pwc-archive/papers-with-abstracts huggingface dataset:
Harmonic Detection from Noisy Speech with Auditory Frame Gain for Intelligibility Enhancement
Authors: A. Queiroz, R. Coelho
Date: 2024-01-22 00:00:00

This paper introduces a novel (HDAG - Harmonic Detection for Auditory Gain) method for speech intelligibility enhancement in noisy scenarios. In the proposed scheme, a series of selective Gammachirp filters are adopted to emphasize the harmonic components of speech reducing the masking effects of acoustic noises. The fundamental frequency are estimated by the HHT-Amp technique. Harmonic patterns estimated with low accuracy are detected and adjusted according the FSFFE low/high pitch separation. The central frequencies of the filterbank are defined considering the third octave subbands which are best suited to cover the regions most relevant to intelligibility. Before signal reconstruction, the gammachirp filtered components are amplified by gain factors regulated by FSFFE classification. The proposed HDAG solution and three baseline techniques are examined considering six background noises with four signal-to-noise ratios. Three objective measures are adopted for the evaluation of speech intelligibility and quality. Several experiments are conducted to demonstrate that the proposed scheme achieves better speech intelligibility improvement when compared to the competing approaches. A perceptual listening test is further considered and corroborates with the objective results.
Empowering Communication: Speech Technology for Indian and Western Accents through AI-powered Speech Synthesis
Authors: Vinotha R, Hepsiba D, L. D. Vijay Anand, Deepak John Reji
Date: 2024-01-22 00:00:00
Tasks: Speaker Verification, Speech Synthesis, text-to-speech, Text to Speech, Voice Cloning

Neural Text-to-speech (TTS) synthesis is a powerful technology that can generate speech using neural networks. One of the most remarkable features of TTS synthesis is its capability to produce speech in the voice of different speakers. This paper introduces voice cloning and speech synthesis https://pypi.org/project/voice-cloning/ an open-source python package for helping speech disorders to communicate more effectively as well as for professionals seeking to integrate voice cloning or speech synthesis capabilities into their projects. This package aims to generate synthetic speech that sounds like the natural voice of an individual, but it does not replace the natural human voice. The architecture of the system comprises a speaker verification system, a synthesizer, a vocoder, and noise reduction. Speaker verification system trained on a varied set of speakers to achieve optimal generalization performance without relying on transcriptions. Synthesizer is trained using both audio and transcriptions that generate Mel spectrogram from a text and vocoder which converts the generated Mel Spectrogram into corresponding audio signal. Then the audio signal is processed by a noise reduction algorithm to eliminate unwanted noise and enhance speech clarity. The performance of synthesized speech from seen and unseen speakers are then evaluated using subjective and objective evaluation such as Mean Opinion Score (MOS), Gross Pitch Error (GPE), and Spectral distortion (SD). The model can create speech in distinct voices by including speaker characteristics that are chosen randomly.
StreamVoice: Streamable Context-Aware Language Modeling for Real-time Zero-Shot Voice Conversion
Authors: Zhichao Wang, Yuanzhe Chen, Xinsheng Wang, Lei Xie, Yuping Wang
Date: 2024-01-19 00:00:00
Tasks: Language Modeling, Language Modelling, Voice Conversion

Recent language model (LM) advancements have showcased impressive zero-shot voice conversion (VC) performance. However, existing LM-based VC models usually apply offline conversion from source semantics to acoustic features, demanding the complete source speech and limiting their deployment to real-time applications. In this paper, we introduce StreamVoice, a novel streaming LM-based model for zero-shot VC, facilitating real-time conversion given arbitrary speaker prompts and source speech. Specifically, to enable streaming capability, StreamVoice employs a fully causal context-aware LM with a temporal-independent acoustic predictor, while alternately processing semantic and acoustic features at each time step of autoregression which eliminates the dependence on complete source speech. To address the potential performance degradation from the incomplete context in streaming processing, we enhance the context-awareness of the LM through two strategies: 1) teacher-guided context foresight, using a teacher model to summarize the present and future semantic context during training to guide the model's forecasting for missing context; 2) semantic masking strategy, promoting acoustic prediction from preceding corrupted semantic and acoustic input, enhancing context-learning ability. Notably, StreamVoice is the first LM-based streaming zero-shot VC model without any future look-ahead. Experiments demonstrate StreamVoice's streaming conversion capability while achieving zero-shot performance comparable to non-streaming VC systems.
Quality-Aware Hydraulic Control in Drinking Water Networks via Controllability Proxies
Authors: Salma M. Elsherif, Mohamad H. Kazma, Ahmad F. Taha
Date: 2024-01-22 00:00:00
Tasks: Scheduling

