pwc-archive/papers-with-abstracts huggingface dataset:
End-to-End Lyrics Recognition with Self-supervised Learning
Authors: Xiangyu Zhang, Shuyue Stella Li, Zhanhong He, Roberto Togneri, Leibny Paola Garcia
Date: 2022-09-26 00:00:00
Tasks: Contrastive Learning, Domain Generalization, Language Modeling, Language Modelling, Self-Supervised Learning

Lyrics recognition is an important task in music processing. Despite traditional algorithms such as the hybrid HMM- TDNN model achieving good performance, studies on applying end-to-end models and self-supervised learning (SSL) are limited. In this paper, we first establish an end-to-end baseline for lyrics recognition and then explore the performance of SSL models on lyrics recognition task. We evaluate a variety of upstream SSL models with different training methods (masked reconstruction, masked prediction, autoregressive reconstruction, and contrastive learning). Our end-to-end self-supervised models, evaluated on the DAMP music dataset, outperform the previous state-of-the-art (SOTA) system by 5.23% for the dev set and 2.4% for the test set even without a language model trained by a large corpus. Moreover, we investigate the effect of background music on the performance of self-supervised learning models and conclude that the SSL models cannot extract features efficiently in the presence of background music. Finally, we study the out-of-domain generalization ability of the SSL features considering that those models were not trained on music datasets.
Spatial-aware Speaker Diarization for Multi-channel Multi-party Meeting
Authors: Jie Wang, Yuji Liu, Binling Wang, Yiming Zhi, Song Li, Shipeng Xia, Jiayang Zhang, Feng Tong, Lin Li, Qingyang Hong
Date: 2022-09-24 00:00:00
Tasks: speaker-diarization, Speaker Diarization

This paper describes a spatial-aware speaker diarization system for the multi-channel multi-party meeting. The diarization system obtains direction information of speaker by microphone array. Speaker spatial embedding is generated by xvector and s-vector derived from superdirective beamforming (SDB) which makes the embedding more robust. Specifically, we propose a novel multi-channel sequence-to-sequence neural network architecture named discriminative multi-stream neural network (DMSNet) which consists of attention superdirective beamforming (ASDB) block and Conformer encoder. The proposed ASDB is a self-adapted channel-wise block that extracts the latent spatial features of array audios by modeling interdependencies between channels. We explore DMSNet to address overlapped speech problem on multi-channel audio and achieve 93.53% accuracy on evaluation set. By performing DMSNet based overlapped speech detection (OSD) module, the diarization error rate (DER) of cluster-based diarization system decrease significantly from 13.45% to 7.64%.
NWPU-ASLP System for the VoicePrivacy 2022 Challenge
Authors: Jixun Yao, Qing Wang, Li Zhang, Pengcheng Guo, Yuhao Liang, Lei Xie
Date: 2022-09-24 00:00:00
Tasks: Speaker anonymization, Speaker Verification

This paper presents the NWPU-ASLP speaker anonymization system for VoicePrivacy 2022 Challenge. Our submission does not involve additional Automatic Speaker Verification (ASV) model or x-vector pool. Our system consists of four modules, including feature extractor, acoustic model, anonymization module, and neural vocoder. First, the feature extractor extracts the Phonetic Posteriorgram (PPG) and pitch from the input speech signal. Then, we reserve a pseudo speaker ID from a speaker look-up table (LUT), which is subsequently fed into a speaker encoder to generate the pseudo speaker embedding that is not corresponding to any real speaker. To ensure the pseudo speaker is distinguishable, we further average the randomly selected speaker embedding and weighted concatenate it with the pseudo speaker embedding to generate the anonymized speaker embedding. Finally, the acoustic model outputs the anonymized mel-spectrogram from the anonymized speaker embedding and a modified version of HifiGAN transforms the mel-spectrogram into the anonymized speech waveform. Experimental results demonstrate the effectiveness of our proposed anonymization system.
ControlVC: Zero-Shot Voice Conversion with Time-Varying Controls on Pitch and Speed
Authors: Meiying Chen, Zhiyao Duan
Date: 2022-09-23 00:00:00
Tasks: Pitch control, Speech Synthesis, Voice Conversion

