pwc-archive/papers-with-abstracts huggingface dataset:
ReachLipBnB: A branch-and-bound method for reachability analysis of neural autonomous systems using Lipschitz bounds
Authors: Taha Entesari, Sina Sharifi, Mahyar Fazlyab
Date: 2022-11-01 00:00:00

We propose a novel Branch-and-Bound method for reachability analysis of neural networks in both open-loop and closed-loop settings. Our idea is to first compute accurate bounds on the Lipschitz constant of the neural network in certain directions of interest offline using a convex program. We then use these bounds to obtain an instantaneous but conservative polyhedral approximation of the reachable set using Lipschitz continuity arguments. To reduce conservatism, we incorporate our bounding algorithm within a branching strategy to decrease the over-approximation error within an arbitrary accuracy. We then extend our method to reachability analysis of control systems with neural network controllers. Finally, to capture the shape of the reachable sets as accurately as possible, we use sample trajectories to inform the directions of the reachable set over-approximations using Principal Component Analysis (PCA). We evaluate the performance of the proposed method in several open-loop and closed-loop settings.
Reducing Two-Way Ranging Variance by Signal-Timing Optimization
Authors: Mohammed Ayman Shalaby, Charles Champagne Cossette, James Richard Forbes, Jerome Le Ny
Date: 2022-11-01 00:00:00
Tasks: Vocal Bursts Valence Prediction

Time-of-flight-based ranging among transceivers with different clocks requires protocols that accommodate varying rates of the clocks. Double-sided two-way ranging (DS-TWR) is widely adopted as a standard protocol due to its accuracy; however, the precision of DS-TWR has not been clearly addressed. In this paper, an analytical model of the variance of DS-TWR is derived as a function of the user-programmed response delays, which is then compared to the Cramer-Rao Lower Bound (CRLB). This is then used to formulate an optimization problem over the response delays in order to maximize the information gained from range measurements. The derived analytical variance model and optimized protocol are validated experimentally with 2 ranging UWB transceivers, where 29 million range measurements are collected.
SCA: Streaming Cross-attention Alignment for Echo Cancellation
Authors: Yang Liu, Yangyang Shi, Yun Li, Kaustubh Kalgaonkar, Sriram Srinivasan, Xin Lei
Date: 2022-11-01 00:00:00
Tasks: Speech Enhancement, Speech Separation

End-to-End deep learning has shown promising results for speech enhancement tasks, such as noise suppression, dereverberation, and speech separation. However, most state-of-the-art methods for echo cancellation are either classical DSP-based or hybrid DSP-ML algorithms. Components such as the delay estimator and adaptive linear filter are based on traditional signal processing concepts, and deep learning algorithms typically only serve to replace the non-linear residual echo suppressor. This paper introduces an end-to-end echo cancellation network with a streaming cross-attention alignment (SCA). Our proposed method can handle unaligned inputs without requiring external alignment and generate high-quality speech without echoes. At the same time, the end-to-end algorithm simplifies the current echo cancellation pipeline for time-variant echo path cases. We test our proposed method on the ICASSP2022 and Interspeech2021 Microsoft deep echo cancellation challenge evaluation dataset, where our method outperforms some of the other hybrid and end-to-end methods.
A Comparative Study on Multichannel Speaker-Attributed Automatic Speech Recognition in Multi-party Meetings
Authors: Mohan Shi, Jie Zhang, Zhihao Du, Fan Yu, Qian Chen, Shiliang Zhang, Li-Rong Dai
Date: 2022-11-01 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speaker-diarization, Speaker Diarization, Speaker Separation, speech-recognition, Speech Recognition

