pwc-archive/papers-with-abstracts huggingface dataset:
Enabling Intelligent Vehicular Networks Through Distributed Learning in the Non-Terrestrial Networks 6G Vision
Authors: David Naseh, Swapnil Sadashiv Shinde, Daniele Tarchi
Date: 2023-09-07 00:00:00
Tasks: Edge-computing, Federated Learning, Transfer Learning

The forthcoming 6G-enabled Intelligent Transportation System (ITS) is set to redefine conventional transportation networks with advanced intelligent services and applications. These technologies, including edge computing, Machine Learning (ML), and network softwarization, pose stringent requirements for latency, energy efficiency, and user data security. Distributed Learning (DL), such as Federated Learning (FL), is essential to meet these demands by distributing the learning process at the network edge. However, traditional FL approaches often require substantial resources for satisfactory learning performance. In contrast, Transfer Learning (TL) and Split Learning (SL) have shown effectiveness in enhancing learning efficiency in resource-constrained wireless scenarios like ITS. Non-terrestrial Networks (NTNs) have recently acquired a central place in the 6G vision, especially for boosting the coverage, capacity, and resilience of traditional terrestrial facilities. Air-based NTN layers, such as High Altitude Platforms (HAPs), can have added advantages in terms of reduced transmission distances and flexible deployments and thus can be exploited to enable intelligent solutions for latency-critical vehicular scenarios. With this motivation, in this work, we introduce the concept of Federated Split Transfer Learning (FSTL) in joint air-ground networks for resource-constrained vehicular scenarios. Simulations carried out in vehicular scenarios validate the efficacy of FSTL on HAPs in NTN, demonstrating significant improvements in addressing the demands of ITS applications.
170-260 GHz Sub-THz Optical Heterodyne Analog Radio-over-Fiber Link for 6G Wireless System
Authors: Amol Delmade, Alison Kearney, Simon Nellen, Robert B. Kohlhaas, Colm Browning, Martin Schell, Frank Smyth, Liam Barry
Date: 2023-08-10 00:00:00

The frequency and phase fluctuations of free-running lasers limit the performance of optical heterodyne sub-THz systems - especially for low subcarrier spacing OFDM signals. Digital impairment compensation is implemented here for the successful generation of 170 - 260 GHz sub-THz OFDM signals over 10km analog-RoF link.
Extended Kalman Filter State Estimation for Autonomous Competition Robots
Authors: Ethan Kou, Acshi Haggenmiller
Date: 2023-10-05 00:00:00
Tasks: Navigate, Position, State Estimation

Autonomous mobile robot competitions judge based on a robot's ability to quickly and accurately navigate the game field. This means accurate localization is crucial for creating an autonomous competition robot. Two common localization methods are odometry and computer vision landmark detection. Odometry provides frequent velocity measurements, while landmark detection provides infrequent position measurements. The state can also be predicted with a physics model. These three types of localization can be "fused" to create a more accurate state estimate using an Extended Kalman Filter (EKF). The EKF is a nonlinear full-state estimator that approximates the state estimate with the lowest covariance error when given the sensor measurements, the model prediction, and their variances. In this paper, we demonstrate the effectiveness of the EKF by implementing it on a 4-wheel mecanum-drive robot simulation. The position and velocity accuracy of fusing together various combinations of these three data sources are compared. We also discuss the assumptions and limitations of an EKF.
Modeling of Speech-dependent Own Voice Transfer Characteristics for Hearables with In-ear Microphones
Authors: Mattes Ohlenbusch, Christian Rollwage, Simon Doclo
Date: 2023-10-10 00:00:00
Tasks: Phoneme Recognition