The operation of water distribution networks simply aims at efficiently delivering consumers adequate water while maintaining safe water quality (WQ). However, this process entails a multi-scale interplay between hydraulic and WQ dynamics evolving spatio-temporally within such a complex infrastructure network. While prior research has addressed the hydraulic optimization problem and WQ regulation as decoupled or coupled, they often overlook control-theoretic guided solutions. This paper takes a novel approach by investigating the coupling between hydraulic and WQ dynamics from a control networks perspective. We propose a quality-aware control framework that embeds WQ controllability metrics into the network-level pump scheduling problem, acknowledging the direct influence of system hydraulics on WQ controller behavior. We examine the trade-offs between pump control energy cost and WQ performance across various network sizes and scenarios. Our results showcase how network topology, hydraulic constraints, and WQ metrics jointly impact optimal pump schedules and, accordingly, the achievable level of WQ regulation, offering insights into designing efficient control strategies for water infrastructure networks governed by interdependent dynamics.
Analyzing the coupling process of distributed mixed real-virtual prototypes
Authors: Peter Baumann, Lars Mikelsons, Oliver Kotte, Dieter Schramm
Date: 2024-01-22 00:00:00

The ongoing connection and automation of vehicles leads to a closer interaction of the individual vehicle components, which demands for consideration throughout the entire development process. In the design phase, this is achieved through co-simulation of component models. However, complex co-simulation environments are rarely (re-)used in the verification and validation phases, in which mixed real-virtual prototypes (e.g. Hardware-in-the-Loop) are already available. One reason for this are coupling errors such as time-delays, which inevitably occur in co-simulation of virtual and real-time systems, and which influence system behavior in an unknown and generally detrimental way. This contribution introduces a novel, adaptive method to compensate for constant time-delays in potentially highly nonlinear, spatially distributed mixed real-virtual prototypes, using small feedforward neural networks. Their optimal initialization with respect to defined frequency domain features results from a-priori frequency domain analysis of the entire coupled system, including coupling faults and compensation methods. A linear and a nonlinear example demonstrate the method and emphasize its suitability for nonlinear systems due to online training and adaptation. As the compensation method requires knowledge only of the bandwidths, the proposed method is applicable to distributed mixed real-virtual prototypes in general.
Emulation-based Stabilization for Networked Control Systems with Stochastic Channels
Authors: Wei Ren, Wei Wang, Zhuo-Rui Pan, Xi-Ming Sun, Andrew R. Teel, Dragan Nesic
Date: 2024-01-22 00:00:00
Tasks: Scheduling

This paper studies the stabilization problem of networked control systems (NCSs) with random packet dropouts caused by stochastic channels. To describe the effects of stochastic channels on the information transmission, the transmission times are assumed to be deterministic, whereas the packet transmission is assumed to be random. We first propose a stochastic scheduling protocol to model random packet dropouts, and address the properties of the proposed stochastic scheduling protocol. The proposed scheduling protocol provides a unified modelling framework for a general class of random packet dropouts due to different stochastic channels. Next, the proposed scheduling protocol is embedded into the closed-loop system, which leads to a stochastic hybrid model for NCSs with random packet dropouts. Based on this stochastic hybrid model, we follow the emulation approach to establish sufficient conditions to guarantee uniform global asymptotical stability in probability. In particular, an upper bound on the maximally allowable transmission interval is derived explicitly for all stochastic protocols satisfying Lyapunov conditions that guarantee uniform global asymptotic stability in probability. Finally, two numerical examples are presented to demonstrate the derived results.
Real-Time Systems Optimization with Black-box Constraints and Hybrid Variables
Authors: Sen Wang, Dong Li, Shao-Yu Huang, Xuanliang Deng, Ashrarul H. Sifat, Changhee Jung, Ryan Williams, Haibo Zeng
Date: 2024-01-21 00:00:00

