pwc-archive/papers-with-abstracts huggingface dataset:
Exploring the Importance of F0 Trajectories for Speaker Anonymization using X-vectors and Neural Waveform Models
Authors: Ünal Ege Gaznepoglu, Nils Peters
Date: 2021-10-13 00:00:00
Tasks: Resynthesis, Speaker anonymization, Voice Conversion

Voice conversion for speaker anonymization is an emerging field in speech processing research. Many state-of-the-art approaches are based on the resynthesis of the phoneme posteriorgrams (PPG), the fundamental frequency (F0) of the input signal together with modified X-vectors. Our research focuses on the role of F0 for speaker anonymization, which is an understudied area. Utilizing the VoicePrivacy Challenge 2020 framework and its datasets we developed and evaluated eight low-complexity F0 modifications prior resynthesis. We found that modifying the F0 can improve speaker anonymization by as much as 8% with minor word-error rate degradation.
Revisiting Speech Content Privacy
Authors: Jennifer Williams, Junichi Yamagishi, Paul-Gauthier Noe, Cassia Valentini Botinhao, Jean-Francois Bonastre
Date: 2021-10-13 00:00:00

In this paper, we discuss an important aspect of speech privacy: protecting spoken content. New capabilities from the field of machine learning provide a unique and timely opportunity to revisit speech content protection. There are many different applications of content privacy, even though this area has been under-explored in speech technology research. This paper presents several scenarios that indicate a need for speech content privacy even as the specific techniques to achieve content privacy may necessarily vary. Our discussion includes several different types of content privacy including recoverable and non-recoverable content. Finally, we introduce evaluation strategies as well as describe some of the difficulties that may be encountered.
Independence-based Joint Dereverberation and Separation with Neural Source Model
Authors: Kohei Saijo, Robin Scheibler
Date: 2021-10-13 00:00:00
Tasks: Speech Dereverberation

We propose an independence-based joint dereverberation and separation method with a neural source model. We introduce a neural network in the framework of time-decorrelation iterative source steering, which is an extension of independent vector analysis to joint dereverberation and separation. The network is trained in an end-to-end manner with a permutation invariant loss on the time-domain separation output signals. Our proposed method can be applied in any situation with at least as many microphones as sources, regardless of their number. In experiments, we demonstrate that our method results in high performance in terms of both speech quality metrics and word error rate (WER), even for mixtures with a different number of speakers than training. Furthermore, the model, trained on synthetic mixtures, without any modifications, greatly reduces the WER on the recorded dataset LibriCSS.
A Teacher-Student Framework with Fourier Augmentation for COVID-19 Infection Segmentation in CT Images
Authors: Han Chen, Yifan Jiang, Hanseok Ko, Murray Loew
Date: 2021-10-13 00:00:00
Tasks: Computed Tomography (CT), Segmentation

Automatic segmentation of infected regions in computed tomography (CT) images is necessary for the initial diagnosis of COVID-19. Deep-learning-based methods have the potential to automate this task but require a large amount of data with pixel-level annotations. Training a deep network with annotated lung cancer CT images, which are easier to obtain, can alleviate this problem to some extent. However, this approach may suffer from a reduction in performance when applied to unseen COVID-19 images during the testing phase due to the domain shift. In this paper, we propose a novel unsupervised method for COVID-19 infection segmentation that aims to learn the domain-invariant features from lung cancer and COVID-19 images to improve the generalization ability of the segmentation network for use with COVID-19 CT images. To overcome the intensity shift, our method first transforms annotated lung cancer data into the style of unlabeled COVID-19 data using an effective augmentation approach via a Fourier transform. Furthermore, to reduce the distribution shift, we design a teacher-student network to learn rotation-invariant features for segmentation. Experiments demonstrate that even without getting access to the annotations of COVID-19 CT during training, the proposed network can achieve a state-of-the-art segmentation performance on COVID-19 images.
Towards Safer Transportation: a self-supervised learning approach for traffic video deraining
Authors: Shuya Zong, Sikai Chen, Samuel Labi
Date: 2021-10-11 00:00:00
Tasks: Management, Rain Removal, Self-Supervised Learning, Video deraining