Many hearables contain an in-ear microphone, which may be used to capture the own voice of its user. However, due to the hearable occluding the ear canal, the in-ear microphone mostly records body-conducted speech, typically suffering from band-limitation effects and amplification at low frequencies. Since the occlusion effect is determined by the ratio between the air-conducted and body-conducted components of own voice, the own voice transfer characteristics between the outer face of the hearable and the in-ear microphone depend on the speech content and the individual talker. In this paper, we propose a speech-dependent model of the own voice transfer characteristics based on phoneme recognition, assuming a linear time-invariant relative transfer function for each phoneme. We consider both individual models as well as models averaged over several talkers. Experimental results based on recordings with a prototype hearable show that the proposed speech-dependent model enables to simulate in-ear signals more accurately than a speech-independent model in terms of technical measures, especially under utterance mismatch and talker mismatch. Additionally, simulation results show that talker-averaged models generalize better to different talkers than individual models.
Super Denoise Net: Speech Super Resolution with Noise Cancellation in Low Sampling Rate Noisy Environments
Authors: Junkang Yang, Hongqing Liu, Lu Gan, Yi Zhou
Date: 2023-10-09 00:00:00
Tasks: Denoising, Speech Denoising, Super-Resolution

Speech super-resolution (SSR) aims to predict a high resolution (HR) speech signal from its low resolution (LR) corresponding part. Most neural SSR models focus on producing the final result in a noise-free environment by recovering the spectrogram of high-frequency part of the signal and concatenating it with the original low-frequency part. Although these methods achieve high accuracy, they become less effective when facing the real-world scenario, where unavoidable noise is present. To address this problem, we propose a Super Denoise Net (SDNet), a neural network for a joint task of super-resolution and noise reduction from a low sampling rate signal. To that end, we design gated convolution and lattice convolution blocks to enhance the repair capability and capture information in the time-frequency axis, respectively. The experiments show our method outperforms baseline speech denoising and SSR models on DNS 2020 no-reverb test set with higher objective and subjective scores.
Cross-modal Cognitive Consensus guided Audio-Visual Segmentation
Authors: Zhaofeng Shi, Qingbo Wu, Fanman Meng, Linfeng Xu, Hongliang Li
Date: 2023-10-10 00:00:00
Tasks: Object, Segmentation, Video Editing

Audio-Visual Segmentation (AVS) aims to extract the sounding object from a video frame, which is represented by a pixel-wise segmentation mask for application scenarios such as multi-modal video editing, augmented reality, and intelligent robot systems. The pioneering work conducts this task through dense feature-level audio-visual interaction, which ignores the dimension gap between different modalities. More specifically, the audio clip could only provide a Global semantic label in each sequence, but the video frame covers multiple semantic objects across different Local regions, which leads to mislocalization of the representationally similar but semantically different object. In this paper, we propose a Cross-modal Cognitive Consensus guided Network (C3N) to align the audio-visual semantics from the global dimension and progressively inject them into the local regions via an attention mechanism. Firstly, a Cross-modal Cognitive Consensus Inference Module (C3IM) is developed to extract a unified-modal label by integrating audio/visual classification confidence and similarities of modality-agnostic label embeddings. Then, we feed the unified-modal label back to the visual backbone as the explicit semantic-level guidance via a Cognitive Consensus guided Attention Module (CCAM), which highlights the local features corresponding to the interested object. Extensive experiments on the Single Sound Source Segmentation (S4) setting and Multiple Sound Source Segmentation (MS3) setting of the AVSBench dataset demonstrate the effectiveness of the proposed method, which achieves state-of-the-art performance. Code is available at https://github.com/ZhaofengSHI/AVS-C3N.
DPM-TSE: A Diffusion Probabilistic Model for Target Sound Extraction
Authors: Jiarui Hai, Helin Wang, Dongchao Yang, Karan Thakkar, Najim Dehak, Mounya Elhilali
Date: 2023-10-06 00:00:00
Tasks: Target Sound Extraction

Common target sound extraction (TSE) approaches primarily relied on discriminative approaches in order to separate the target sound while minimizing interference from the unwanted sources, with varying success in separating the target from the background. This study introduces DPM-TSE, a first generative method based on diffusion probabilistic modeling (DPM) for target sound extraction, to achieve both cleaner target renderings as well as improved separability from unwanted sounds. The technique also tackles common background noise issues with DPM by introducing a correction method for noise schedules and sample steps. This approach is evaluated using both objective and subjective quality metrics on the FSD Kaggle 2018 dataset. The results show that DPM-TSE has a significant improvement in perceived quality in terms of target extraction and purity.
Technocratic model of the human auditory system
Authors: M. V. Semotiuk, A. V. Palagin
Date: 2023-10-09 00:00:00
Tasks: model

