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On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression

Jun Qi, Jun Du, Sabato Marco Siniscalchi, Xiaoli Ma, Chin-Hui Lee
2020 IEEE Signal Processing Letters  
In this paper, we exploit the properties of mean absolute error (MAE) as a loss function for the deep neural network (DNN) based vector-to-vector regression.  ...  The goal of this work is two-fold: (i) presenting performance bounds of MAE, and (ii) demonstrating new properties of MAE that make it more appropriate than mean squared error (MSE) as a loss function  ...  We employ the fact that DNNs with the ReLU activation function are Lipschitz continuous [36] .  ... 
doi:10.1109/lsp.2020.3016837 fatcat:2mvc47bh6nbltb63upet7anpiu

Non Local Means Estimation of Intrinsic Mode Functions for Speech Enhancement

2019 Turkish Journal of Electrical Engineering and Computer Sciences  
The main aim of this paper is to introduce a new approach to enhance speech signals by exploring the advantages of nonlocal means (NLM) estimation and empirical mode decomposition.  ...  To address this issue, initially, the speech signal is decomposed into oscillatory components called intrinsic mode functions (IMFs) by using a temporal decomposition technique known as the sifting process  ...  The first condition is necessary to get a meaningful instantaneous frequency. If the neighboring wave amplitudes are too large a discrepancy, then the second condition is also necessary.  ... 
doi:10.3906/elk-1901-86 fatcat:wxkaauq7sne4dfpfdf5b5w7gra

Single Channel Speech Enhancement using Wiener Filter and Compressive Sensing

Amart Sulong, Teddy Surya Gunawan, Othman O Khalifa, Mira Kartiwi, Hassan Dao
2017 International Journal of Electrical and Computer Engineering (IJECE)  
perceptual improvement aspects to listener fatigue with noiseless reduction conditions.  ...  The proposed algorithm shows an enhancement in testing performance evaluation of objective assessment tests outperform compared to other conventional algorithms at various noise type conditions of 0, 5  ...  Table 1 shown that the worst case appear with 0dB at all type of noise conditions.  ... 
doi:10.11591/ijece.v7i4.pp1941-1951 fatcat:tpbmewzndzb7dcrwqhmz62fgjm

Construct Validity of the Ecological Momentary Assessment in Audiology Research

Yu-Hsiang Wu, Elizabeth Stangl, Xuyang Zhang, Ruth A. Bentler
2015 Journal of american academy of audiology  
Nevertheless, higher noisiness rating was associated with higher background noise level.  ...  Ecological momentary assessment (EMA) is a methodology involving repeated assessments/ surveys to collect data describing respondents' current or very recent experiences and related contexts in their natural  ...  During the week, whenever the participants had a listening condition .10 min, they described the auditory activity and acoustic environment of that condition in the journal.  ... 
doi:10.3766/jaaa.15034 pmid:26554491 pmcid:PMC4732705 fatcat:gl6uxx5rcjaydhopc5r7r7gvaa

Emd-Based Noise Estimation And Tracking (Enet) With Application To Speech Enhancement

Navin Chatlani, J.J. Soraghan
2009 Zenodo  
activity.  ...  The background necessary to understand the EMD and IMCRA is first presented in sections 2 and 3 respectively. In section 4, the novel ENET system with application to speech enhancement is developed.  ... 
doi:10.5281/zenodo.41464 fatcat:n3bpjpfbvfd43ausaxm2szfve4

Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network Based Vector-to-Vector Regression

Jun Qi, Jun Du, Sabato Marco Siniscalchi, Xiaoli Ma, Chin-Hui Lee
2020 IEEE Transactions on Signal Processing  
Moreover, we assess our theoretical results through a set of image de-noising and speech enhancement experiments.  ...  Leveraging upon error decomposition techniques in statistical learning theory and non-convex optimization theory, we derive upper bounds for each of the three aforementioned errors and impose necessary  ...  The experimental results as shown in Table VI are in line with those observed in the consistent noisy conditions.  ... 
doi:10.1109/tsp.2020.2993164 fatcat:kxp5f65xxfakrg2rqqdt3areni

Noise reduction of NDVI time series: An empirical comparison of selected techniques

Jennifer N. Hird, Gregory J. McDermid
2009 Remote Sensing of Environment  
A model-based empirical comparison of six selected NDVI time series noise-reduction techniques revealed the general superiority of the double logistic and asymmetric Gaussian function-fitting methods over  ...  Satellite-derived NDVI time series are fundamental to the remote sensing of vegetation phenology, but their application is hindered by prevalent noise resulting chiefly from varying atmospheric conditions  ...  dates (i.e. low, moderate and high levels of noise) with values from noisy pixels in the study area (Fig. 3) .  ... 
doi:10.1016/j.rse.2008.09.003 fatcat:nku3ljuxn5cnxm3km6hvca2ive

ISA - an inverse surface-based approach for cortical fMRI data projection

Lucie Thiebaut Lonjaret, Christine Bakhous, Timothe Boutelier, Sylvain Takerkart, Olivier Coulon
2017 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)  
Surface-based approaches have proven particularly relevant and reliable to study cortical functional magnetic resonance imaging (fMRI) data.  ...  This approach shows interesting perspectives for fMRI data processing as it is highly robust to noise and offers a good accuracy in terms of activations localization.  ...  Under noisy conditions ISA outperforms the convolution kernels both in terms of localization and correlation to the experimental paradigm ( Fig. 2.a,b) .  ... 
doi:10.1109/isbi.2017.7950709 dblp:conf/isbi/LonjaretBBTC17 fatcat:ql3judm4vzfp3lcygl43r2uuui

