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Minimal Adversarial Perturbations In Mobile Health Applications: The Epileptic Brain Activity Case Study
Today, the security of wearable and mobile-health technologies represents one of the main challenges in the Internet of Things (IoT) era. Adversarial manipulation of sensitive health-related information, e.g., if such information is used for prescribing m
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A Fast And Accurate Frequent Directions Algorithm For Low Rank Approximation Via Block Krylov Iteration
It is known that frequent directions (FD) is a popular deterministic matrix sketching method for low rank approximation. However, FD and its randomized variants usually meet high computational cost or computational instability in dealing with large-scale
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Privacy-Preserving Pattern Recognition Using Encrypted Sparse Representations In L0 Norm Minimization
In this paper, we propose a privacy-preserving pattern recognition method that uses encrypted sparse representations in L0 norm minimization. We prove, theoretically, that the proposal has exactly the same dictionary and sparse coefficient estimation perf
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Label Propagation Adaptive Resonance Theory For Semi-Supervised Continuous Learning
Semi-supervised learning and continuous learning are fundamental paradigms for human-level intelligence. To deal with real-world problems where labels are rarely given and the opportunity to access the same data is limited, it is necessary to apply these
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Efficient Super-Resolution Two-Dimensional Harmonic Retrieval Via Enhanced Low-Rank Structured Covariance Reconstruction
This paper develops an enhanced low-rank structured covariance reconstruction (LRSCR) method based on the decoupled atomic norm minimization (D-ANM), for super-resolution two-dimensional (2D) harmonic retrieval with multiple measurement vectors. This LRSC
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Deep Flow Collaborative Network For Online Visual Tracking
The deep learning-based visual tracking algorithms such as MDNet achieve high performance leveraging to the feature extraction ability of a deep neural network. However, the tracking efficiency of these trackers is not very high due to the slow feature ex
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On The Impact Of Language Familiarity In Talker Change Detection
The ability to detect talker changes when listening to conversational speech is fundamental to the perception and understanding of multi-talker speech. In this paper, we propose a novel experimental paradigm to provide insights on the impact of language f
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Addressing Accent Mismatch In Mandarin-English Code-Switching Speech Recognition
Automatic speech recognition systems suffer from accuracy degradation when code-switching (multiple languages are spoken in a single utterance) is encountered. This is especially common for non-native speakers where there is a mismatch between speech and
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Expression-Guided Eeg Representation Learning For Emotion Recognition
Learning a joint and coordinated representation between different modalities can improve multimodal emotion recognition. In this paper, we propose a deep representation learning approach for emotion recognition from electroencephalogram (EEG) signals guid
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Fusionndvi: A Novel Fusion Method For Ndvi In Remote Sensing
Normalized difference vegetation index (NDVI) is widely utilized to examine vegetation coverage and estimate crop yield. To obtain a high-resolution (HR) NDVI, fusion techniques, which first generates a HR multispectral (MS) image by fusing a low-resoluti
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Dynamic Resource Optimization And Altitude Selection In Uav-Based Multi-Access Edge Computing
The aim of this work is to develop a dynamic optimization strategy to allocate communication and computation resources in a Multi-access Edge Computing (MEC) scenario, where Unmanned AerialVehicles (UAVs) act as flying base station platforms endowed with
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Cp-Gan: Context Pyramid Generative Adversarial Network For Speech Enhancement
The topic of speech enhancement has been largely improved recently, especially with the development of generative adversarial networks (GANs). However prior methods simply follow the GAN architectures from computer vision tasks without specific designs fo
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Towards Linking The Lakh And Imslp Datasets
This paper investigates the problem of matching a MIDI file against a large database of piano sheet music images. Previous sheet-audio and sheet-MIDI alignment approaches have primarily focused on a 1-to-1 alignment task, which is not a scalable solution
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Efficient Shallow Wavenet Vocoder Using Multiple Samples Output Based On Laplacian Distribution And Linear Prediction
This paper presents a novel way for an efficient implementation scheme of shallow WaveNet vocoder with multiple samples (segment) output based on the use of Laplacian distribution and linear prediction. In our previous work, we have proposed a shallow arc
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End-To-End Spoken Language Understanding Without Matched Language Speech Model Pretraining Data
In contrast to conventional approaches to spoken language understanding (SLU) that consist of cascading a speech recognizer with a natural language understanding component, end-to-end (E2E) approaches for SLU infer semantics directly from the speech signa
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Two-Dimensional Doa Estimation For Coprime Planar Array: A Coarray Tensor-Based Solution
Coprime arrays can cope with the underdetermined case for direction-of-arrival (DOA) estimation. However, the popular matrix-based coarray signal processing approaches suffer performance loss on the underlying characteristics among the multi-dimensional s
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Voice Activity Detection For Transient Noisy Environment Based On Diffusion Nets
We address voice activity detection in acoustic environments of transients and stationary noises, which often occur in real-life scenarios. We exploit unique spatial patterns of speech and non-speech audio frames by independently learning their underlying
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Chronological Age Estimation Under The Guidance Of Age-Related Facial Attributes
Although the researches of facial attributes' analysis have been launched for decades, the estimation of chronological age attribute remains a big challenge. Previous researchers have found that some facial attributes (e.g., gender and race attributes) ha
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Real-Time Acoustic Scene Classification For Hearing Aids
Acoustic scene classification is a popular topic mostly combining the fields of audio signal processing and machine learning. Particularly the detection and classification of acoustic scenes and events (DCASE) challenge, which is held each year, increased
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IEEE ICASSP 2020 - State of the Society, Town Hall
IEEE ICASSP 2020 - State of the Society, Town Hall, by Dr. Ahmed Tewfik, May 2020.