In this work, we investigate the phenomenon of transverse resonance and transverse standing waves that occur within the cochlea of living organisms. It is demonstrated that the predisposing factor for their occurrence is the cochlear shape, which resembles a conical acoustic tube coiled into a spiral and exhibits non-uniformities on its internal surface. This cochlear structure facilitates the analysis of constituent sound signals akin to a spectrum analyzer, with a corresponding interpretation of the physical processes occurring in the auditory system. Additionally, we conclude that the cochlear duct's scala media, composed of a system of membranes and the organ of Corti, functions primarily as an information collection and amplification system along the cochlear spiral. Collectively, these findings enable the development of a novel, highly realistic wave model of the auditory system in living organisms based on a technocratic approach within the scientific context.
Thech. Report: Genuinization of Speech waveform PMF for speaker detection spoofing and countermeasures
Authors: Itshak Lapidot, Jean-Francois Bonastre
Date: 2023-10-09 00:00:00
Tasks: Speaker Recognition

In the context of spoofing attacks in speaker recognition systems, we observed that the waveform probability mass function (PMF) of genuine speech differs significantly from the PMF of speech resulting from the attacks. This is true for synthesized or converted speech as well as replayed speech. We also noticed that this observation seems to have a significant impact on spoofing detection performance. In this article, we propose an algorithm, denoted genuinization, capable of reducing the waveform distribution gap between authentic speech and spoofing speech. Our genuinization algorithm is evaluated on ASVspoof 2019 challenge datasets, using the baseline system provided by the challenge organization. We first assess the influence of genuinization on spoofing performance. Using genuinization for the spoofing attacks degrades spoofing detection performance by up to a factor of 10. Next, we integrate the genuinization algorithm in the spoofing countermeasures and we observe a huge spoofing detection improvement in different cases. The results of our experiments show clearly that waveform distribution plays an important role and must be taken into account by anti-spoofing systems.
Partial Rank Similarity Minimization Method for Quality MOS Prediction of Unseen Speech Synthesis Systems in Zero-Shot and Semi-supervised setting
Authors: Hemant Yadav, Erica Cooper, Junichi Yamagishi, Sunayana Sitaram, Rajiv Ratn Shah
Date: 2023-10-08 00:00:00
Tasks: Prediction, Speech Synthesis

This paper introduces a novel objective function for quality mean opinion score (MOS) prediction of unseen speech synthesis systems. The proposed function measures the similarity of relative positions of predicted MOS values, in a mini-batch, rather than the actual MOS values. That is the partial rank similarity is measured (PRS) rather than the individual MOS values as with the L1 loss. Our experiments on out-of-domain speech synthesis systems demonstrate that the PRS outperforms L1 loss in zero-shot and semi-supervised settings, exhibiting stronger correlation with ground truth. These findings highlight the importance of considering rank order, as done by PRS, when training MOS prediction models. We also argue that mean squared error and linear correlation coefficient metrics may be unreliable for evaluating MOS prediction models. In conclusion, PRS-trained models provide a robust framework for evaluating speech quality and offer insights for developing high-quality speech synthesis systems. Code and models are available at github.com/nii-yamagishilab/partial_rank_similarity/
Multi-objective Progressive Clustering for Semi-supervised Domain Adaptation in Speaker Verification
Authors: Ze Li, Yuke Lin, Ning Jiang, Xiaoyi Qin, Guoqing Zhao, Haiying Wu, Ming Li
Date: 2023-10-07 00:00:00
Tasks: Clustering, Denoising, Domain Adaptation, Semi-supervised Domain Adaptation, Speaker Verification

