pwc-archive/papers-with-abstracts huggingface dataset:
Developing a Speech Recognition System for Recognizing Tonal Speech Signals Using a Convolutional Neural Network
Authors: Sakshi Dua, Sethuraman Sambath Kumar, Yasser Albagory, Rajakumar Ramalingam, Ankur Dumka, Rajesh Singh, MAMOON RASHID, Anita Gehlot, Sultan S. Alshamrani, Ahmed Saeed AlGhamdi
Date: 2022-06-17 00:00:00
Proceeding: 05-24 2022 6
Tasks: speech-recognition, Speech Recognition, Speech-to-Text

Deep learning-based machine learning models have shown significant results in speech recognition and numerous vision-related tasks. The performance of the present speech-to-text model relies upon the hyperparameters used in this research work. In this research work, it is shown that convolutional neural networks (CNNs) can model raw and tonal speech signals. Their performance is on par with existing recognition systems. This study extends the role of the CNN-based approach to robust and uncommon speech signals (tonal) using its own designed database for target research. The main objective of this research work was to develop a speech-to-text recognition system to recognize the tonal speech signals of Gurbani hymns using a CNN. Further, the CNN model, with six layers of 2DConv, 2DMax Pooling, and 256 dense layer units (Google’s TensorFlow service) was also used in this work, as well as Praat for speech segmentation. Feature extraction was enforced using the MFCC feature extraction technique, which extracts standard speech features and features of background music as well. Our study reveals that the CNN-based method for identifying tonal speech sentences and adding instrumental knowledge performs better than the existing and conventional approaches. The experimental results demonstrate the significant performance of the present CNN architecture by providing an 89.15% accuracy rate and a 10.56% WER for continuous and extensive vocabulary sentences of speech signals with different tones.
Keywords:
machine learning; Convolutional neural network; speech recognition; feature extraction; Praat; word error rate; speech signal
A systematic literature review on solution approaches for the index tracking problem in the last decade
Authors: Julio Cezar Soares Silva, Adiel Teixeira de Almeida Filho
Date: 2023-06-02 00:00:00
Tasks: Management, Systematic Literature Review

The passive management approach offers conservative investors a way to reduce risk concerning the market. This investment strategy aims at replicating a specific index, such as the NASDAQ Composite or the FTSE100 index. The problem is that buying all the index's assets incurs high rebalancing costs, and this harms future returns. The index tracking problem concerns building a portfolio that follows a specific benchmark with fewer transaction costs. Since a subset of assets is required to solve the index problem this class of problems is NP-hard, and in the past years, researchers have been studying solution approaches to obtain tracking portfolios more practically. This work brings an analysis, spanning the last decade, of the advances in mathematical approaches for index tracking. The systematic literature review covered important issues, such as the most relevant research areas, solution methods, and model structures. Special attention was given to the exploration and analysis of metaheuristics applied to the index tracking problem.
Safe Environmental Envelopes of Discrete Systems
Authors: Rômulo Meira-Góes, Ian Dardik, Eunsuk Kang, Stéphane Lafortune, Stavros Tripakis
Date: 2023-06-01 00:00:00

A safety verification task involves verifying a system against a desired safety property under certain assumptions about the environment. However, these environmental assumptions may occasionally be violated due to modeling errors or faults. Ideally, the system guarantees its critical properties even under some of these violations, i.e., the system is \emph{robust} against environmental deviations. This paper proposes a notion of \emph{robustness} as an explicit, first-class property of a transition system that captures how robust it is against possible \emph{deviations} in the environment. We modeled deviations as a set of \emph{transitions} that may be added to the original environment. Our robustness notion then describes the safety envelope of this system, i.e., it captures all sets of extra environment transitions for which the system still guarantees a desired property. We show that being able to explicitly reason about robustness enables new types of system analysis and design tasks beyond the common verification problem stated above. We demonstrate the application of our framework on case studies involving a radiation therapy interface, an electronic voting machine, a fare collection protocol, and a medical pump device.
Efficient Ray-Tracing Channel Emulation in Industrial Environments: An Analysis of Propagation Model Impact
Authors: Gurjot Singh Bhatia, Yoann Corre, M. Di Renzo
Date: 2023-06-02 00:00:00