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A Random Gossip Bmuf Process For Neural Language Modeling
Neural network language model (NNLM) is an essential component of industrial ASR systems. One important challenge of training an NNLM is to leverage between scaling the learning process and handling big data. Conventional approaches such as block momentum
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Consistency-Aware Multi-Channel Speech Enhancement Using Deep Neural Networks
This paper proposes a deep neural network (DNN)--based multi-channel speech enhancement system in which a DNN is trained to maximize the quality of the enhanced time-domain signal. DNN-based multi-channel speech enhancement is often conducted in the time-
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Unsupervised Training For Deep Speech Source Separation With Kullback-Leibler Divergence Based Probabilistic Loss Function
In this paper, we propose a multi-channel speech source separation method with a deep neural network (DNN) which is trained under the condition that no clean signal is available. As an alternative to a clean signal, the proposed method adopts an estimated
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A Deep Gradient Boosting Network For Optic Disc And Cup Segmentation
Segmentation of optic disc (OD) and optic cup (OC) is critical in automated fundus image analysis system. Existing state-ofthe-arts focus on designing deep neural networks with one or multiple dense prediction branches. Such kind of designs ignore connect
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Feature Selection Under Orthogonal Regression With Redundancy Minimizing
Various supervised embedded methods have been proposed to select discriminative features from original ones, such as Feature Selection with Orthogonal Regression (FSOR) and Robust Feature Selection. Compared with embedded methods based on the least square
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Audio Codec Enhancement With Generative Adversarial Networks
Audio codecs are typically transform-domain based and efficiently code stationary audio signals, but they struggle with speech and signals containing dense transient events such as applause. Specifically, with these two classes of signals as examples, we
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Differentiable Branching In Deep Networks For Fast Inference
In this paper, we consider the design of deep neural networks augmented with multiple auxiliary classifiers departing from the main (backbone) network. These classifiers can be used to perform early-exit from the network at various layers, making them con
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Cross Image Cubic Interpolator For Spatially Varying Exposures
Spatially varying exposures via rolling shutter is an efficient way to capture differently exposed images for high dynamic range (HDR) scenes. Neither camera movement nor moving objects is an issue for such a captured method. However, a possible issue is
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Beyond The Dcase 2017 Challenge On Rare Sound Event Detection: A Proposal For A More Realistic Training And Test Framework
There are many ways to evaluate rare sound event detection (SED) approaches, e.g., the DCASE 2017 challenge provides a widely employed framework. This paper proposes a rare SED training and test framework, which is reflecting an SED application in a more
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Lie Group State Estimation Via Optimal Transport
Many applications in science and engineering involve tracking the state of a stochastic differential equation (SDE) evolving in a Lie group. This has been tackled by particle filtering although some existing schemes fail to satisfy geometric constraints.