Recent developments in neural speech synthesis and vocoding have sparked a renewed interest in voice conversion (VC). Beyond timbre transfer, achieving controllability on para-linguistic parameters such as pitch and Speed is critical in deploying VC systems in many application scenarios. Existing studies, however, either only provide utterance-level global control or lack interpretability on the controls. In this paper, we propose ControlVC, the first neural voice conversion system that achieves time-varying controls on pitch and speed. ControlVC uses pre-trained encoders to compute pitch and linguistic embeddings from the source utterance and speaker embeddings from the target utterance. These embeddings are then concatenated and converted to speech using a vocoder. It achieves speed control through TD-PSOLA pre-processing on the source utterance, and achieves pitch control by manipulating the pitch contour before feeding it to the pitch encoder. Systematic subjective and objective evaluations are conducted to assess the speech quality and controllability. Results show that, on non-parallel and zero-shot conversion tasks, ControlVC significantly outperforms two other self-constructed baselines on speech quality, and it can successfully achieve time-varying pitch and speed control.
Data-Enabled Predictive Control for Fast Charging of Lithium-Ion Batteries with Constraint Handling
Authors: Kaixiang Zhang, Kaian Chen, Xinfan Lin, Yusheng Zheng, Xunyun Yin, Xiaosong Hu, Ziyou Song, Zhaojian Li
Date: 2022-09-26 00:00:00

Fast charging of lithium-ion batteries has gained extensive research interests, but most of existing methods are either based on simple rule-based charging profiles or require explicit battery models that are non-trivial to identify accurately. In this paper, instead of relying on parametric battery models that are costly to derive and calibrate, we employ a novel data-enabled predictive control (DeePC) paradigm to perform safe and optimal fast charging for lithium-ion batteries. The developed DeePC methodology is based on behavioral system theory and directly utilizes the input-output measurements from the battery system to predict the future trajectory and compute the optimal control policy. Constraints on input current and battery states are incorporated in the DeePC formulation to ensure battery fast charging with safe operations. Furthermore, we propose a principal component analysis based scheme to reduce the dimension of the optimization variables in the DeePC algorithm, which significantly enhances the computation efficiency without compromising the charging performance. Numerical simulations are performed on a high-fidelity battery simulator to validate the efficacy of the proposed fast charging strategy.
Ultrawideband USRP-Based Channel Sounding Utilizing the RFNoC Framework
Authors: Michiel Sandra, Christian Nelson, Anders J Johansson
Date: 2022-09-26 00:00:00

This paper shows how an ultrawideband channel sounder can be built with the latest National Instruments (NI) Universal Software Radio Peripheral (USRP) equipment, featuring onboard FPGA processing and utilizing open-source tools. Compared to other USRP-based channel sounders, our design is versatile, eases the requirements on the data link between the USRPs and the host, limits the measurement data size, and increases the signal-to-interference-plus-noise ratio due to averaging. Furthermore, this paper serves as a proof-of-concept for other researchers seeking to build a channel sounder based on the USRP equipment.
Design of Automatic Driving Safety Level and Positioning Accuracy
Authors: Tiantian Tang, Hao Xu, Chengcheng Wu, Sijie Lye, Yan Xiang
Date: 2022-09-26 00:00:00
Tasks: Autonomous Driving

Autonomous driving is a hot research topic in the frontier of science and technology. Technology companies and traditional car companies are developing and designing autonomous driving technology from two different directions. Based on the automatic driving classification standard and ISO safety level, combined with the number of traffic accidents and death data in China, and referring to the risk allocation method of the automated driving virtual drive system in the United States, the risk allocation of China's virtual drive system will be carried out. In addition, combined with the vehicle "positioning box" model, the theoretical calculation of the alarm limit of positioning accuracy in China will be carried out and the positioning accuracy requirements of related vehicles will be designed.
Learning Critical Scenarios in Feedback Control Systems for Automated Driving
Authors: Mengjia Zhu, Alberto Bemporad, Maximilian Kneissl, Hasan Esen
Date: 2022-09-26 00:00:00