Industrial environments are considered to be severe from the point of view of electromagnetic (EM) wave propagation. When dealing with a wide range of industrial environments and deployment setups, ray-tracing channel emulation can capture many distinctive characteristics of a propagation scenario. Ray-tracing tools often require a detailed and accurate description of the propagation scenario. Consequently, industrial environments composed of complex objects can limit the effectiveness of a ray-tracing tool and lead to computationally intensive simulations. This study analyzes the impact of using different propagation models by evaluating the number of allowed ray path interactions and digital scenario representation for an industrial environment. This study is realized using the Volcano ray-tracing tool at frequencies relevant to 5G industrial networks: 2 GHz (mid-band) and 28 GHz (high-band). This analysis can help in enhancing a ray-tracing tool that relies on a digital representation of the propagation environment to produce deterministic channel models for Indoor Factory (InF) scenarios, which can subsequently be used for industrial network design.
Auditory Representation Effective for Estimating Vocal Tract Information
Authors: Toshio Irino, Shintaro Doan
Date: 2023-06-02 00:00:00

We can estimate the size of the speakers based on their speech sounds alone. We had proposed an auditory computational theory of the Stabilised Wavelet-Mellin Transform (SWMT), which segregates information about the size and shape of the vocal tract and glottal vibration, to explain this observation. It has been shown that the auditory representation or excitation pattern (EP) associated with a weighting function based on the SWMT, termed the ``SSI weight,'' can account for the psychometric functions of size perception. In this study, we investigated whether EP with SSI weight can accurately estimate vocal tract lengths (VTLs) which were measured by magnetic resonance imaging (MRI) in male and female subjects. It was found that the use of SSI weight significantly improved the VTL estimation. Furthermore, the estimation errors in the EP with the SSI weight were significantly smaller than those in the commonly used spectra derived from the Fourier transform, Mel filterbank, and WORLD vocoder. It was also shown that the SSI weight can be easily introduced into these spectra to improve the performance.
HD-DEMUCS: General Speech Restoration with Heterogeneous Decoders
Authors: Doyeon Kim, Soo-Whan Chung, Hyewon Han, Youna Ji, Hong-Goo Kang
Date: 2023-06-02 00:00:00
Tasks: Decoder

This paper introduces an end-to-end neural speech restoration model, HD-DEMUCS, demonstrating efficacy across multiple distortion environments. Unlike conventional approaches that employ cascading frameworks to remove undesirable noise first and then restore missing signal components, our model performs these tasks in parallel using two heterogeneous decoder networks. Based on the U-Net style encoder-decoder framework, we attach an additional decoder so that each decoder network performs noise suppression or restoration separately. We carefully design each decoder architecture to operate appropriately depending on its objectives. Additionally, we improve performance by leveraging a learnable weighting factor, aggregating the two decoder output waveforms. Experimental results with objective metrics across various environments clearly demonstrate the effectiveness of our approach over a single decoder or multi-stage systems for general speech restoration task.
Adaptive ship-radiated noise recognition with learnable fine-grained wavelet transform
Authors: Yuan Xie, Jiawei Ren, Ji Xu
Date: 2023-05-31 00:00:00
Tasks: Transfer Learning

