pwc-archive/papers-with-abstracts huggingface dataset:
Drop the beat! Freestyler for Accompaniment Conditioned Rapping Voice Generation
Authors: Ziqian Ning, Shuai Wang, Yuepeng Jiang, Jixun Yao, Lei He, Shifeng Pan, Jie Ding, Lei Xie
Date: 2024-08-28 00:00:00
Tasks: Language Modeling, Language Modelling

Rap, a prominent genre of vocal performance, remains underexplored in vocal generation. General vocal synthesis depends on precise note and duration inputs, requiring users to have related musical knowledge, which limits flexibility. In contrast, rap typically features simpler melodies, with a core focus on a strong rhythmic sense that harmonizes with accompanying beats. In this paper, we propose Freestyler, the first system that generates rapping vocals directly from lyrics and accompaniment inputs. Freestyler utilizes language model-based token generation, followed by a conditional flow matching model to produce spectrograms and a neural vocoder to restore audio. It allows a 3-second prompt to enable zero-shot timbre control. Due to the scarcity of publicly available rap datasets, we also present RapBank, a rap song dataset collected from the internet, alongside a meticulously designed processing pipeline. Experimental results show that Freestyler produces high-quality rapping voice generation with enhanced naturalness and strong alignment with accompanying beats, both stylistically and rhythmically.
Examining the Interplay Between Privacy and Fairness for Speech Processing: A Review and Perspective
Authors: Anna Leschanowsky, Sneha Das
Date: 2024-08-27 00:00:00
Tasks: Fairness

Speech technology has been increasingly deployed in various areas of daily life including sensitive domains such as healthcare and law enforcement. For these technologies to be effective, they must work reliably for all users while preserving individual privacy. Although tradeoffs between privacy and utility, as well as fairness and utility, have been extensively researched, the specific interplay between privacy and fairness in speech processing remains underexplored. This review and position paper offers an overview of emerging privacy-fairness tradeoffs throughout the entire machine learning lifecycle for speech processing. By drawing on well-established frameworks on fairness and privacy, we examine existing biases and sources of privacy harm that coexist during the development of speech processing models. We then highlight how corresponding privacy-enhancing technologies have the potential to inadvertently increase these biases and how bias mitigation strategies may conversely reduce privacy. By raising open questions, we advocate for a comprehensive evaluation of privacy-fairness tradeoffs for speech technology and the development of privacy-enhancing and fairness-aware algorithms in this domain.
DualSpeech: Enhancing Speaker-Fidelity and Text-Intelligibility Through Dual Classifier-Free Guidance
Authors: Jinhyeok Yang, Junhyeok Lee, Hyeong-Seok Choi, Seunghun Ji, Hyeongju Kim, Juheon Lee
Date: 2024-08-26 00:00:00
Tasks: Diversity, text-to-speech, Text to Speech

Text-to-Speech (TTS) models have advanced significantly, aiming to accurately replicate human speech's diversity, including unique speaker identities and linguistic nuances. Despite these advancements, achieving an optimal balance between speaker-fidelity and text-intelligibility remains a challenge, particularly when diverse control demands are considered. Addressing this, we introduce DualSpeech, a TTS model that integrates phoneme-level latent diffusion with dual classifier-free guidance. This approach enables exceptional control over speaker-fidelity and text-intelligibility. Experimental results demonstrate that by utilizing the sophisticated control, DualSpeech surpasses existing state-of-the-art TTS models in performance. Demos are available at https://bit.ly/48Ewoib.
Comparative Analysis Of Discriminative Deep Learning-Based Noise Reduction Methods In Low SNR Scenarios
Authors: Shrishti Saha Shetu, Emanuël A. P. Habets, Andreas Brendel
Date: 2024-08-26 00:00:00

