but challenges persist.
Study evaluates profitability of Islamic banks in Bangladesh using ROA, ROE, and ROD.
problem Evaluating profitability of Islamic banks in Bangladesh.
method Used ROA, ROE, and ROD as measures, analyzed relationships with AU and OE.
result ROD significantly associated with ROA, but not with OE and AU.
A modern version of Monetary Circuit Theory with a particular emphasis on stochastic underpinning mechanisms is developed. It is explained how money is created by the banking system as a whole and by individual banks. The role of central banks as system stabilizers and liquidity providers is elucidated. It is shown how…
Research examines how Islamic banking principles spread among managers and scholars.
problem Diffusion of Islamic banking principles among managers and scholars.
method Literature review focusing on knowledge diffusion and Islamic banking governance principles.
result Emergence of common Islamic banking governance principles from diverse knowledge streams.
Modeling financial contagion through bank networks, revealing solvency correlations.
problem Understanding how financial shocks propagate through interconnected banks.
method Simulated financial network of 100 banks, randomly generated with varying link probabilities, and shocks applied to 15 banks.
result Ranges of probability values and banks' solvency are positively correlated.
We analyze the influence and interactions of 60 largest world banks for 195 world countries using the reduced Google matrix algorithm for the English Wikipedia network with 5 416 537 articles. While the top asset rank positions are taken by the banks of China, with China Industrial and Commercial Bank of China at the f…
In the wake of the still ongoing global financial crisis, bank interdependencies have come into focus in trying to assess linkages among banks and systemic risk. To date, such analysis has largely been based on numerical data. By contrast, this study attempts to gain further insight into bank interconnections by tappin…
The paper discusses fairness in bank stress tests, comparing various methods to address institutional differences.
problem Fair aggregation of bank-specific stress test models into a common model.
method Comparing various notions of regression fairness, including estimating and discarding centered bank fixed effects.
result The method of estimating and discarding centered bank fixed effects is preferable for linear models, improving forecast accuracy and equal treatment.
Oil prices affect Russian banks' stability, with negative impacts from decreases.
problem The impact of international oil prices on Russian public banks' financial stability.
method Data from 17 Russian public banks (2008-2016), Pool Mean Group (PMG) estimator.
result An increase in international oil prices and price to book value ratio positively affects Russian public banks' stability in the long run, while negative shocks have the opposite effect.
Contagion Flow Through Banking Networkscond-mat.other
Based on an empirical analysis of the network structure of the Austrian inter-bank market, we study the flow of funds through the banking network following exogenous shocks to the system. These shocks are implemented by stochastic changes in variables like interest rates, exchange rates, etc. We demonstrate that the sy…
Steerable CNNscs.LG
It has long been recognized that the invariance and equivariance properties of a representation are critically important for success in many vision tasks. In this paper we present Steerable Convolutional Neural Networks, an efficient and flexible class of equivariant convolutional networks. We show that steerable CNNs …
Structured factor copulas for modeling the systemic risk of European and United States banksq-fin.ST
The paper models systemic risk in European and U.S. banks using factor copulas.
problem Modeling the joint and conditional distress probabilities of banks across Europe and the U.S.
method Employing Credit Default Swaps (CDS) and factor copulas, the paper proposes multi-factor, structured factor, and factor-vine models.
result Systematic contagion channel drives distress probabilities in the banking system as a whole, while regional factors are important within each region.
Bailouts in financial networks are hard to optimize due to NP-hardness.
problem Optimizing bailouts in a network of insolvent banks.
method Modeling bailouts as an optimization problem, proving NP-hardness and inapproximability.
result Banks can strategically alter debt contracts to increase their market value in the event of a bailout.
Study evaluates sustainability of European banks using a new model.
problem Lack of a framework to evaluate sustainability of banking business models.
method Delphi-Analytic Hierarchy Process method to develop and assess the model.
result Norwegian and German banks have higher sustainability of their business models.
We use bank-level balance sheet data from 2005 to 2010 to study interactions within the banking system of five emerging countries: Argentina, Brazil, Mexico, South Africa, and Taiwan. For each country we construct a financial network based on the leverage ratio dependence between each pair of banks, and find results th…
Study assesses the impact of Basel III reforms on Bangladeshi banks.
problem Impact of Basel III liquidity and capital requirements on Bangladeshi banks.
method Panel data analysis with fixed effects, including macroeconomic variables.
result Higher capital and liquidity requirements negatively affect banks' profitability but positively impact interest rates and private sector lending.