pwc-archive/papers-with-abstracts huggingface dataset:
CosyAudio: Improving Audio Generation with Confidence Scores and Synthetic Captions
Authors: Xinfa Zhu, Wenjie Tian, Xinsheng Wang, Lei He, Xi Wang, Sheng Zhao, Lei Xie
Date: 2025-01-28 00:00:00
Tasks: Audio captioning, Audio Generation

Text-to-Audio (TTA) generation is an emerging area within AI-generated content (AIGC), where audio is created from natural language descriptions. Despite growing interest, developing robust TTA models remains challenging due to the scarcity of well-labeled datasets and the prevalence of noisy or inaccurate captions in large-scale, weakly labeled corpora. To address these challenges, we propose CosyAudio, a novel framework that utilizes confidence scores and synthetic captions to enhance the quality of audio generation. CosyAudio consists of two core components: AudioCapTeller and an audio generator. AudioCapTeller generates synthetic captions for audio and provides confidence scores to evaluate their accuracy. The audio generator uses these synthetic captions and confidence scores to enable quality-aware audio generation. Additionally, we introduce a self-evolving training strategy that iteratively optimizes CosyAudio across both well-labeled and weakly-labeled datasets. Initially trained with well-labeled data, AudioCapTeller leverages its assessment capabilities on weakly-labeled datasets for high-quality filtering and reinforcement learning, which further improves its performance. The well-trained AudioCapTeller refines corpora by generating new captions and confidence scores, serving for the audio generator training. Extensive experiments on open-source datasets demonstrate that CosyAudio outperforms existing models in automated audio captioning, generates more faithful audio, and exhibits strong generalization across diverse scenarios.
Neural Kalman Filters for Acoustic Echo Cancellation
Authors: Ernst Seidel, Gerald Enzner, Pejman Mowlaee, Tim Fingscheidt
Date: 2025-01-22 00:00:00
Tasks: Acoustic echo cancellation

Kalman filtering is a powerful approach to adaptive filtering for various problems in signal processing. The frequency-domain adaptive Kalman filter (FDKF), based on the concept of the acoustic state space, provides a unifying solution to the adaptive filter update and the related stepsize control. It was conceived for the problem of acoustic echo cancellation and, as such, is frequently applied in hands-free systems. This article motivates and briefly recapitulates the linear FDKF and investigates how it can be further supported by deep neural networks (DNNs) in various ways, specifically to overcome the challenges and limitations related to the usually required estimation of process and observation noise covariances for the Kalman filter. While the mere FDKF comes with very low computational complexity, its neural Kalman filter variants may deliver faster (re)convergence, better echo cancellation, and even exceed the FDKF in its excellent double-talk near-end speech preservation both under linear and nonlinear loudspeaker conditions. To provide a synopsis of the state of the art, this article contributes a comparison of a range of DNN-based extensions of FDKF in the same training framework and using the same data.
WhiSPA: Semantically and Psychologically Aligned Whisper with Self-Supervised Contrastive and Student-Teacher Learning
Authors: Rajath Rao, Adithya Ganesan, Oscar Kjell, Jonah Luby, Akshay Raghavan, Scott Feltman, Whitney Ringwald, Ryan L. Boyd, Benjamin Luft, Camilo Ruggero, Neville Ryant, Roman Kotov, H. Andrew Schwartz
Date: 2025-01-15 00:00:00
Tasks: cross-modal alignment, Language Modeling, Language Modelling, Speech-to-Text

Current speech encoding pipelines often rely on an additional text-based LM to get robust representations of human communication, even though SotA speech-to-text models often have a LM within. This work proposes an approach to improve the LM within an audio model such that the subsequent text-LM is unnecessary. We introduce WhiSPA (Whisper with Semantic and Psychological Alignment), which leverages a novel audio training objective: contrastive loss with a language model embedding as a teacher. Using over 500k speech segments from mental health audio interviews, we evaluate the utility of aligning Whisper's latent space with semantic representations from a text autoencoder (SBERT) and lexically derived embeddings of basic psychological dimensions: emotion and personality. Over self-supervised affective tasks and downstream psychological tasks, WhiSPA surpasses current speech encoders, achieving an average error reduction of 73.4% and 83.8%, respectively. WhiSPA demonstrates that it is not always necessary to run a subsequent text LM on speech-to-text output in order to get a rich psychological representation of human communication.
Developing Enhanced Conversational Agents for Social Virtual Worlds
Authors: D. Griol, A. Sanchis, J. M. Molina, Z. Callejas
Date: 2025-01-14 00:00:00