Testing is essential for verifying and validating control designs, especially in safety-critical applications. In particular, the control system governing an automated driving vehicle must be proven reliable enough for its acceptance on the market. Recently, much research has focused on scenario-based methods. However, the number of possible driving scenarios to test is in principle infinite. In this paper, we formalize a learning-based optimization framework to generate corner test-cases, where we take into account the operational design domain. We examine the approach on the case of a feedback control system for automated driving, for which we suggest the design of the objective function expressing the criticality of scenarios. Numerical tests on two logical scenarios of the case study demonstrate that the approach can identify critical scenarios within a limited number of closed-loop experiments.
An Efficient Implementation for Spatial-Temporal Gaussian Process Regression and Its Applications
Authors: Junpeng Zhang, Yue Ju, Biqiang Mu, Renxin Zhong, Tianshi Chen
Date: 2022-09-26 00:00:00
Tasks: regression

Spatial-temporal Gaussian process regression is a popular method for spatial-temporal data modeling. Its state-of-art implementation is based on the state-space model realization of the spatial-temporal Gaussian process and its corresponding Kalman filter and smoother, and has computational complexity $\mathcal{O}(NM^3)$, where $N$ and $M$ are the number of time instants and spatial input locations, respectively, and thus can only be applied to data with large $N$ but relatively small $M$. In this paper, our primary goal is to show that by exploring the Kronecker structure of the state-space model realization of the spatial-temporal Gaussian process, it is possible to further reduce the computational complexity to $\mathcal{O}(M^3+NM^2)$ and thus the proposed implementation can be applied to data with large $N$ and moderately large $M$. The proposed implementation is illustrated over applications in weather data prediction and spatially-distributed system identification. Our secondary goal is to design a kernel for both the Colorado precipitation data and the GHCN temperature data, such that while having more efficient implementation, better prediction performance can also be achieved than the state-of-art result.
A Novel Observer-Centric Approach for Detecting Faults in Islanded AC Microgrids with Uncertainties
Authors: Gabriel Intriago, Andres Intriago, Charalambos Konstantinou, Yu Zhang
Date: 2022-09-26 00:00:00
Tasks: Fault Detection

Fault detection is vital in ensuring AC microgrids' reliable and resilient operation. Its importance lies in swiftly identifying and isolating faults, preventing cascading failures, and enabling rapid power restoration. This paper proposes a strategy based on observers and residuals for detecting internal faults in grid-forming inverters with power-sharing coordination. The dynamics of the inverters are captured through a nonlinear state space model. The design of our observers and residuals considers $H_{-}/H_{\infty}$ conditions to ensure robustness against disturbances and responsiveness to faults. The proposed design is less restrictive than existing observer-based fault detection schemes by leveraging the properties of quadratic inner-boundedness and one-sided Lipschitz conditions. The internal faults considered in this paper include actuator faults, busbar faults, and inverter bridge faults, which are modeled using vector-matrix representations that modify the state space model of the inverters. One significant advantage of the proposed approach is its cost-effectiveness, as it does not require additional sensors. Experiments are conducted on an islanded AC microgrid with three inductive lines, four inductive loads, and four grid-forming inverters to validate the merits of the proposed fault detection strategy. The results demonstrate that our design outperforms existing methods in the field.
Carbon-Aware EV Charging
Authors: Kai-Wen Cheng, Yuexin Bian, Yuanyuan Shi, Yize Chen
Date: 2022-09-26 00:00:00