Speaker-attributed automatic speech recognition (SA-ASR) in multi-party meeting scenarios is one of the most valuable and challenging ASR task. It was shown that single-channel frame-level diarization with serialized output training (SC-FD-SOT), single-channel word-level diarization with SOT (SC-WD-SOT) and joint training of single-channel target-speaker separation and ASR (SC-TS-ASR) can be exploited to partially solve this problem. In this paper, we propose three corresponding multichannel (MC) SA-ASR approaches, namely MC-FD-SOT, MC-WD-SOT and MC-TS-ASR. For different tasks/models, different multichannel data fusion strategies are considered, including channel-level cross-channel attention for MC-FD-SOT, frame-level cross-channel attention for MC-WD-SOT and neural beamforming for MC-TS-ASR. Results on the AliMeeting corpus reveal that our proposed models can consistently outperform the corresponding single-channel counterparts in terms of the speaker-dependent character error rate.
Adapting self-supervised models to multi-talker speech recognition using speaker embeddings
Authors: Zili Huang, Desh Raj, Paola García, Sanjeev Khudanpur
Date: 2022-11-01 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Self-Supervised Learning, speech-recognition, Speech Recognition, Target Speaker Extraction

Self-supervised learning (SSL) methods which learn representations of data without explicit supervision have gained popularity in speech-processing tasks, particularly for single-talker applications. However, these models often have degraded performance for multi-talker scenarios -- possibly due to the domain mismatch -- which severely limits their use for such applications. In this paper, we investigate the adaptation of upstream SSL models to the multi-talker automatic speech recognition (ASR) task under two conditions. First, when segmented utterances are given, we show that adding a target speaker extraction (TSE) module based on enrollment embeddings is complementary to mixture-aware pre-training. Second, for unsegmented mixtures, we propose a novel joint speaker modeling (JSM) approach, which aggregates information from all speakers in the mixture through their embeddings. With controlled experiments on Libri2Mix, we show that using speaker embeddings provides relative WER improvements of 9.1% and 42.1% over strong baselines for the segmented and unsegmented cases, respectively. We also demonstrate the effectiveness of our models for real conversational mixtures through experiments on the AMI dataset.
Metric Learning for User-defined Keyword Spotting
Authors: Jaemin Jung, Youkyum Kim, Jihwan Park, Youshin Lim, Byeong-Yeol Kim, Youngjoon Jang, Joon Son Chung
Date: 2022-11-01 00:00:00
Tasks: Keyword Spotting, Metric Learning

The goal of this work is to detect new spoken terms defined by users. While most previous works address Keyword Spotting (KWS) as a closed-set classification problem, this limits their transferability to unseen terms. The ability to define custom keywords has advantages in terms of user experience. In this paper, we propose a metric learning-based training strategy for user-defined keyword spotting. In particular, we make the following contributions: (1) we construct a large-scale keyword dataset with an existing speech corpus and propose a filtering method to remove data that degrade model training; (2) we propose a metric learning-based two-stage training strategy, and demonstrate that the proposed method improves the performance on the user-defined keyword spotting task by enriching their representations; (3) to facilitate the fair comparison in the user-defined KWS field, we propose unified evaluation protocol and metrics. Our proposed system does not require an incremental training on the user-defined keywords, and outperforms previous works by a significant margin on the Google Speech Commands dataset using the proposed as well as the existing metrics.
Disentangled representation learning for multilingual speaker recognition
Authors: Kihyun Nam, Youkyum Kim, Jaesung Huh, Hee Soo Heo, Jee-weon Jung, Joon Son Chung
Date: 2022-11-01 00:00:00
Tasks: Disentanglement, Metric Learning, Representation Learning, Speaker Recognition

The goal of this paper is to learn robust speaker representation for bilingual speaking scenario. The majority of the world's population speak at least two languages; however, most speaker recognition systems fail to recognise the same speaker when speaking in different languages. Popular speaker recognition evaluation sets do not consider the bilingual scenario, making it difficult to analyse the effect of bilingual speakers on speaker recognition performance. In this paper, we publish a large-scale evaluation set named VoxCeleb1-B derived from VoxCeleb that considers bilingual scenarios. We introduce an effective disentanglement learning strategy that combines adversarial and metric learning-based methods. This approach addresses the bilingual situation by disentangling language-related information from speaker representation while ensuring stable speaker representation learning. Our language-disentangled learning method only uses language pseudo-labels without manual information.
Waveform Boundary Detection for Partially Spoofed Audio
Authors: Zexin Cai, Weiqing Wang, Ming Li
Date: 2022-11-01 00:00:00
Tasks: Boundary Detection