Video monitoring of traffic is useful for traffic management and control, traffic counting, and traffic law enforcement. However, traffic monitoring during inclement weather such as rain is a challenging task because video quality is corrupted by streaks of falling rain on the video image, and this hinders reliable characterization not only of the road environment but also of road-user behavior during such adverse weather events. This study proposes a two-stage self-supervised learning method to remove rain streaks in traffic videos. The first and second stages address intra- and inter-frame noise, respectively. The results indicated that the model exhibits satisfactory performance in terms of the image visual quality and the Peak Signal-Noise Ratio value.
On the Nonlinear Distortion Characterization in Photovoltaic Modules for Visible Light Communication
Authors: Shuyan Chen, Liqiong Liu, Lian-Kuan Chen
Date: 2021-10-14 00:00:00

Photovoltaic (PV) modules have been employed in visible light communication (VLC) for simultaneous energy harvesting and data reception. A PV-based receiver features easy optical alignment and self-powered operation. It is commonly assumed that PV modules in VLC have a linear optical-electrical response, which is generally true under high illumination levels. This paper will illustrate the exacerbated PV's nonlinear distortion when under typical indoor illumination. The nonlinearity of a PV module for different numbers of PV cells is also characterized. We investigated the transmission performance of a 1-Mbit/s PAM4 signal under different illuminance. Experimental results show that the bit-error rate (BER) first decreases and then increases with the increasing illuminance. Thus, an optimal illuminance to minimize BER exists. In addition, we demonstrated two distortion mitigation methods, namely localized distortion compensation lighting and post-distortion compensation. BER reduction from 3.2x10-1 to 2.6x10-3 and 2.8x10-2 to 1.5x10-2 are achieved with the two respective schemes.
Node-screening tests for L0-penalized least-squares problem with supplementary material
Authors: Théo Guyard, Cédric Herzet, Clément Elvira
Date: 2021-10-14 00:00:00
Tasks: regression

We present a novel screening methodology to safely discard irrelevant nodes within a generic branch-and-bound (BnB) algorithm solving the l0-penalized least-squares problem. Our contribution is a set of two simple tests to detect sets of feasible vectors that cannot yield optimal solutions. This allows to prune nodes of the BnB search tree, thus reducing the overall optimization time. One cornerstone of our contribution is a nesting property between tests at different nodes that allows to implement them with a low computational cost. Our work leverages the concept of safe screening, well known for sparsity-inducing convex problems, and some recent advances in this field for l0-penalized regression problems.
Statistical-mechanical analysis of adaptive filter with clipping saturation-type nonlinearity
Authors: Seiji Miyoshi
Date: 2021-10-13 00:00:00
Tasks: Acoustic echo cancellation, Vocal Bursts Type Prediction

In most practical adaptive signal processing systems, e.g., active noise control, active vibration control, and acoustic echo cancellation, substantial nonlinearities that cannot be neglected exist. In this paper, we analyze the behaviors of an adaptive system in which the output of the adaptive filter has the clipping saturation-type nonlinearity by a statistical-mechanical method. We discuss the dynamical and steady-state behaviors of the adaptive system by asymptotic analysis, steady-state analysis, and numerical calculation. As a result, it has become clear that the saturation value has the critical point at which the system's mean-square stability and instability switch. The obtained theory well explains the strange behaviors around the critical point observed in the computer simulation. Finally, the exact value of the critical point is also derived.
A Deep Learning Approach to Location- and Orientation-aided 3D Beam Selection for mmWave Communications
Authors: Sajad Rezaie, Elisabeth de Carvalho, Carles Navarro Manchón
Date: 2021-10-13 00:00:00
Tasks: Position