In this study, we conduct a comparative analysis of deep learning-based noise reduction methods in low signal-to-noise ratio (SNR) scenarios. Our investigation primarily focuses on five key aspects: The impact of training data, the influence of various loss functions, the effectiveness of direct and indirect speech estimation techniques, the efficacy of masking, mapping, and deep filtering methodologies, and the exploration of different model capacities on noise reduction performance and speech quality. Through comprehensive experimentation, we provide insights into the strengths, weaknesses, and applicability of these methods in low SNR environments. The findings derived from our analysis are intended to assist both researchers and practitioners in selecting better techniques tailored to their specific applications within the domain of low SNR noise reduction.
Combined assessment of auditory distance perception and externalization
Authors: Henning Hoppe, Steven van de Par, Virginia Flanagin, Stephan D. Ewert
Date: 2024-08-26 00:00:00

This study investigates frontal auditory distance perception (ADP) and externalization in virtual audio-visual environments, considering effects of headphone rendering method, room size, reverberation, and visual representation of the room. Either head-related impulse responses from an artificial head or a spherical head model were used for diotic (monophonic) and binaural auralizations with and without real-time head tracking. The visuals were presented through a head-mounted display. Two differently sized rooms as well as an infinitely extending space (echoic and anechoic) were used in which an invisible frontal virtual sound source was located. Additionally, the effect of a freely movable loudspeaker for visually indicating perceived distances was investigated. Both ADP and externalization were significantly affected by room size, but otherwise the two perceptual quantities differed in their outcomes. Room visibility significantly affected ADP, leading to considerable overestimations and more variability in the absence of a visual environment, although externalization was not affected. The movable loudspeaker improved distance estimation significantly, however, did not affect externalization. For reverberation, a (non-significant) trend of improved ADP was observed, however, externalization was significantly improved. Different headphone renderings did not significantly affect ADP or externalization, although a clear trend was observed for externalization.
Energy Control of Grid-forming Energy Storage based on Bandwidth Separation Principle
Authors: Chu Sun, Syed Qaseem Ali, Geza Joos
Date: 2024-08-29 00:00:00

The reduced inertia in power system introduces more operation risks and challenges to frequency regulation. The existing virtual inertia and frequency support control are restricted by the normally non-dispatchable energy resources behind the power electronic converters. In this letter, an improved virtual synchronous machine (VSM) control based on energy storage is proposed, considering the limitation of state-of-charge. The steady-state energy consumed by energy storage in inertia, damping and frequency services is investigated. Based on bandwidth separation principle, an energy recovery control is designed to restore the energy consumed, thereby ensuring constant energy reserve. Effectiveness of the proposed control and design is verified by comprehensive simulation results.
Multi-layer optimisation of hybrid energy storage systems for electric vehicles
Authors: Wouter Andriesse, Jorn van Kampen, Theo Hofman
Date: 2024-08-29 00:00:00

This research presents a multi-layer optimization framework for hybrid energy storage systems (HESS) for passenger electric vehicles to increase the battery system's performance by combining multiple cell chemistries. Specifically, we devise a battery model capturing voltage dynamics, temperature and lifetime degradation solely using data from manufacturer datasheets, and jointly optimize the capacity distribution between the two batteries and the power split, for a given drive cycle and HESS topology. The results show that the lowest energy consumption is obtained with a hybrid solution consisting of a NCA-NMC combination, since this provides the best trade-off between efficiency and added weight.
System-level thermal and electrical modeling of battery systems for electric aircraft design
Authors: Thomas Kuijpers, Jorn van Kampen, Theo Hofman
Date: 2024-08-29 00:00:00