The present paper proposes a waveform boundary detection system for audio spoofing attacks containing partially manipulated segments. Partially spoofed/fake audio, where part of the utterance is replaced, either with synthetic or natural audio clips, has recently been reported as one scenario of audio deepfakes. As deepfakes can be a threat to social security, the detection of such spoofing audio is essential. Accordingly, we propose to address the problem with a deep learning-based frame-level detection system that can detect partially spoofed audio and locate the manipulated pieces. Our proposed method is trained and evaluated on data provided by the ADD2022 Challenge. We evaluate our detection model concerning various acoustic features and network configurations. As a result, our detection system achieves an equal error rate (EER) of 6.58% on the ADD2022 challenge test set, which is the best performance in partially spoofed audio detection systems that can locate manipulated clips.
ImagineNET: Target Speaker Extraction with Intermittent Visual Cue through Embedding Inpainting
Authors: Zexu Pan, Wupeng Wang, Marvin Borsdorf, Haizhou Li
Date: 2022-10-31 00:00:00
Tasks: Target Speaker Extraction

The speaker extraction technique seeks to single out the voice of a target speaker from the interfering voices in a speech mixture. Typically an auxiliary reference of the target speaker is used to form voluntary attention. Either a pre-recorded utterance or a synchronized lip movement in a video clip can serve as the auxiliary reference. The use of visual cue is not only feasible, but also effective due to its noise robustness, and becoming popular. However, it is difficult to guarantee that such parallel visual cue is always available in real-world applications where visual occlusion or intermittent communication can occur. In this paper, we study the audio-visual speaker extraction algorithms with intermittent visual cue. We propose a joint speaker extraction and visual embedding inpainting framework to explore the mutual benefits. To encourage the interaction between the two tasks, they are performed alternately with an interlacing structure and optimized jointly. We also propose two types of visual inpainting losses and study our proposed method with two types of popularly used visual embeddings. The experimental results show that we outperform the baseline in terms of signal quality, perceptual quality, and intelligibility.
Optimal Covariance Steering for Discrete-Time Linear Stochastic Systems
Authors: Fengjiao Liu, George Rapakoulias, Panagiotis Tsiotras
Date: 2022-11-01 00:00:00

In this paper, we study the optimal control problem for steering the state covariance of a discrete-time linear stochastic system over a finite time horizon. First, we establish the existence and uniqueness of the optimal control law for a quadratic cost function. Then, we show the separation of the optimal mean and the covariance steering problems. We also develop efficient computational methods to solve for the optimal control law, which is identified as the solution to a semi-definite program. The effectiveness of the proposed approach is demonstrated through numerical examples. In the process, we also obtain some novel theoretical results for a matrix Riccati difference equation, which may be of independent interest.
Pricing for Reconfigurable Intelligent Surface Aided Wireless Networks: Models and Principles
Authors: Yulan Gao, Yue Xiao, Xianfu Lei, Qiaonan Zhu, Dusit Niyato, Kai-Kit Wong, Pingzhi Fan, Rose Qingyang Hu
Date: 2022-11-01 00:00:00

Owing to the recent advancements of meta-materials and meta-surfaces, the concept of reconfigurable intelligent surface (RIS) has been embraced to meet the spectral- and energy-efficient, and yet cost-effective solutions for the sixth-generation (6G) wireless networks. From an operational standpoint, RISs can be easily deployed on the facades of buildings and indoor walls. Albeit promising, in the actual network operation, the deployment of RISs may face challenges because of the willingness and benefits of RIS holders from the aspect of installing RISs on their properties. Accordingly, RIS-aided wireless networks are faced with a formidable mission: how to balance the wireless service providers (WSPs) and RIS holders in terms of their respective interests. To alleviate this deadlock, we focus on the application of pricing models in RIS-aided wireless networks in pursuit of a win-win solution for both sides. Specifically, we commence with a comprehensive introduction of RIS pricing with its potential applications in RIS networks, meanwhile the fundamentals of pricing models are summarized in order to benefit both RIS holders and WSPs. In addition, a Stackelberg game-based model is exemplified to illustrate the operation of utility-maximization pricing. Finally, we highlight open issues and future research directions of applying pricing models to the RIS-aided wireless networks.
Comprehensive Study on Railway Communications Systems to Support Hyperloop
Authors: Hamid Amiriara
Date: 2022-11-01 00:00:00
Tasks: Friction