This paper examines the problem of optimizing the charging pattern of electric vehicles (EV) by taking real-time electricity grid carbon intensity into consideration. The objective of the proposed charging scheme is to minimize the carbon emissions contributed by EV charging events, while simultaneously satisfying constraints posed by EV user's charging schedules, charging station transformer limits, and battery physical constraints. Using real-world EV charging data and California electricity generation records, this paper shows that our carbon-aware real-time charging scheme saves an average of 3.81% of carbon emission while delivering satisfactory amount of energy. Furthermore, by using an adaptive balanced factor, we can reduce 26.00% of carbon emission on average while compromising 12.61% of total energy delivered.
Terra: Blockage Resilience in Outdoor mmWave Networks
Authors: Santosh Ganji, Jaewon Kim, P. R. Kumar
Date: 2022-09-25 00:00:00
Tasks: Detect Ground Reflections

We address the problem of pedestrian blockage in outdoor mm-Wave networks, which can disrupt Line of Sight (LoS) communication and result in an outage. This necessitates either reacquisition as a new user that can take up to 1.28 sec in 5G New Radio, disrupting high-performance applications, or handover to a different base station (BS), which however requires very costly dense deployments to ensure multiple base stations are always visible to a mobile outdoors. We have found that there typically exists a strong ground reflection from concrete and gravel surfaces, within 4-6 dB of received signal strength (RSS) of LoS paths. The mobile can switch to such a Non-Line Sight (NLoS) beam to sustain the link for use as a control channel during a blockage event. This allows the mobile to maintain time-synchronization with the base station, allowing it to revert to the LoS path when the temporary blockage disappears. We present a protocol, Terra, to quickly discover, cache, and employ ground reflections. It can be used in most outdoor built environments since pedestrian blockages typically last only a few hundred milliseconds.
Asymptotic Theory for Regularized System Identification Part I: Empirical Bayes Hyper-parameter Estimator
Authors: Yue Ju, Biqiang Mu, Lennart Ljung, Tianshi Chen
Date: 2022-09-25 00:00:00

Regularized system identification is the major advance in system identification in the last decade. Although many promising results have been achieved, it is far from complete and there are still many key problems to be solved. One of them is the asymptotic theory, which is about convergence properties of the model estimators as the sample size goes to infinity. The existing related results for regularized system identification are about the almost sure convergence of various hyper-parameter estimators. A common problem of those results is that they do not contain information on the factors that affect the convergence properties of those hyper-parameter estimators, e.g., the regression matrix. In this paper, we tackle problems of this kind for the regularized finite impulse response model estimation with the empirical Bayes (EB) hyper-parameter estimator and filtered white noise input. In order to expose and find those factors, we study the convergence in distribution of the EB hyper-parameter estimator, and the asymptotic distribution of its corresponding model estimator. For illustration, we run Monte Carlo simulations to show the efficacy of our obtained theoretical results.
Adaptive Dynamic Programming and Data-Driven Cooperative Optimal Output Regulation with Adaptive Observers
Authors: Omar Qasem, Khalid Jebari, Weinan Gao
Date: 2022-09-25 00:00:00

In this paper, a novel adaptive optimal control strategy is proposed to achieve the cooperative optimal output regulation of continuous-time linear multi-agent systems based on adaptive dynamic programming (ADP). The proposed method is different from those in the existing literature of ADP and cooperative output regulation in the sense that the knowledge of the exosystem dynamics is not required in the design of the exostate observers for those agents with no direct access to the exosystem. Moreover, an optimal control policy is obtained without the prior knowledge of the modeling information of any agent while achieving the cooperative output regulation. Instead, we use the state/input information along the trajectories of the underlying dynamical systems and the estimated exostates to learn the optimal control policy. Simulation results show the efficacy of the proposed algorithm, where both estimation errors of exosystem matrix and exostates, and the tracking errors converge to zero in an optimal sense, which solves the cooperative optimal output regulation problem.
Optimal dispatch of low-carbon integrated energy system considering nuclear heating and carbon trading
Authors: Yang Li, Fanjin Bu, Jiankai Gao, Guoqing Lia
Date: 2022-09-24 00:00:00
Tasks: Scheduling