Utilizing the pseudo-labeling algorithm with large-scale unlabeled data becomes crucial for semi-supervised domain adaptation in speaker verification tasks. In this paper, we propose a novel pseudo-labeling method named Multi-objective Progressive Clustering (MoPC), specifically designed for semi-supervised domain adaptation. Firstly, we utilize limited labeled data from the target domain to derive domain-specific descriptors based on multiple distinct objectives, namely within-graph denoising, intra-class denoising and inter-class denoising. Then, the Infomap algorithm is adopted for embedding clustering, and the descriptors are leveraged to further refine the target domain's pseudo-labels. Moreover, to further improve the quality of pseudo labels, we introduce the subcenter-purification and progressive-merging strategy for label denoising. Our proposed MoPC method achieves 4.95% EER and ranked the 1$^{st}$ place on the evaluation set of VoxSRC 2023 track 3. We also conduct additional experiments on the FFSVC dataset and yield promising results.
An Exploration of Task-decoupling on Two-stage Neural Post Filter for Real-time Personalized Acoustic Echo Cancellation
Authors: Zihan Zhang, Jiayao Sun, Xianjun Xia, Ziqian Wang, Xiaopeng Yan, Yijian Xiao, Lei Xie
Date: 2023-10-07 00:00:00
Tasks: Acoustic echo cancellation, Speech Enhancement

Deep learning based techniques have been popularly adopted in acoustic echo cancellation (AEC). Utilization of speaker representation has extended the frontier of AEC, thus attracting many researchers' interest in personalized acoustic echo cancellation (PAEC). Meanwhile, task-decoupling strategies are widely adopted in speech enhancement. To further explore the task-decoupling approach, we propose to use a two-stage task-decoupling post-filter (TDPF) in PAEC. Furthermore, a multi-scale local-global speaker representation is applied to improve speaker extraction in PAEC. Experimental results indicate that the task-decoupling model can yield better performance than a single joint network. The optimal approach is to decouple the echo cancellation from noise and interference speech suppression. Based on the task-decoupling sequence, optimal training strategies for the two-stage model are explored afterwards.
Spike-Triggered Contextual Biasing for End-to-End Mandarin Speech Recognition
Authors: Kaixun Huang, Ao Zhang, BinBin Zhang, Tianyi Xu, Xingchen Song, Lei Xie
Date: 2023-10-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

The attention-based deep contextual biasing method has been demonstrated to effectively improve the recognition performance of end-to-end automatic speech recognition (ASR) systems on given contextual phrases. However, unlike shallow fusion methods that directly bias the posterior of the ASR model, deep biasing methods implicitly integrate contextual information, making it challenging to control the degree of bias. In this study, we introduce a spike-triggered deep biasing method that simultaneously supports both explicit and implicit bias. Moreover, both bias approaches exhibit significant improvements and can be cascaded with shallow fusion methods for better results. Furthermore, we propose a context sampling enhancement strategy and improve the contextual phrase filtering algorithm. Experiments on the public WenetSpeech Mandarin biased-word dataset show a 32.0% relative CER reduction compared to the baseline model, with an impressively 68.6% relative CER reduction on contextual phrases.
Analyzing Key Users' behavior trends in Volunteer-Based Networks
Authors: Nofar Piterman, Tamar Makov, Michael Fire
Date: 2023-10-04 00:00:00

Online social networks usage has increased significantly in the last decade and continues to grow in popularity. Multiple social platforms use volunteers as a central component. The behavior of volunteers in volunteer-based networks has been studied extensively in recent years. Here, we explore the development of volunteer-based social networks, primarily focusing on their key users' behaviors and activities. We developed two novel algorithms: the first reveals key user behavior patterns over time; the second utilizes machine learning methods to generate a forecasting model that can predict the future behavior of key users, including whether they will remain active donors or change their behavior to become mainly recipients, and vice-versa. These algorithms allowed us to analyze the factors that significantly influence behavior predictions. To evaluate our algorithms, we utilized data from over 2.4 million users on a peer-to-peer food-sharing online platform. Using our algorithm, we identified four main types of key user behavior patterns that occur over time. Moreover, we succeeded in forecasting future active donor key users and predicting the key users that would change their behavior to donors, with an accuracy of up to 89.6%. These findings provide valuable insights into the behavior of key users in volunteer-based social networks and pave the way for more effective communities-building in the future, while using the potential of machine learning for this goal.
A Parallelized, Adam-Based Solver for Reserve and Security Constrained AC Unit Commitment
Authors: Samuel Chevalier
Date: 2023-10-10 00:00:00