In this paper, we present a methodology for the development of embodied conversational agents for social virtual worlds. The agents provide multimodal communication with their users in which speech interaction is included. Our proposal combines different techniques related to Artificial Intelligence, Natural Language Processing, Affective Computing, and User Modeling. Firstly, the developed conversational agents. A statistical methodology has been developed to model the system conversational behavior, which is learned from an initial corpus and improved with the knowledge acquired from the successive interactions. In addition, the selection of the next system response is adapted considering information stored into users profiles and also the emotional contents detected in the users utterances. Our proposal has been evaluated with the successful development of an embodied conversational agent which has been placed in the Second Life social virtual world. The avatar includes the different models and interacts with the users who inhabit the virtual world in order to provide academic information. The experimental results show that the agents conversational behavior adapts successfully to the specific characteristics of users interacting in such environments.
Methods to Increase the Amount of Data for Speech Recognition for Low Resource Languages
Authors: Alexan Ayrapetyan, Sofia Kostandian, Ara Yeroyan, Mher Yerznkanyan, Nikolay Karpov, Nune Tadevosyan, Vitaly Lavrukhin, Boris Ginsburg
Date: 2025-01-08 00:00:00
Tasks: speech-recognition, Speech Recognition

This study explores methods to increase data volume for low-resource languages using techniques such as crowdsourcing, pseudo-labeling, advanced data preprocessing and various permissive data sources such as audiobooks, Common Voice, YouTube. While these methods are well-explored for highresource languages, their application for low-resource languages remains underexplored. Using Armenian and Georgian as case studies, we demonstrate how linguistic and resource-specific characteristics influence the success of these methods. This work provides practical guidance for researchers to choose cost-effective and quality-driven dataset extension strategies for low-resource languages. The key takeaway from various data extension approaches is that paid crowd-sourcing offers the best balance between cost and quality, outperforming volunteer crowd-sourcing, open-source audiobooks, and unlabeled data usage. Ablation study shows that models trained on the expanded datasets outperform existing baselines and achieve 5.73% for Gergian and 9.9% for Armenian ASR word error rate using a relatively small FastConformer architecture. We open-sourced both the Armenian and Georgian models to allow further research and practical applications.
Leveraging Cross-Attention Transformer and Multi-Feature Fusion for Cross-Linguistic Speech Emotion Recognition
Authors: Ruoyu Zhao, Xiantao Jiang, F. Richard Yu, Victor C. M. Leung, Tao Wang, Shaohu Zhang
Date: 2025-01-06 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition, Transfer Learning

Speech Emotion Recognition (SER) plays a crucial role in enhancing human-computer interaction. Cross-Linguistic SER (CLSER) has been a challenging research problem due to significant variability in linguistic and acoustic features of different languages. In this study, we propose a novel approach HuMP-CAT, which combines HuBERT, MFCC, and prosodic characteristics. These features are fused using a cross-attention transformer (CAT) mechanism during feature extraction. Transfer learning is applied to gain from a source emotional speech dataset to the target corpus for emotion recognition. We use IEMOCAP as the source dataset to train the source model and evaluate the proposed method on seven datasets in five languages (e.g., English, German, Spanish, Italian, and Chinese). We show that, by fine-tuning the source model with a small portion of speech from the target datasets, HuMP-CAT achieves an average accuracy of 78.75% across the seven datasets, with notable performance of 88.69% on EMODB (German language) and 79.48% on EMOVO (Italian language). Our extensive evaluation demonstrates that HuMP-CAT outperforms existing methods across multiple target languages.
QualityFlow: An Agentic Workflow for Program Synthesis Controlled by LLM Quality Checks
Authors: Yaojie Hu, Qiang Zhou, Qihong Chen, Xiaopeng Li, Linbo Liu, Dejiao Zhang, Amit Kachroo, Talha Oz, Omer Tripp
Date: 2025-01-20 00:00:00
Tasks: Code Generation, HumanEval, Language Modeling, Language Modelling, Large Language Model, mbpp, Program Synthesis

We introduce QualityFlow, a dynamic agentic workflow for program synthesis. Given the English description of a programming problem and a set of unit tests, the model's goal is to synthesize the correct program that solves the problem and passes the tests. QualityFlow includes large language model (LLM) agents resembling a software development team, including code generation, testing, and self-debugging. We propose the LLM Quality Checker, which explicitly "imagines" whether the synthesized programs' execution would conform to the unit tests. The Quality Checks dynamically control the workflow, including actions to submit the final answer, clarify the problem statement, and revert previous workflow steps. Our experiments show that the Quality Checker can precisely accept any correct program, mitigate faulty synthesized tests, and prevent potential workflow deviation. QualityFlow establishes the state-of-the-art results on four program synthesis benchmarks: MBPP, HumanEval, and stricter evaluations from MBPP-EvalPlus and HumanEval-EvalPlus.
Replacing the Gallium Oxide Shell with Conductive Ag: Toward a Printable and Recyclable Composite for Highly Stretchable Electronics, Electromagnetic Shielding, and Thermal Interfaces
Authors: Abdollah Hajalilou, Elahe Parvini, Tiago A. Morgado, Pedro Alhais Lopes, M. Estrela Melo Jorge, Marta Freitas, Mahmoud Tavakoli
Date: 2025-01-29 00:00:00
Tasks: Blocking