This work introduces a framework for simulating the electrical power consumption of an 8-seater electric aircraft equipped with high-energy-density NMC Lithium-ion cells. We propose an equivalent circuit model (ECM) to capture the thermal and electrical battery behavior. Furthermore, we assess the need for a battery thermal management system (BTMS) by determining heat generation at the cell level and optimize BTMS design to minimize energy consumption over a predefined flight regime. The proposed baseline battery design includes a 304-kWh battery system with BTMS, ensuring failure redundancy through two parallel switched battery banks. Simulation results explore the theoretical flight range without BTMS and reveal advantages in increasing battery capacity under specific conditions. Optimization efforts focus on BTMS design, highlighting the superior performance of water cooling over air cooling. However, the addition of a 9.9 kW water-cooled BTMS results in a 16.5% weight increase (387 kg) compared to no BTMS, reducing the simulated range of the aircraft from 480 km to 410 km. Lastly, we address a heating-induced thermal runaway scenario, demonstrating the robustness of the proposed battery design in preventing thermal runaway.
Active Search for Low-altitude UAV Sensing and Communication for Users at Unknown Locations
Authors: Yuanshuai Zheng, Junting Chen
Date: 2024-08-26 00:00:00

This paper studies optimal unmanned aerial vehicle (UAV) placement to ensure line-of-sight (LOS) communication and sensing for a cluster of ground users possibly in deep shadow, while the UAV maintains backhaul connectivity with a base station (BS). The key challenges include unknown user locations, uncertain channel model parameters, and unavailable urban structure. Addressing these challenges, this paper focuses on developing an efficient online search strategy which jointly estimates channels, guides UAV positioning, and optimizes resource allocation. Analytically exploiting the geometric properties of the equipotential surface, this paper develops an LOS discovery trajectory on the equipotential surface while the closed-form search directions are determined using perturbation theory. Since the explicit expression of the equipotential surface is not available, this paper proposes to locally construct a channel model for each user in the LOS regime utilizing polynomial regression without depending on user locations or propagation distance. A class of spiral trajectories to simultaneously construct the LOS channels and search on the equipotential surface is developed. An optimal radius of the spiral and an optimal measurement pattern for channel gain estimation are derived to minimize the mean squared error (MSE) of the locally constructed channel. Numerical results on real 3D city maps demonstrate that the proposed scheme achieves over 94% of the performance of a 3D exhaustive search scheme with just a 3-kilometer search.
Deep DeePC: Data-enabled predictive control with low or no online optimization using deep learning
Authors: Xuewen Zhang, Kaixiang Zhang, Zhaojian Li, Xunyuan Yin
Date: 2024-08-29 00:00:00

Data-enabled predictive control (DeePC) is a data-driven control algorithm that utilizes data matrices to form a non-parametric representation of the underlying system, predicting future behaviors and generating optimal control actions. DeePC typically requires solving an online optimization problem, the complexity of which is heavily influenced by the amount of data used, potentially leading to expensive online computation. In this paper, we leverage deep learning to propose a highly computationally efficient DeePC approach for general nonlinear processes, referred to as Deep DeePC. Specifically, a deep neural network is employed to learn the DeePC vector operator, which is an essential component of the non-parametric representation of DeePC. This neural network is trained offline using historical open-loop input and output data of the nonlinear process. With the trained neural network, the Deep DeePC framework is formed for online control implementation. At each sampling instant, this neural network directly outputs the DeePC operator, eliminating the need for online optimization as conventional DeePC. The optimal control action is obtained based on the DeePC operator updated by the trained neural network. To address constrained scenarios, a constraint handling scheme is further proposed and integrated with the Deep DeePC to handle hard constraints during online implementation. The efficacy and superiority of the proposed Deep DeePC approach are demonstrated using two benchmark process examples.
Economic Optimal Power Management of Second-Life Battery Energy Storage Systems
Authors: Amir Farakhor, Di wu, Pingen Chen, Junmin Wang, Yebin Wang, Huazhen Fang
Date: 2024-08-29 00:00:00
Tasks: Management