When optimizing real-time systems, designers often face a challenging problem where the schedulability constraints are non-convex, non-continuous, or lack an analytical form to understand their properties. Although the optimization framework NORTH proposed in previous work is general (it works with arbitrary schedulability analysis) and scalable, it can only handle problems with continuous variables, which limits its application. In this paper, we extend the applications of the framework NORTH to problems with a hybrid of continuous and discrete variables. This is achieved in a coordinate-descent method, where the continuous and discrete variables are optimized separately during iterations. The new framework, NORTH+, improves around 20% solution quality than NORTH in experiments.
Model Predictive Approach for Detumbling an Underactuated Satellite
Authors: Kota Kondo, Yasuhiro Yoshimura, Mai Bando, Shuji Nagasaki, Toshiya Hanada
Date: 2024-01-21 00:00:00
Tasks: model, Model Predictive Control

This research proposes an innovative approach to detumble satellites' triple-axis angular velocities with only one single-axis magnetic torquer. Since magnetic torque is generated perpendicularly to magnetorquers, no intended control torque along the magnetorquer can be produced, which makes systems underactuated. Our paper introduces a control method using Model Predictive Control (MPC) and compares it with B-dot control algorithm. By applying these control laws to Kyushu University Light Curve Inversion (Q-Li) Demonstration Satellite in numerical simulations, we describe the applicability of these control laws to underactuated systems.
Nonlinear Model Predictive Detumbling of Small Satellites with a Single-axis Magnetorquer
Authors: Kota Kondo, Ilya Kolmanovsky, Yasuhiro Yoshimura, Mai Bando, Shuji Nagasaki, Toshiya Hanada
Date: 2024-01-21 00:00:00

Various actuators are used in spacecraft to achieve attitude stabilization, including thrusters, momentum wheels, and control moment gyros. Small satellites, however, have stringent size, weight, and cost constraints, which makes many actuator choices prohibitive. Consequently, magnetic torquers have commonly been applied to spacecraft to attenuate angular rates. Approaches for dealing with under-actuation due to magnetic control torque's dependency on the magnetic field and required high magnetic flux densities have been previously considered. Generally speaking, control of a satellite that becomes under-actuated as a result of on-board failures has been a recurrent theme in the literature. Methods for controlling spacecraft with fewer actuators than degrees of freedom are increasingly in demand due to the increased number of small satellite launches. Magnetic torquers have been extensively investigated for momentum management of spacecraft with momentum wheels and for nutation damping of spin satellites, momentum-biased, and dual-spin satellites. Nonetheless, severely under-actuated small spacecraft that carry only a single-axis magnetic torquer have not been previously treated. This note considers the detumbling of a small spacecraft using only a single-axis magnetic torquer. Even with a three-axis magnetic torquer, the spacecraft is under-actuated, while, in the case of only a single axis magnetic torquer, the problem is considerably more demanding. Our note examines the feasibility of spacecraft attitude control with a single-axis magnetic torquer and possible control methods that can be used.
Pulse Width Modulation Method Applied to Nonlinear Model Predictive Control on an Under-actuated Small Satellite
Authors: Kota Kondo, Yasuhiro Yoshimura, Shiji Nagasaki, Toshiya Hanada
Date: 2024-01-21 00:00:00
Tasks: continuous-control, Continuous Control, Model Predictive Control