Position-aided beam selection methods have been shown to be an effective approach to achieve high beamforming gain while limiting the overhead and latency of initial access in millimeter wave (mmWave) communications. Most research in the area, however, has focused on vehicular applications, where the orientation of the user terminal (UT) is mostly fixed at each position of the environment. This paper proposes a location- and orientation-based beam selection method to enable context information (CI)-based beam alignment in applications where the UT can take arbitrary orientation at each location. We propose three different network structures, with different amounts of trainable parameters that can be used with different training dataset sizes. A professional 3-dimensional ray tracing tool is used to generate datasets for an IEEE standard indoor scenario. Numerical results show the proposed networks outperform a CI-aided benchmark such as the generalized inverse fingerprinting (GIFP) method as well as hierarchical beam search as a non-CI-based approach. Moreover, compared to the GIFP method, the proposed deep learning-based beam selection shows higher robustness to different line-of-sight blockage probability in the training and test datasets and lower sensitivity to inaccuracies in the position and orientation information.
Stepped-frequency DORT Imaging with Singular Value Decomposition for a Single-antenna Ultrawideband Radar in Multi-path Environments
Authors: Takuya Sakamoto
Date: 2021-10-13 00:00:00

Ultrawideband radar is an attractive technology for a variety of applications including security systems. As such, it is essential to develop low-cost systems that produce clear target images. Electromagnetic inverse scattering with time-reversal imaging has been studied for a variety of applications. The time-reversal method, however, uses large-scale antenna arrays, making the system potentially more costly. In this study, we propose an ultrawideband radar imaging algorithm, namely the stepped-frequency DORT algorithm, that uses multi-path scattering for a single antenna. The proposed imaging method is an extension of the conventional DORT method, and uses a frequency-frequency matrix that is suitable for a system with a single antenna. The performance of the proposed method is verified through numerical simulations.
A Memory-Efficient Learning Framework for SymbolLevel Precoding with Quantized NN Weights
Authors: Abdullahi Mohammad, Christos Masouros, Yiannis Andreopoulos
Date: 2021-10-13 00:00:00
Tasks: Model Compression, Quantization

This paper proposes a memory-efficient deep neural network (DNN) framework-based symbol level precoding (SLP). We focus on a DNN with realistic finite precision weights and adopt an unsupervised deep learning (DL) based SLP model (SLP-DNet). We apply a stochastic quantization (SQ) technique to obtain its corresponding quantized version called SLP-SQDNet. The proposed scheme offers a scalable performance vs memory tradeoff, by quantizing a scale-able percentage of the DNN weights, and we explore binary and ternary quantizations. Our results show that while SLP-DNet provides near-optimal performance, its quantized versions through SQ yield 3.46x and 2.64x model compression for binary-based and ternary-based SLP-SQDNets, respectively. We also find that our proposals offer 20x and 10x computational complexity reductions compared to SLP optimization-based and SLP-DNet, respectively.
DQN-based Beamforming for Uplink mmWave Cellular-Connected UAVs
Authors: Susarla Praneeth, Gouda Bikshapathi, Deng Yansha, Juntti Markku, Silven Olli, Tolli Antti
Date: 2021-10-12 00:00:00
Tasks: Reinforcement Learning (RL)

Unmanned aerial vehicles (UAVs) are the emerging vital components of millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential for efficient beam-based mmWave communications of UAVs with base stations (BSs). Conventional beam sweeping approaches often have large overhead due to the high mobility and autonomous operation of UAVs. Learning-based approaches greatly reduce the overhead by leveraging UAV data, like position to identify optimal beam directions. In this paper, we propose a reinforcement learning (RL)-based framework for UAV-BS beam alignment using deep Q-Network (DQN) in a mmWave setting. We consider uplink communications where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information to maximize data rate through the optimal beam-pairs efficiently, upon every communication request from UAV inside the multi-location environment. We compare our proposed framework against the Multi-Armed Bandit (MAB) learning-based approach and the traditional exhaustive approach, respectively, and also analyse the training performance of DQN-based beam alignment over different coverage area requirements and channel conditions. Our results show that the proposed DQN-based beam alignment converges faster and generic for different environmental conditions. The framework can also learn optimal beam alignment comparable to the exhaustive approach in an online manner under real-time conditions.
Switch-based Hybrid Beamforming for Wideband Multi-carrier Communications
Authors: Mengyuan Ma, Nhan Thanh Nguyen, Markku Juntti
Date: 2021-10-12 00:00:00