Second-life battery energy storage systems (SL-BESS) are an economical means of long-duration grid energy storage. They utilize retired battery packs from electric vehicles to store and provide electrical energy at the utility scale. However, they pose critical challenges in achieving optimal utilization and extending their remaining useful life. These complications primarily result from the constituent battery packs' inherent heterogeneities in terms of their size, chemistry, and degradation. This paper proposes an economic optimal power management approach to ensure the cost-minimized operation of SL-BESS while adhering to safety regulations and maintaining a balance between the power supply and demand. The proposed approach takes into account the costs associated with the degradation, energy loss, and decommissioning of the battery packs. In particular, we capture the degradation costs of the retired battery packs through a weighted average Ah-throughput aging model. The presented model allows us to quantify the capacity fading for second-life battery packs for different operating temperatures and C-rates. To evaluate the performance of the proposed approach, we conduct extensive simulations on a SL-BESS consisting of various heterogeneous retired battery packs in the context of grid operation. The results offer novel insights into SL-BESS operation and highlight the importance of prudent power management to ensure economically optimal utilization.
Internet of Things Networks: Enabling Simultaneous Wireless Information and Power Transfer
Authors: Prerna Dhull, Andrea P. Guevara, Maral Ansari, Sofie Pollin, Negin Shariati, Dominique Schreurs
Date: 2024-08-29 00:00:00

The number of sensors deployed in the world is expected to explode in the near future. At this moment, nearly 30 billion Internet of Things (IoT) devices are connected and this number is expected to double in the next four years. While not all of these are battery powered, as technology becomes smaller and mobility becomes more important to consumers, soon a larger portion will be. This forecast predicts that the number of machine to machine (M2M) devices will have the largest increase, representing nearly 50% of all devices in 2023.
A Control Theoretic Approach to Simultaneously Estimate Average Value of Time and Determine Dynamic Price for High-occupancy Toll Lanes
Authors: Xuting Wang, Wen-Long Jin, Yafeng Yin
Date: 2024-08-28 00:00:00

The dynamic pricing problem of a freeway corridor with high-occupancy toll (HOT) lanes was formulated and solved based on a point queue abstraction of the traffic system [Yin and Lou, 2009]. However, existing pricing strategies cannot guarantee that the closed-loop system converges to the optimal state, in which the HOT lanes' capacity is fully utilized but there is no queue on the HOT lanes, and a well-behaved estimation and control method is quite challenging and still elusive. This paper attempts to fill the gap by making three fundamental contributions: (i) to present a simpler formulation of the point queue model based on the new concept of residual capacity, (ii) to propose a simple feedback control theoretic approach to estimate the average value of time and calculate the dynamic price, and (iii) to analytically and numerically prove that the closed-loop system is stable and guaranteed to converge to the optimal state, in either Gaussian or exponential manners.
Practical Challenges for Reliable RIS Deployment in Heterogeneous Multi-Operator Multi-Band Networks
Authors: Mehdi Monemi, Mehdi Rasti, Arthur S. de Sena, Mohammad Amir Fallah, Matti Latva-aho, Marco Di Renzo
Date: 2024-08-28 00:00:00

Reconfigurable intelligent surfaces (RISs) have been introduced as arrays of nearly passive elements with software-tunable electromagnetic properties to dynamically manipulate the reflection/transmission of radio signals. Research works in this area are focused on two applications, namely {\it user-assist} RIS aiming at tuning the RIS to enhance the quality-of-service (QoS) of target users, and the {\it malicious} RIS aiming for an attacker to degrade the QoS at victim receivers through generating {\it intended} destructive interference. While both user-assist and malicious RIS applications have been explored extensively, the impact of RIS deployments on imposing {\it unintended} interference on various wireless user-equipments (EUs) remains underexplored. This paper investigates the challenges of integrating RISs into multi-carrier, multi-user, and multi-operator networks. We discuss how RIS deployments intended to benefit specific users can negatively impact other users served at various carrier frequencies through different network operators. While not an ideal solution, we discuss how ultra-narrowband metasurfaces can be incorporated into the manufacturing of RISs to mitigate some challenges of RIS deployment in wireless networks. We also present a simulation scenario to illuminate some practical challenges associated with the deployment of RISs in shared public environments.
A Stochastic Robust Adaptive Systems Level Approach to Stabilizing Large-Scale Uncertain Markovian Jump Linear Systems
Authors: SooJean Han, Minwoo M. Kim, Ieun Choo
Date: 2024-08-28 00:00:00
Tasks: Bayesian Inference