Analyzing the ocean acoustic environment is a tricky task. Background noise and variable channel transmission environment make it complicated to implement accurate ship-radiated noise recognition. Existing recognition systems are weak in addressing the variable underwater environment, thus leading to disappointing performance in practical application. In order to keep the recognition system robust in various underwater environments, this work proposes an adaptive generalized recognition system - AGNet (Adaptive Generalized Network). By converting fixed wavelet parameters into fine-grained learnable parameters, AGNet learns the characteristics of underwater sound at different frequencies. Its flexible and fine-grained design is conducive to capturing more background acoustic information (e.g., background noise, underwater transmission channel). To utilize the implicit information in wavelet spectrograms, AGNet adopts the convolutional neural network with parallel convolution attention modules as the classifier. Experiments reveal that our AGNet outperforms all baseline methods on several underwater acoustic datasets, and AGNet could benefit more from transfer learning. Moreover, AGNet shows robust performance against various interference factors.
Scale-free Non-collaborative Linear Protocol Design for A Class of Homogeneous Multi-agent Systems
Authors: Zhenwei Liu, Ali Saberi, Anton A. Stoorvogel
Date: 2023-06-02 00:00:00

In this paper, we have focused on identifying a class of continuous- and discrete-time MAS for which a scale-free non-collaborative (i.e., scale-free fully distributed) linear protocol design is developed. We have identified conditions on agent models that enable us to design scalable linear protocols. Moreover, we show that these conditions are necessary if the agents are single input and single output. We also provide a complete design of scalable protocols for this class.
Active Noise Control in The New Century: The Role and Prospect of Signal Processing
Authors: Dongyuan Shi, Bhan Lam, Woon-Seng Gan, Jordan Cheer, Stephen J. Elliott
Date: 2023-06-02 00:00:00

Since Paul Leug's 1933 patent application for a system for the active control of sound, the field of active noise control (ANC) has not flourished until the advent of digital signal processors forty years ago. Early theoretical advancements in digital signal processing and processors laid the groundwork for the phenomenal growth of the field, particularly over the past quarter-century. The widespread commercial success of ANC in aircraft cabins, automobile cabins, and headsets demonstrates the immeasurable public health and economic benefits of ANC. This article continues where Elliott and Nelson's 1993 Signal Processing Magazine article and Elliott's 1997 50th anniversary commentary on ANC left off, tracing the technical developments and applications in ANC spurred by the seminal texts of Nelson and Elliott (1991), Kuo and Morgan (1996), Hansen and Snyder (1996), and Elliott (2001) since the turn of the century. This article focuses on technical developments pertaining to real-world implementations, such as improving algorithmic convergence, reducing system latency, and extending control to non-stationary and/or broadband noise, as well as the commercial transition challenges from analog to digital ANC systems. Finally, open issues and the future of ANC in the era of artificial intelligence are discussed.
Physics-Augmented Data-EnablEd Predictive Control for Eco-driving of Mixed Traffic Considering Diverse Human Behaviors
Authors: Dongjun Li, Kaixiang Zhang, Haoxuan Dong, Qun Wang, Zhaojian Li, Ziyou Song
Date: 2023-06-02 00:00:00

Data-driven cooperative control of connected and automated vehicles (CAVs) has gained extensive research interest as it can utilize collected data to generate control actions without relying on parametric system models that are generally challenging to obtain. Existing methods mainly focused on improving traffic safety and stability, while less emphasis has been placed on energy efficiency in the presence of uncertainties and diversities of human-driven vehicles (HDVs). In this paper, we employ a data-enabled predictive control (DeePC) scheme to address the eco-driving of mixed traffic flows with diverse behaviors of human drivers. Specifically, by incorporating the physical relationship of the studied system and the Hankel matrix update from the generalized behavior representation to a particular one, we develop a new Physics-Augmented Data-EnablEd Predictive Control (PA-DeePC) approach to handle human driver diversities. In particular, a power consumption term is added to the DeePC cost function to reduce the holistic energy consumption of both CAVs and HDVs. Simulation results demonstrate the effectiveness of our approach in accurately capturing random human driver behaviors and addressing the complex dynamics of mixed traffic flows, while ensuring driving safety and traffic efficiency. Furthermore, the proposed optimization framework achieves substantial reductions in energy consumption, i.e., average reductions of 4.83% and 9.16% when compared to the benchmark algorithms.
Average AoI Minimization for Energy Harvesting Relay-aided Status Update Network Using Deep Reinforcement Learning
Authors: Sin-Yu Huang, Kuang-Hao, Liu
Date: 2023-06-02 00:00:00
Tasks: Deep Reinforcement Learning