Switch-based hybrid beamforming (SW-HBF) architectures are promising for realizing massive multiple-input multiple-output (MIMO) communications systems because of their low cost and low power consumption. In this paper, we study the performance of SW-HBF in a wideband multi-carrier MIMO communication system considering the beam squint effect. We aim at designing the switch-based combiner that maximizes the system spectral efficiency (SE). However, the design problem is challenging because the analog combing matrix elements are binary variables. To overcome this, we propose tabu search-based (TS) SW-HBF schemes that can attain near-optimal performance with reasonable computational complexity. Furthermore, we compare the total power consumption and energy efficiency (EE) of the SW-HBF architecture to those of the phase-shifter-based hybrid beamforming (PS-HBF) architecture. Numerical simulations show that the proposed algorithms can efficiently find near-optimal solutions. Moreover, the SW-HBF scheme can significantly mitigate the beam squint effect and is less affected by the number of subcarriers than PS-HBF. It also provides improved SE and EE performance compared to PS-HBF schemes.
Analysing the Effect of Test-and-Trace Strategy in an SIR Epidemic Model
Authors: Dongni Zhang, Tom Britton
Date: 2021-10-14 00:00:00

Consider a Markovian SIR epidemic model in a homogeneous community. To this model we add a rate at which individuals are tested, and once an infectious individual tests positive it is isolated and each of their contacts are traced and tested independently with some fixed probability. If such a traced individual tests positive it is isolated, and the contact tracing is iterated. This model is analysed using large population approximations, both for the early stage of the epidemic when the "to-be-traced components" of the epidemic behaves like a branching process, and for the main stage of the epidemic where the process of to-be-traced components converges to a deterministic process defined by a system of differential equations. These approximations are used to quantify the effect of testing and of contact tracing on the effective reproduction numbers (for the components as well as for the individuals), the probability of a major outbreak, and the final fraction getting infected. Using numerical illustrations when rates of infection and natural recovery are fixed, it is shown that Test-and-Trace strategy is effective in reducing the reproduction number. Surprisingly, the reproduction number for the branching process of components is not monotonically decreasing in the tracing probability, but the individual reproduction number is conjectured to be monotonic as expected. Further, in the situation where individuals also self-report for testing, the tracing probability is more influential than the screening rate (measured by the fraction infected being screened).
Eukaryotic gene regulation at equilibrium, or non?
Authors: Benjamin Zoller, Thomas Gregor, Gašper Tkačik
Date: 2021-10-13 00:00:00
Tasks: Navigate

Models of transcriptional regulation that assume equilibrium binding of transcription factors have been very successful at predicting gene expression from sequence in bacteria. However, analogous equilibrium models do not perform as well in eukaryotes, most likely due to the largely out-of-equilibrium nature of eukaryotic regulatory processes. These processes come with unavoidable energy expenditure at the molecular level to support precise, reliable, or fast gene expression responses that could correspond to evolutionarily optimized regulatory strategies. Unfortunately, the space of possible non-equilibrium mechanisms is vast and predominantly uninteresting. The key question is therefore how this space can be navigated efficiently, to focus on mechanisms and models that are biologically relevant. In this review, we advocate for the normative role of theory - theory that prescribes rather than just describes - in providing such a focus. Theory should expand its remit beyond inferring models from data (by fitting), towards identifying non-equilibrium gene regulatory schemes (by optimizing) that may have been evolutionarily selected due to their favorable functional characteristics which outperform regulation at equilibrium. This approach can help us navigate the expanding complexity of regulatory architectures, and refocus the questions about the observed biological mechanisms from how they work towards why they have evolved in the first place. We illustrate our reasoning by toy examples for which we provide simulation code.
Simultaneously exploring multi-scale and asymmetric EEG features for emotion recognition
Authors: Yihan Wu, Min Xia, Li Nie, Yangsong Zhang, Andong Fan
Date: 2021-10-13 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Emotion Recognition