Power system optimization problems which include the nonlinear AC power flow equations require powerful and robust numerical solution algorithms. Within this sub-field of nonlinear optimization, interior point methods have come to dominate the solver landscape. Over the last decade, however, a number of efficient numerical optimizers have emerged from the field of Machine Learning (ML). One algorithm in particular, Adam, has become the optimizer-of-choice for a massive percentage of ML training problems (including, e.g., the training of GPT-3), solving some of the largest unconstrained optimization problems ever conceived of. Inspired by such progress, this paper designs a parallelized Adam-based numerical solver to overcome one of the most challenging power system optimization problems: security and reserve constrained AC Unit Commitment. The resulting solver, termed QuasiGrad, recently competed in the third ARPA-E Grid Optimization (GO3) competition. In the day-ahead market clearing category (with systems ranging from 3 to 23,643 buses over 48 time periods), QuasiGrad's aggregated market surplus scores were within 5% of the winningest market surplus scores. The QuasiGrad solver is now released as an open-source Julia package: QuasiGrad.jl. The internal gradient-based solver (Adam) can easily be substituted for other ML-inspired solvers (e.g., AdaGrad, AdaDelta, RMSProp, etc.). Test results from large experiments are provided.
Safe-by-Construction Autonomous Vehicle Overtaking using Control Barrier Functions and Model Predictive Control
Authors: Dingran Yuan, Xinyi Yu, ShaoYuan Li, Xiang Yin
Date: 2023-10-10 00:00:00
Tasks: Autonomous Driving, Model Predictive Control

Ensuring safety for vehicle overtaking systems is one of the most fundamental and challenging tasks in autonomous driving. This task is particularly intricate when the vehicle must not only overtake its front vehicle safely but also consider the presence of potential opposing vehicles in the opposite lane that it will temporarily occupy. In order to tackle the overtaking task in such challenging scenarios, we introduce a novel integrated framework tailored for vehicle overtaking maneuvers. Our approach integrates the theories of varying-level control barrier functions (CBF) and time-optimal model predictive control (MPC). The main feature of our proposed overtaking strategy is that it is safe-by-construction, which enables rigorous mathematical proof and validation of the safety guarantees. We show that the proposed framework is applicable when the opposing vehicle is either fully autonomous or driven by human drivers. To demonstrate our framework, we perform a set of simulations for overtaking scenarios under different settings. The simulation results show the superiority of our framework in the sense that it ensures collision-free and achieves better safety performance compared with the standard MPC-based approach without safety guarantees.
Photovoltaic grid-forming control strategy investigation using hardware-in-the-loop experiments
Authors: Somesh Bhattacharya, Chrysanthos Charalambous, Anja Banjac, Zoran Miletic, Thomas Strasser, Brian Azzopardi, Christina Papadimitriou, Venizelos Efthymiou, Alexis Polycarpou
Date: 2023-10-10 00:00:00

The frequency stability of a power system is of paramount importance, as a fast frequency swings in the system can lead to oscillatory instability, and thereby blackouts. A grid-connected microgrid, that can operate in the islanded mode can also possess such deteriorating effect due to the higher share of converter-based sources. In this paper, a coordinated frequency control within a distribution network is discussed, with a higher share of Photovoltaics (PV). The main objective of this paper is to test the grid-forming capabilities of PVs, without the requirement of an energy storage in the network. The tests were carried out with the help of the Typhoon Hardware-in-the-loop (HIL) platform using a real Cypriot network feeder. The real-time results confirm the efficacy of the PV as a grid-forming inverter, provided it has sufficient input (irradiance) to provide for the loads within the system of interest. The grid-forming PV also possesses the capability of reconnection with the utility grid through a synchronizer switch that requires minimal communication, makes the overall control independent of any other power source, subject to certain irradiance and loading conditions.
Circulating Current Induced Electromagnetic Torque Generation in Electric Machines with Delta Windings
Authors: Prerit Pramod
Date: 2023-10-10 00:00:00

This paper explains the phenomenon of current circulation and the resulting electromagnetic torque generation in electric machines employing delta windings. The description entails a systematic assessment of the electrical and magnetic behavior of the machine to develop mathematical models, followed by intuitive explanations of the derived analytical forms. The modeling is thoroughly validated through simulation and experimental results on a prototype machine.