BoostEMD: An Extension of EMD Method and Its Application for Denoising of EMG Signals

Robertas Damasevicius, Mindaugas Vasiljevas, Ignas Martisius, Vacius Jusas, Darius Birvinskas, Marcin Wozniak
2015 Elektronika ir Elektrotechnika  
The paper presents a novel extension of the Huang's Empirical Mode Decomposition (EMD) method, called BoostEMD, that allows calculating higher order Intrinsic Mode Functions (IMFs) that capture higher  ...  frequency empirical mode oscillations (empiquencies) in the EMG (electromyography) data.  ...  Using the x component signal with data length of 1000 and adding Gaussian white noise, we generate a noisy Lorenz signal with SNR equal to 5 dB.  ... 
doi:10.5755/j01.eie.21.6.13763 fatcat:hdhet35w3vhrhndxqjnmqqy6dq

Exploring functional relations between brain regions from fMRI meta-analysis data: Comments on Ramsey, Spirtes, and Glymour

Jane Neumann, Robert Turner, Peter T. Fox, Gabriele Lohmann
2011 NeuroImage  
s critical assessment of our method for learning partially directed graphs from meta-analysis imaging data (Neumann et al., 2010) .  ...  This is an important problem which, no doubt, deserves careful consideration in the context of functional neuroimaging.  ...  Such additional assessment can reach from counting and categorizing the experimental paradigms that gave rise to the individual co-activation patterns to a detailed investigation of the functional heterogeneity  ... 
doi:10.1016/j.neuroimage.2010.11.012 pmid:21075207 pmcid:PMC3048168 fatcat:2xakp6iiqjaznccgo44fwnhgsm

Dynamic Causal Modelling of Dynamic Dysfunction in NMDA-Receptor Antibody Encephalitis [chapter]

Richard E. Rosch, Gerald Cooray, Karl J. Friston
2017 Springer Series in Bio-/Neuroinformatics  
In order to assess which of these parameter changes is necessary for the observed differences conditions, Bayesian model selection was performed over a set of reduced model, where only a subset of parameters  ...  Clinically this new way of assessing brain function has had significant impact on our understanding of a number of neurological and psychiatric conditions, but none more so than epileptic seizure disorders  ... 
doi:10.1007/978-3-319-49959-8_6 fatcat:jywv2zhg25cy7kh33mnn5pef5i

Autosploit: A Fully Automated Framework for Evaluating the Exploitability of Security Vulnerabilities [article]

Noam Moscovich, Ron Bitton, Yakov Mallah, Masaki Inokuchi, Tomohiko Yagyu, Meir Kalech, Yuval Elovici, Asaf Shabtai
2020 arXiv   pre-print
These important results can be utilized for more accurate and effective risk assessment.  ...  Given a vulnerable environment and relevant exploits, Autosploit will automatically test the exploits on different configurations of the environment in order to identify the specific properties necessary  ...  Introduction Risk assessment is an activity essential for improving the security of an enterprise network [15] .  ... 
arXiv:2007.00059v1 fatcat:rlfq5atsjjbflmywz6b2wwjhka

Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions

Christoph Werner, Tim Bedford, Roger M. Cooke, Anca M. Hanea, Oswaldo Morales-Nápoles
2017 European Journal of Operational Research  
Many applications in decision making under uncertainty and probabilistic risk assessment require the assessment of multiple, dependent uncertain quantities, so that in addition to marginal distributions  ...  In expert judgement studies, a structured approach to eliciting variables of interest is desirable so that their assessment is methodologically robust.  ...  Empirical findings of the method 970 Figure 4 : 4 Expert's conditional probability assessment as a function of the product moment correlation coefficient.  ... 
doi:10.1016/j.ejor.2016.10.018 fatcat:cei45dplmndtfgrjeglx42g3ie

An extension to the noisy-OR function to resolve the 'explaining away' deficiency for practical Bayesian network problems

Norman Fenton, Takao Noguchi, Martin Neil
2019 IEEE Transactions on Knowledge and Data Engineering  
The "leaky noisy-OR" function is a common and popular method used to simplify the elicitation of complex conditional probability tables in Bayesian networks involving Boolean variables.  ...  However, one of the properties of leaky noisy-OR is Conditional Inter-causal Independence (CII).  ...  One of the most formidable practical challenges in building BN models for decision support and risk assessment is to define the necessary conditional probability tables (CPTs).  ... 
doi:10.1109/tkde.2019.2891680 fatcat:bahm3omsbjbbjjoghhhvhdnume

QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks [article]

Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen
2021 arXiv   pre-print
The advent of noisy intermediate-scale quantum (NISQ) computers raises a crucial challenge to design quantum neural networks for fully quantum learning tasks.  ...  different activation functions.  ...  The activation function used in TTN Table 3 compares the results of QTN-VQC based on different activation functions.  ... 
arXiv:2110.03861v3 fatcat:prx3rasqnrclzhk777ut2drrby
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