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Distributed Quantization For Sparse Time Sequences
Analog signals processed in digital hardware are quantized into a discrete bit-constrained representation. Quantization is typically carried out using analog-to-digital converters (ADCs), operating in a serial scalar manner. In some applications, a set of
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Maximum Likelihood Estimation Of The Interference-Plus-Noise Cross Power Spectral Density Matrix For Own Voice Retrieval
In headset and hearing aid applications, it is of interest to retrieve the user's own voice in a noisy environment, e.g. for telephony applications. To do so, the cross power spectral density (CPSD) of the noise is required. In this paper, a novel maximum
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Multitask Learning And Multistage Fusion For Dimensional Audiovisual Emotion Recognition
Due to its ability to accurately predict emotional state using multimodal features, audiovisual emotion recognition has recently gained more interest from researchers. This paper proposes two methods to predict emotional attributes from audio and visual d
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Joint Resource Allocation And Routing For Service Function Chaining With In-Subnetwork Processing
Network Function Virtualization (NFV) is an efficient approach to simplify and accelerate the deployment of diverse network services. A critical challenge lies in mapping Virtual Network Functions (VNFs) to high-volume servers, resource allocation, and tr
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On-The-Fly Feature Selection And Classification With Application To Civic Engagement Platforms
Online feature selection and classification is crucial for time sensitive decision making. Existing work however either assumes that features are independent or produces a fixed number of features for classification. Instead, we propose an optimal framewo
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Mutual-Information-Based Sensor Placement For Spatial Sound Field Recording
A sensor (microphone) placement method based on mutual information for spatial sound field recording is proposed. The sound field recording methods using distributed sensors enable the estimation of the sound field inside a target region of arbitrary shap
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Dynamic Variational Autoencoders For Visual Process Modeling
This work studies the problem of modeling visual processes by leveraging deep generative architectures for learning linear, Gaussian representations from observed sequences. We propose a joint learning framework, combining a vector autoregressive model an
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Exploiting Rays In Blind Localization Of Distributed Sensor Arrays
Many signal processing algorithms for distributed sensors are capable of improving their performance if the positions of sensors are known. In this paper, we focus on estimators for inferring the relative geometry of distributed arrays and sources, i.e. t
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Opendenoising: An Extensible Benchmark For Building Comparative Studies Of Image Denoisers
Image denoising has recently taken a leap forward due to machine learning. However, image denoisers, both expert-based and learning-based, are mostly tested on well-behaved generated noises (usually Gaussian) rather than on real-life noises, making perfor
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Adaptive Blind Audio Source Extraction Supervised By Dominant Speaker Identification Using X-Vectors
We propose a novel algorithm for adaptive blind audio source extraction. The proposed method is based on independent vector analysis and utilizes the auxiliary function optimization to achieve high convergence speed. The algorithm is partially supervised
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Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is a perceptual-driven approach for single image super-resolution that is able to produce photorealistic images. Despite the visual quality of these generated images, there is still room fo
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Vamp With Vector-Valued Diagonalization
Vector approximate message passing is studied where vector-valued diagonalization instead of a uniform one is employed. Thereby, individual variances are tracked within the algorithm instead of an average one. Straightforward application based on the expe
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Sparse Directed Graph Learning For Head Movement Prediction In 360 Video Streaming
High-definition 360 videos encoded in fine quality are typically too large to stream in its entirety over bandwidth (BW)-constrained networks. One popular remedy is to extract and send a spatial sub-region corresponding to a viewer's current field-of-view
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Bandwidth Extension Of Musical Audio Signals With No Side Information Using Dilated Convolutional Neural Networks
Bandwidth extension has a long history in audio processing. While speech processing tools do not rely on side information, production-ready bandwidth extension tools of general audio signals rely on side information that has to be transmitted alongside th
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Cif: Continuous Integrate-And-Fire For End-To-End Speech Recognition
In this paper, we propose a novel soft and monotonic alignment mechanism used for sequence transduction. It is inspired by the integrate-and-fire model in spiking neural networks and employed in the encoder-decoder framework consists of continuous functio
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Gray-Scale Image Colorization Using Cycle-Consistent Generative Adversarial Networks With Residual Structure Enhancer
The colorization of gray-scale images has always been a challenging task in computer vision. Recently, novel approaches have been introduced for unsupervised image translation between two domains using Generative Adversarial Networks (GANs). Since one can
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Inferring Dynamic Group Leadership Using Sequential Bayesian Methods
In group object tracking, the identification of the group leader can be highly beneficial for predicting the intention and future manoeuvres of objects as well as learning the underlying group behaviour traits. This paper presents an online approach for i
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Audio-Visual Recognition Of Overlapped Speech For The Lrs2 Dataset
Automatic recognition of overlapped speech remains a highly challenging task to date. Motivated by the bimodal nature of human speech perception, this paper investigates the use of audio-visual technologies for overlapped speech recognition. Three issues