We propose a unified framework for robustly and adaptively stabilizing large-scale networked uncertain Markovian jump linear systems (MJLS) under external disturbances and mode switches that can change the network's topology. Adaptation is achieved by using minimal information on the disturbance to identify modes that are consistent with observable data. Robust control is achieved by extending the system level synthesis (SLS) approach, which allows us to pose the problem of simultaneously stabilizing multiple plants as a two-step convex optimization procedure. Our control pipeline computes a likelihood distribution of the system's current mode, uses them as probabilistic weights during simultaneous stabilization, then updates the likelihood via Bayesian inference. Because of this "softer" probabilistic approach to robust stabilization, our control pipeline does not suffer from abrupt destabilization issues due to changes in the system's true mode, which were observed in a previous method. Separability of SLS also lets us compute localized robust controllers for each subsystem, allowing for network scalability; we use several information consensus methods so that mode estimation can also be done locally. We apply our algorithms to disturbance-rejection on two sample dynamic power grid networks, a small-scale system with 7 nodes and a large-scale grid of 25 nodes.
Linear-Quadratic Dynamic Games as Receding-Horizon Variational Inequalities
Authors: Emilio Benenati, Sergio Grammatico
Date: 2024-08-28 00:00:00

We consider dynamic games with linear dynamics and quadratic objective functions. We observe that the unconstrained open-loop Nash equilibrium coincides with the LQR in an augmented space, thus deriving an explicit expression of the cost-to-go. With such cost-to-go as a terminal cost, we show asymptotic stability for the receding-horizon solution of the finite-horizon, constrained game. Furthermore, we show that the problem is equivalent to a non-symmetric variational inequality, which does not correspond to any Nash equilibrium problem. For unconstrained closed-loop Nash equilibria, we derive a receding-horizon controller that is equivalent to the infinite-horizon one and ensures asymptotic stability.
Sufficient and Necessary Barrier-like Conditions for Safety and Reach-avoid Verification of Stochastic Discrete-time Systems
Authors: Bai Xue
Date: 2024-08-28 00:00:00

In this paper, we examine necessary and sufficient barrier-like conditions for infinite-horizon safety verification and reach-avoid verification of stochastic discrete-time systems, derived through a relaxation of Bellman equations. Unlike previous methods focused on barrier-like conditions that primarily address sufficiency, our work rigorously integrates both necessity and sufficiency for properties pertaining to infinite time. Safety verification aims to certify the satisfaction of the safety property, which stipulates that the probability of the system, starting from a specified initial state, remaining within a safe set always is greater than or equal to a specified lower bound. A necessary and sufficient barrier-like condition is formulated for safety verification. In contrast, reach-avoid verification extends beyond safety to include reachability, seeking to certify the satisfaction of the reach-avoid property. It requires that the probability of the system, starting from a specified initial state, reaching a target set eventually while remaining within a safe set until the first hit of the target, is greater than or equal to a specified lower bound. Two necessary and sufficient barrier-like conditions are formulated under certain assumptions.
On the Existence of Linear Observed Systems on Manifolds with Connection
Authors: Changwu Liu, Yuan Shen
Date: 2024-08-28 00:00:00

Linear observed systems on manifolds are a special class of nonlinear systems whose state spaces are smooth manifolds but possess properties similar to linear systems. Such properties can be characterized by preintegration and exact linearization with Jacobians independent of the linearization point. Non-biased IMU dynamics in navigation can be constructed into linear observed settings, leading to invariant filters with guaranteed behaviors such as local convergence and consistency. In this letter, we establish linear observed property for systems evolving on a smooth manifold through the connection structure endowed upon this space. Our key findings are the existence of linear observed systems on manifolds poses constraints on the curvature of the state space, beyond requiring the dynamics to be compatible with some connection-preserving transformations. Specifically, the flat connection case reproduces the characterization of linear observed systems on Lie groups, showing our theory is a true generalization.