In recent years, emotion recognition based on electroencephalography (EEG) has received growing interests in the brain-computer interaction (BCI) field. The neuroscience researches indicate that the left and right brain hemispheres demonstrate activity differences under different emotional activities, which could be an important principle for designing deep learning (DL) model for emotion recognition. Besides, owing to the nonstationarity of EEG signals, using convolution kernels of a single size may not sufficiently extract the abundant features for EEG classification tasks. Based on these two angles, we proposed a model termed Multi-Scales Bi-hemispheric Asymmetric Model (MSBAM) based on convolutional neural network (CNN) structure. Evaluated on the public DEAP and DREAMER datasets, MSBAM achieved over 99% accuracy for the two-class classification of low-level and high-level states in each of four emotional dimensions, i.e., arousal, valence, dominance and liking, respectively. This study further demonstrated the promising potential to design the DL model from the multi-scale characteristics of the EEG data and the neural mechanisms of the emotion cognition.
Assessing the Impact of (Self)-Quarantine Through a Basic Model of Infectious Disease Dynamics
Authors: Jozsef Z. Farkas, Roxane Chatzopoulos
Date: 2021-10-14 00:00:00

We introduce a system of differential equations to assess the impact of (self-)quarantine of symptomatic infectious individuals on disease dynamics. To this end we depart from using the classic bilinear infection process, but remain still within the framework of the mass-action assumption. From the mathematical point of view our model is interesting due to the lack of continuous differentiability at disease free steady states, which implies also that the basic reproductive number cannot be computed following established approaches for certain parameter values. However, we parametrise our mathematical model using published values from the COVID-19 literature, and analyse the model simulations. We also contrast model simulations against publicly available COVID-19 test data focusing on the first wave of the pandemic during March - July 2020 in the UK. Our simulations indicate that actual peak case numbers might have been as much as 200 times higher than the reported positive test cases during the first wave in the UK. We find that very strong adherence to self-quarantine rules yields (only) a reduction of 22$\%$ of peak numbers and delays the onset of the peak by approximately 30-35 days. However, during the early phase of the outbreak the impact of (self)-quarantine is much more significant. We also take into account the effect of a national lockdown in a simplistic way by reducing the effective susceptible population size. We find that in case of a 90$\%$ reduction of the effective susceptible population size, strong adherence to self-quarantine still only yields a 25$\%$ reduction of peak infectious numbers when compared to low adherence. This is due to the significant number of asymptomatic infectious individuals in the population.
An autonomous compartmental model for accelerating epidemics
Authors: Nazmi Burak Budanur, Björn Hof
Date: 2021-10-12 00:00:00
Tasks: model

In Fall 2020, several European countries reported rapid increases in COVID-19 cases along with growing estimates of the effective reproduction rates. Such an acceleration in epidemic spread is usually attributed to time-dependent effects, e.g. human travel, seasonal behavioral changes, mutations of the pathogen etc. In this case however the acceleration occurred when counter measures such as testing and contact tracing exceeded their capacity limit. Considering Austria as an example, here we show that this dynamics can be captured by a time-independent, i.e. autonomous, compartmental model that incorporates these capacity limits. In this model, the epidemic acceleration coincides with the exhaustion of mitigation efforts, resulting in an increasing fraction of undetected cases that drive the effective reproduction rate progressively higher. We demonstrate that standard models which does not include this effect necessarily result in a systematic underestimation of the effective reproduction rate.