Among various satellite actuators, magnetic torquers have been widely equipped for stabilization and attitude control of small satellites. Although magnetorquers are generally used with other actuators, such as momentum wheels, this paper explores a control method where only a magnetic actuation is available. We applied a nonlinear optimal control method, Nonlinear Model Predictive Control (NMPC), to small satellites, employing the generalized minimal residual (GMRES) method, which generates continuous control inputs. Onboard magnetic actuation systems often find it challenging to produce smooth magnetic moments as a control input; hence, we employ the Pulse Width Modulation (PWM) method, which discretizes a control input and reduces the burden on actuators. In our case, the PWM approach discretizes control torques generated by the NMPC scheme. This study's main contributions are investigating the NMPC and the GMRES method applied to small spacecraft and presenting the PWM control system's feasibility.
Distributed Traffic Signal Control of Interconnected Intersections: A Two-Lane Traffic Network Model
Authors: Xinfeng Ru, Weiguo Xia, Ting Bai
Date: 2024-01-21 00:00:00
Tasks: Model Predictive Control, Traffic Signal Control

Practical and accurate traffic models play an important role in capturing real traffic dynamics and then in achieving effective control performance. This paper studies traffic signal control in a traffic network with multiple interconnected intersections, where the target is to balance the vehicle density on each lane by controlling the green times of each phase at every intersection. Different from traditional road-based modeling schemes, a two-lane intersection model is first proposed to model the flow propagation in a more accurate way. A distributed model predictive control (MPC) method is then presented to assign the green times. To enable the real-time feasibility of the proposed approach, the alternating direction method of multipliers (ADMM) is incorporated with the distributed MPC scheme for solving the problem. Finally, the simulation studies performed in VISSIM for a six-intersection traffic network in Dalian, China, show the effectiveness and characteristics of the proposed method.
Application of a Novel Model Reduction Technique to the Assessment of Boundedness/Stability of Some Delay Time-Varying Vector Nonlinear Systems
Authors: Mark A. Pinsky
Date: 2024-01-21 00:00:00

Assessing the boundedness and stability of vector nonlinear systems with variable delays and coefficients remains a challenging problem with broad applications in science and engineering. Existing methods tend to produce overly conservative criteria that offer limited practical value and often fail to explicitly characterize the temporal evolution of solution norms. This paper presents a novel framework for evaluating the evolution of solution norms in such systems. This approach constructs scalar counterparts for the original vector equations. We prove that the solutions to these scalar nonlinear equations, which also include delays and variable coefficients, provide upper bounds for the norms of the original solutions, if the history functions for both equations are properly matched. This reduction enables the evaluation of the boundedness and stability of vector systems through the analysis of the dynamics of their scalar counterparts, which can be performed via straightforward simulations or simplified analytical reasoning. Consequently, we introduce new criteria for boundedness and stability and estimate the radii of the balls containing history functions that yield bounded or stable solutions for the original vector systems. Finally, we validated our inferences through representative simulations that also assessed the accuracy of the proposed approach.
Error bounds of constant gain least-mean-squares algorithms
Authors: Chang Liu, Antwan D. Clark
Date: 2024-01-20 00:00:00

Constant gain least-mean-squares (LMS) algorithms have a wide range of applications in trajectory tracking problems, but the formal convergence of LMS in mean square is not yet fully established. This work provides an upper bound on the constant gain that guarantees a bounded mean-squared error of LMS for a general design vector. These results highlight the role of the fourth-order moment of the design vector. Numerical examples demonstrate the applicability of this upper bound in setting a constant gain in LMS, while existing criteria may fail. We also provide the associated error bound, which can be applied to design vectors with linearly dependent elements.
A Hierarchical Decision-Based Maintenance for a Complex Modular System Driven by the { MoMA} Algorithm
Authors: M. L. Gamiz, D. Montoro-Cazorla, M. C. Segovia-Garcia
Date: 2024-01-20 00:00:00