A dual-hop status update system aided by energy harvesting (EH) relays with finite data and energy buffers is studied in this work. To achieve timely status updates, the best relays should be selected to minimize the average age of information (AoI), which is a recently proposed metric to evaluate information freshness. The average AoI minimization can be formulated as a Markov decision process (MDP), but the state space for capturing channel and buffer evolution grows exponentially with the number of relays, leading to high solution complexity. We propose a relay selection (RS) scheme based on deep reinforcement learning (DRL) according to the instantaneous channel packet freshness and buffer information of each relay. Simulation results show a significant improvement of the proposed DRL-based RS scheme over state-of-art approaches.
The Effects of Varying Penetration Rates of L4-L5 Autonomous Vehicles on Fuel Efficiency and Mobility of Traffic Networks
Authors: Ozgenur Kavas-Torris, M. Ridvan Cantas, Karina Meneses Cime, Bilin Aksun-Guvenc, Levent Guvenc
Date: 2023-06-01 00:00:00
Tasks: Autonomous Vehicles

Microscopic traffic simulators that simulate realistic traffic flow are crucial in studying, understanding and evaluating the fuel usage and mobility effects of having a higher number of autonomous vehicles (AVs) in traffic under realistic mixed traffic conditions including both autonomous and non-autonomous vehicles. In this paper, L4-L5 AVs with varying penetration rates in total traffic flow were simulated using the microscopic traffic simulator Vissim on urban, mixed and freeway roadways. The roadways used in these simulations were replicas of real roadways in and around Columbus, Ohio, including an AV shuttle routes in operation. The road-specific information regarding each roadway, such as the number of traffic lights and positions, number of STOP signs and positions, and speed limits, were gathered using OpenStreetMap with SUMO. In simulating L4-L5 AVs, the All-Knowing CoEXist AV and a vehicle with Wiedemann 74 driver were taken to represent AV and non-AV driving, respectively. Then, the driving behaviors, such as headway time and car following, desired acceleration and deceleration profiles of AV, and non-AV car following and lane change models were modified. The effect of having varying penetration rates of L4-L5 AVs were then evaluated using criteria such as average fuel consumption, existence of queues and their average/maximum length, total number of vehicles in the simulation, average delay experience by all vehicles, total number of stops experienced by all vehicles, and total emission of CO, NOx and volatile organic compounds (VOC) from the vehicles in the simulation. The results show that while increasing penetration rates of L4-L5 AVs generally improve overall fuel efficiency and mobility of the traffic network, there were also cases when the opposite trend was observed.
Frequency-dependent Switching Control for Disturbance Attenuation of Linear Systems
Authors: Jingjing Zhang, Jan Heiland, Peter Benner, Xin Du
Date: 2023-06-01 00:00:00
Tasks: LEMMA

The generalized Kalman-Yakubovich-Popov lemma as established by Iwasaki and Hara in 2005 marks a milestone in the analysis and synthesis of linear systems from a finite-frequency perspective. Given a pre-specified frequency band, it allows us to produce passive controllers with excellent in-band disturbance attenuation performance at the expense of some of the out-of-band performance. This paper focuses on control design of linear systems in the presence of disturbances with non-strictly or non-stationary limited frequency spectrum. We first propose a class of frequency-dependent excited energy functions (FD-EEF) as well as frequency-dependent excited power functions (FD-EPF), which possess a desirable frequency-selectiveness property with regard to the in-band and out-of-band excited energy as well as excited power of the system. Based upon a group of frequency-selective passive controllers, we then develop a frequency-dependent switching control (FDSC) scheme that selects the most appropriate controller at runtime. We show that our FDSC scheme is capable to approximate the solid in-band performance while maintaining acceptable out-of-band performance with regard to global time horizons as well as localized time horizons. The method is illustrated by a commonly used benchmark model.
Data-driven modeling and parameter estimation of Nonlinear systems
Authors: Kaushal Kumar
Date: 2023-05-31 00:00:00
Tasks: parameter estimation