Hyperloop is a sonic-speed train transporting passengers and freights in a vacuum tube without friction or air resistance. Two essential communications in such vehicles are central control connection and real-time dispatching, also an optional data connection for passengers is welcome. The high mobility of Hyperloop imposes a severe impact on the performance of wireless communication links. Therefore, designing a new wireless communication system is necessary to cope with the challenges. Motivated by the importance of doppler spreading, this paper focuses on the characterizations of the wireless channel in Hyperloop and then analyzes the performance degradation of the radio link. Afterward, a comprehensive overview of the present railway communication technologies as potential solutions for the Hyperloop project and a detailed discussion of their cons and pros are provided. It is shown that current communication technologies are not satisfactory for this scenario. Finally, this paper concludes the key points that need to be considered in future railway communications systems in order to overcome the Hyperloop communication challenges. So, we open up a new issue that Hyperloop communications require designing a novel method or improving the existing technologies or a combination of different techniques (some of these techniques are mentioned).
Comparative analysis of PV configurations for agrivoltaic systems in Europe
Authors: Kamran Ali Khan Niazi, Marta Victoria
Date: 2022-11-01 00:00:00

Agrivoltaics (APV) is the dual use of land by combining agricultural crop production and photovoltaic (PV) systems. In this work, we have analyzed three different APV configurations: static with optimal tilt, vertically-mounted bifacial, and single-axis horizontal tracking. A model is developed to calculate the shadowing losses on the PV panels along with the reduced solar irradiation reaching the area under them for different PV capacity densities. First, we investigate the trade-offs using a location in Denmark as a case study and second, we extrapolate the analysis to the rest of Europe. We find that the vertical and single-axis tracking produce more uniform irradiance on the ground, and a capacity density of around 30 W/m2 is suitable for APV systems. Based on our model and a 100 m-resolution land cover database, we calculate the potential for APV in every NUTS-2 region within the European Union (EU). The potential for APV is enormous as the electricity generated by APV systems could produce 28 times the current electricity demand in Europe. Overall, the potential capacity for APV in Europe is 51 TW, which would result in an electricity yield of 71500 TWh/year.
Feature-Informed Data Assimilation -- Definitions and Illustrative Examples
Authors: Wei Kang, Daniel M. Tartakovsky, Apoorv Srivastava
Date: 2022-11-01 00:00:00

We introduce a mathematical formulation of feature-informed data assimilation (FIDA). In FIDA, the information about feature events, such as shock waves, level curves, wavefronts and peak value, in dynamical systems are used for the estimation of state variables and unknown parameters. The observation operator in FIDA is a set-valued functional, which is fundamentally different from the observation operators in conventional data assimilation. Demonstrated in three example, FIDA problems introduced in this note exist in a wide spectrum of applications in science and engineering.
Rarest-First with Probabilistic-Mode-Suppression
Authors: Nouman Khan, Mehrdad Moharrami, Vijay Subramanian
Date: 2022-11-01 00:00:00

Recent studies suggested that the BitTorrent's rarest-first protocol, owing to its work-conserving nature, can become unstable in the presence of non-persistent users. Consequently, for any provably stable protocol, many peers, at some point, would have to be endogenously forced to hold off their file-download activity. In this work, we propose a tunable piece-selection policy that minimizes this (undesirable) requisite by combining the (work-conserving but not stabilizing) rarest-first protocol with only an appropriate share of the (non-work conserving and stabilizing) mode-suppression protocol. We refer to this policy as ``Rarest-First with Probabilistic Mode-Suppression'' or simply RFwPMS. We study RFwPMS using a stochastic abstraction of the BitTorrent network that is general enough to capture a multiple swarm setting of non-persistent users -- each swarm having its own altruistic preferences that may or may not overlap with those of other swarms. Using Lyapunov drift analysis, we show that for all kinds of inter-swarm behaviors and all arrival-rate configurations, RFwPMS is stable. Then, using the Kingman's moment bound technique, we further show that the expected steady-state sojourn time of RFwPMS is independent of the arrival-rate in the single-swarm case (under a mild additional assumption). Finally, our simulation-based performance evaluation confirms our theoretical findings and shows that the steady-state expected sojourn time is linear in the file-size (compared to our loose estimate of a polynomial with degree 6). Overall, an improved performance is observed in comparison to previously proposed stabilizing schemes like mode-suppression (MS).
A Primary Frequency Control Strategy for Variable-Speed Pumped-Storage Plant in Power Generation Based on Adaptive Model Predictive Control
Authors: Zhenghua Xu, Changhong Deng, Qiuling Yang
Date: 2022-11-01 00:00:00
Tasks: Model Predictive Control