This paper presents a maintenance policy for a modular system formed by K independent modules (n-subsystems) subjected to environmental conditions (shocks). For the modeling of this complex system, the use of the Matrix-Analytical Method (MAM) is proposed under a layered approach according to its hierarchical structure. Thus, the operational state of the system (top layer) depends on the states of the modules (middle layer), which in turn depend on the states of their components (bottom layer). This allows a detailed description of the system operation to plan maintenance actions appropriately and optimally. We propose a hierarchical decision-based maintenance strategy with periodic inspections as follows: at the time of the inspection, the condition of the system is first evaluated. If intervention is necessary, the modules are then checked to make individual decisions based on their states, and so on. Replacement or repair will be carried out as appropriate. An optimization problem is formulated as a function of the length of the inspection period and the intervention cost incurred over the useful life of the system. Our method shows the advantages, providing compact and implementable expressions. The model is illustrated on a submarine Electrical Control Unit (ECU).
Transversally exponentially stable Euclidean space extension technique for discrete time systems
Authors: Soham Shanbhag, Dong Eui Chang
Date: 2024-01-20 00:00:00

We propose a modification technique for discrete time systems for exponentially fast convergence to compact sets. The extension technique allows us to use tools defined on Euclidean spaces to systems evolving on manifolds by modifying the dynamics of the system such that the manifold is an attractor set. We show the stability properties of this technique using the simulation of the rigid body rotation system on the unit sphere $S^3$. We also show the improvement afforded due to this technique on a Luenberger like observer designed for the rigid body rotation system on $S^3$.
Angular velocity and linear acceleration measurement bias estimators for the rigid body system with global exponential convergence
Authors: Soham Shanbhag, Dong Eui Chang
Date: 2024-01-20 00:00:00

Rigid body systems usually consider measurements of the pose of the body using onboard cameras/LiDAR systems, that of linear acceleration using an accelerometer and of angular velocity using an IMU. However, the measurements of the linear acceleration and angular velocity are usually biased with an unknown constant or slowly varying bias. We propose a measurement bias estimator for such systems under assumption of boundedness of angular velocity. We also provide continuous estimates to the state of the system, i.e. the pose, linear velocity, and position of the body. These estimates are globally exponentially convergent to the state of the rigid body system. We propose two bias estimators designed with the estimate of the pose in the ambient Euclidean space of the Special Euclidean group and show global exponential convergence of the proposed observers to the state of the system. The first observer assumes knowledge of bounds of the angular velocity, while the second observer uses a Riccati observer to overcome this limitation. We show the convergence with an example of a rigid body rotation and translation system on the special Euclidean group. We show that the observer is able to estimate the bias using data collected from an Intel Realsense camera.
Globally exponentially convergent observer for systems evolving on matrix Lie groups
Authors: Soham Shanbhag, Dong Eui Chang
Date: 2024-01-20 00:00:00
Tasks: Translation

We propose a globally exponentially convergent observer for the dynamical system evolving on matrix Lie groups with bounded velocity with unknown bound. We design the observer in the ambient Euclidean space and show exponential convergence of the observer to the state of the system. We show the convergence with an example of a rigid body rotation and translation system on the special Euclidean group. We compare the proposed observer with an observer present in the literature.
Predictive stability filters for nonlinear dynamical systems affected by disturbances
Authors: Alexandre Didier, Andrea Zanelli, Kim P. Wabersich, Melanie N. Zeilinger
Date: 2024-01-20 00:00:00
Tasks: Model Predictive Control

Predictive safety filters provide a way of projecting potentially unsafe inputs, proposed, e.g. by a human or learning-based controller, onto the set of inputs that guarantee recursive state and input constraint satisfaction by leveraging model predictive control techniques. In this paper, we extend this framework such that in addition, robust asymptotic stability of the closed-loop system can be guaranteed by enforcing a decrease of an implicit Lyapunov function which is constructed using a predicted system trajectory. Differently from previous results, we show robust asymptotic stability with respect to a predefined disturbance set on an extended state consisting of the system state and a warmstart input sequence. The proposed strategy is applied to an automotive lane keeping example in simulation.