Nonlinear systems play a significant role in numerous scientific and engineering disciplines, and comprehending their behavior is crucial for the development of effective control and prediction strategies. This paper introduces a novel data-driven approach for accurately modeling and estimating parameters of nonlinear systems utilizing trust region optimization. The proposed method is applied to three well-known systems: the Van der Pol oscillator, the Damped oscillator, and the Lorenz system, which find broad applications in engineering, physics, and biology. The results demonstrate the efficacy of the approach in accurately identifying the parameters of these nonlinear systems, enabling a reliable characterization of their behavior. Particularly in chaotic systems like the Lorenz system, capturing the dynamics on the attractor proves to be crucial. Overall, this article presents a robust data-driven approach for parameter estimation in nonlinear dynamical systems, holding promising potential for real-world applications.
Number of attractors in the critical Kauffman model is exponential
Authors: T. M. A. Fink, F. C. Sheldon
Date: 2023-06-02 00:00:00

The Kauffman model is the archetypal model of genetic computation. It highlights the importance of criticality, at which many biological systems seem poised. In a series of advances, researchers have honed in on how the number of attractors in the critical regime grows with network size. But a definitive answer has proved elusive. We prove that, for the critical Kauffman model with connectivity one, the number of attractors grows at least, and at most, as $(2/\!\sqrt{e})^N$. This is the first proof that the number of attractors in a critical Kauffman model grows exponentially.
Dynamical Theory for Adaptive Systems
Authors: Tuan Minh Pham, Kunihiko Kaneko
Date: 2023-06-02 00:00:00

The study of adaptive dynamics, involving many degrees of freedom on two separated timescales, one for fast changes of state variables and another for the slow adaptation of parameters controlling the former's dynamics is crucial for understanding feedback mechanisms underlying evolution and learning. We present a path-integral approach \`a la Martin-Siggia-Rose-De Dominicis-Janssen (MSRDJ) to analyse nonequilibrium phase transitions in such dynamical systems. As an illustration, we apply our framework to the adaptation of gene-regulatory networks under a dynamic genotype-phenotype map: phenotypic variations are shaped by the fast stochastic gene-expression dynamics and are coupled to the slowly evolving distribution of genotypes, each encoded by a network structure. We establish that under this map, genotypes corresponding to reciprocal networks of coherent feedback loops are selected within an intermediate range of environmental noise, leading to phenotypic robustness.
Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model
Authors: Wenqian Chen, Yucheng Fu, Panos Stinis
Date: 2023-05-31 00:00:00
Tasks: Physics-informed machine learning

In this paper, we present a physics-informed neural network (PINN) approach for predicting the performance of an all-vanadium redox flow battery, with its physics constraints enforced by a two-dimensional (2D) mathematical model. The 2D model, which includes 6 governing equations and 24 boundary conditions, provides a detailed representation of the electrochemical reactions, mass transport and hydrodynamics occurring inside the redox flow battery. To solve the 2D model with the PINN approach, a composite neural network is employed to approximate species concentration and potentials; the input and output are normalized according to prior knowledge of the battery system; the governing equations and boundary conditions are first scaled to an order of magnitude around 1, and then further balanced with a self-weighting method. Our numerical results show that the PINN is able to predict cell voltage correctly, but the prediction of potentials shows a constant-like shift. To fix the shift, the PINN is enhanced by further constrains derived from the current collector boundary. Finally, we show that the enhanced PINN can be even further improved if a small number of labeled data is available.