Variable-speed pumped-storage (VSPS) has great potential in helping solve the frequency control problem caused by low inertia, owing to its remarkable flexibility beyond conventional fixed-speed one, to make better use of which, a primary frequency control strategy based on adaptive model predictive control (AMPC) is proposed in this paper for VSPS plant in power generation.
A 5G Enabled Adaptive Computing Workflow for Greener Power Grid
Authors: Yousu Chen, LiWei Wang, Xiaoyuan Fan, Dexin Wang, James Ogle
Date: 2022-10-31 00:00:00
Tasks: Cloud Computing, Edge-computing, GPU

5G wireless technology can deliver higher data speeds, ultra low latency, more reliability, massive network capacity, increased availability, and a more uniform user experience to users. It brings additional power to help address the challenges brought by renewable integration and decarbonization. In this paper, a 5G enabled adaptive computing workflow has been presented that consists of various computing resources, such as 5G equipment, edge computing, cluster, Graphics processing unit (GPU) and cloud computing, with two examples showing technical feasibility for edge-grid-cloud interaction for power system real-time monitoring, security assessment, and forecasting. Benefiting from the high speed data transport and massive connection capability of 5G, the workflow shows its potential to seamlessly integrate various applications at distributed and/or centralized locations to build more complex and powerful functions, with better flexibility.
HAVOK Model Predictive Control for Time-Delay Systems with Applications to District Heating
Authors: Christian M. Jensen, Mathias C. Frederiksen, Carsten S. Kallesøe, Jeppe N. Jensen, Laurits H. Andersen, Roozbeh Izadi-Zamanabadi
Date: 2022-10-31 00:00:00
Tasks: Model Predictive Control

A computationally efficient Model-Predictive Control (MPC) approach is proposed for systems with unknown delay using only input/output data. We use the Koopman operator framework and the related Hankel Alternative View of Koopman (HAVOK) algorithm to identify a model in a basis of projected time-delay coordinates and demonstrate a novel MPC structure that reduces and bounds the computational complexity. The proposed HAVOK-MPC approach is validated experimentally on a laboratory-scale District Heating System (DHS), demonstrating excellent prediction and tracking performance while only requiring knowledge of a conservative upper bound on the system delay.
RIS-enhanced Resilience in Cell-Free MIMO
Authors: Kevin Weinberger, Robert-Jeron Reifert, Aydin Sezgin, Ertugrul Basar
Date: 2022-10-31 00:00:00

More and more applications that require high reliability and fault tolerance are realized with wireless network architectures and thus ultimately rely on the wireless channels, which can be subject to impairments and blockages. Hence, these architectures require a backup plan in the physical layer in order to guarantee functionality, especially when safety-relevant aspects are involved. To this end, this work proposes to utilize the reconfigurable intelligent surface (RIS) as a resilience mechanism to counteract outages. The advantages of RISs for such a purpose derive from their inherent addition of alternative channel links in combination with their reconfigurability. The major benefits are investigated in a cell-free multiple-input and multiple-output (MIMO) setting, in which the direct channel paths are subject to blockages. An optimization problem is formulated that includes rate allocation with beamforming and phase shift configuration and is solved with a resilience-aware alternating optimization approach. Numerical results show that deploying even a randomly-configured RIS to a network reduces the performance degradation caused by blockages. This becomes even more pronounced in the optimized case, in which the RIS is able to potentially counteract the performance degradation entirely. Interestingly, adding more reflecting elements to the system brings an overall benefit for the resilience, even for time-sensitive systems, due to the contribution of the RIS reflections, even when unoptimized.