The development of miniaturized nuclear power (NP) units and the improvement of the carbon trading market provide a new way to realize the low-carbon operation of integrated energy systems (IES). In this study, NP units and carbon trading mechanisms are introduced into the IES to build a new low-carbon scheduling model. In view of the decrease in system operation flexibility caused by the introduction of NP unit, on the one hand, the heating renovation of the NP unit is carried out to make it a cogeneration unit, to expand its operating range and improve its operating flexibility; on the other hand, auxiliary equipment such as electricity storage system, heat storage system and power to gas unit, which can carry out energy time translation or energy form conversion, are introduced into IES to jointly improve the flexibility of system operation. In the model-solving stage, the chance-constrained programming (CCP) model considering the uncertainty of the renewable energy (RE) output is converted into an equivalent mixed-integer linear programming (MILP) model using discretized step transformation. The test system built based on real data of an IES in North China shows that the proposed method has good economic and low-carbon environmental protection benefits.
Robust Model Predictive Control of Time-Delay Systems through System Level Synthesis
Authors: Shaoru Chen, Ning-Yuan Li, Victor M. Preciado, Nikolai Matni
Date: 2022-09-23 00:00:00
Tasks: Model Predictive Control

We present a robust model predictive control method (MPC) for discrete-time linear time-delayed systems with state and control input constraints. The system is subject to both polytopic model uncertainty and additive disturbances. In the proposed method, a time-varying feedback control policy is optimized such that the robust satisfaction of all constraints for the closed-loop system is guaranteed. By encoding the effects of the delayed states and inputs into the feedback policy, we solve the robust optimal control problem in MPC using System Level Synthesis which results in a convex quadratic program that jointly conducts uncertainty over-approximation and robust controller synthesis. Notably, the number of variables in the quadratic program is independent of the delay horizon. The effectiveness and scalability of our proposed method are demonstrated numerically.
One-Shot Reachability Analysis of Neural Network Dynamical Systems
Authors: Shaoru Chen, Victor M. Preciado, Mahyar Fazlyab
Date: 2022-09-23 00:00:00

The arising application of neural networks (NN) in robotic systems has driven the development of safety verification methods for neural network dynamical systems (NNDS). Recursive techniques for reachability analysis of dynamical systems in closed-loop with a NN controller, planner, or perception can over-approximate the reachable sets of the NNDS by bounding the outputs of the NN and propagating these NN output bounds forward. However, this recursive reachability analysis may suffer from compounding errors, rapidly becoming overly conservative over a longer horizon. In this work, we prove that an alternative one-shot reachability analysis framework which directly verifies the unrolled NNDS can significantly mitigate the compounding errors for a general class of NN verification methods built on layerwise abstraction. Our analysis is motivated by the fact that certain NN verification methods give rise to looser bounds when applied in one shot than recursively. In our analysis, we characterize the performance gap between the recursive and one-shot frameworks for NNDS with general computational graphs. The applicability of one-shot analysis is demonstrated through numerical examples on a cart-pole system.
On the Endemic Behavior of a Competitive Tri-Virus SIS Networked Model
Authors: Sebin Gracy, Mengbin Ye, Brian DO Anderson, Cesar A. Uribe
Date: 2022-09-23 00:00:00

This paper studies the endemic behavior of a multi-competitive networked susceptible-infected-susceptible (SIS) model. In particular, we focus on the case where there are three competing viruses (i.e., the tri-virus system). First, we show that the tri-virus system is not a monotone system. Thereafter, we provide a condition that guarantees local exponential convergence to a boundary equilibrium (exactly one virus is endemic, the other two are dead), and identify a special case that admits the existence and local exponential attractivity of a line of coexistence equilibria (at least two viruses are active). Finally, we identify a particular case (subsumed by the aforementioned special case) such that, for all nonzero initial infection levels, the dynamics of the tri-virus system converge to a plane of coexistence equilibria.