Liquid metal (LM)-based composites hold promise for soft electronics due to their high conductivity and fluidic nature. However, the presence of {\alpha}_Ga2O3 and GaOOH layers around LM droplets impairs conductivity and performance. We tackle this issue by replacing the oxide layer with conductive silver (Ag) using an ultrasonic_assisted galvanic replacement reaction. The Ag_coated nanoparticles form aggregated, porous microparticles that are mixed with styrene_isoprene_styrene (SIS) polymers, resulting in a digitally printable composite with superior electrical conductivity and electromechanical properties compared to conventional fillers. Adding more LM enhances these properties further. The composite achieves EMI shielding effectiveness (SE) exceeding 75 dB in the X_band frequency range, even at 200 per cent strain, meeting stringent military and medical standards. It is applicable in wireless communications and Bluetooth signal blocking and as a thermal interface material (TIM). Additionally, we highlight its recyclability using a biodegradable solvent, underscoring its eco_friendly potential. This composite represents a significant advancement in stretchable electronics and EMI shielding, with implications for wearable and bioelectronic applications.
Recyclable Thin-Film Soft Electronics for Smart Packaging and E-Skins
Authors: Manuel Reis Carneiro, Anibal T. de Almeida, Mahmoud Tavakoli, Carmel Majidi
Date: 2025-01-29 00:00:00

Despite advances in soft, sticker_like electronics, few efforts have dealt with the challenge of electronic waste. Here, this is addressed by introducing an eco friendly conductive ink for thin_film circuitry composed of silver flakes and a water_based polyurethane dispersion. This ink uniquely combines high electrical conductivity (1.6 x 105 S m_1), high resolution digital printability, robust adhesion for microchip integration, mechanical resilience, and recyclability. Recycling is achieved with an ecologically friendly processing method to decompose the circuits into constituent elements and recover the conductive ink with a decrease of only 2.4 per cent in conductivity. Moreover, adding liquid metal enables stretchability of up to 200 per cent strain, although this introduces the need for more complex recycling steps. Finally, on_skin electrophysiological monitoring biostickers along with a recyclable smart package with integrated sensors for monitoring safe storage of perishable foods are demonstrated.
Analysis and Control of Perturbed Density Systems
Authors: Igor Furtat
Date: 2025-01-29 00:00:00

The paper investigates dynamical systems for which the derivative of some positive-definite function along the solutions of this system depends on so-called density function. In turn, such dynamical systems are called density systems. The density function sets the density of the space, where the system is evolved, and affects the behaviour of the original system. For example, this function can define (un)stable regions and forbidden regions where there are no system solutions. The density function can be used in the design of new adaptive control laws with the formulation of appropriate new control goals, e.g., stabilization in given bounded or semi-bounded sets. To design a novel adaptive control law that ensures the system outputs in a given set, systems with known and unknown parameters under disturbances are considered. All theoretical results and conclusions are illustrated by numerical simulations.
Physics-Informed Neural Networks in Power System Dynamics: Improving Simulation Accuracy
Authors: Ignasi Ventura Nadal, Rahul Nellikkath, Spyros Chatzivasileiadis
Date: 2025-01-29 00:00:00

The importance and cost of time-domain simulations when studying power systems have exponentially increased in the last decades. With the growing share of renewable energy sources, the slow and predictable responses from large turbines are replaced by the fast and unpredictable dynamics from power electronics. The current existing simulation tools require new solutions designed for faster dynamics. Physics-Informed Neural Networks (PINNs) have recently emerged in power systems to accelerate such simulations. By incorporating knowledge during the up-front training, PINNs provide more accurate results over larger time steps than traditional numerical methods. This paper introduces PINNs as an alternative approximation method that seamlessly integrates with the current simulation framework. We replace a synchronous machine for a trained PINN in the IEEE 9-, 14-, and 30-bus systems and simulate several network disturbances. Including PINNs systematically boosts the simulations' accuracy, providing more accurate results for both the PINN-modeled component and the whole multi-machine system states.
Economic Nonlinear Model Predictive Control of Prosumer District Heating Networks: The Extended Version
Authors: Max Sibeijn, Saeed Ahmed, Mohammad Khosravi, Tamás Keviczky
Date: 2025-01-29 00:00:00
Tasks: energy management, Model Predictive Control

In this paper, we propose an economic nonlinear model predictive control (MPC) algorithm for district heating networks (DHNs). The proposed method features prosumers, multiple producers, and storage systems, which are essential components of 4th generation DHNs. These networks are characterized by their ability to optimize their operations, aiming to reduce supply temperatures, accommodate distributed heat sources, and leverage the flexibility provided by thermal inertia and storage, all crucial for achieving a fossil-fuel-free energy supply. Developing a smart energy management system to accomplish these goals requires detailed models of highly complex nonlinear systems and computational algorithms able to handle large-scale optimization problems. To address this, we introduce a graph-based optimization-oriented model that efficiently integrates distributed producers, prosumers, storage buffers, and bidirectional pipe flows, such that it can be implemented in a real-time MPC setting. Furthermore, we conduct several numerical experiments to evaluate the performance of the proposed algorithms in closed-loop. Our findings demonstrate that the MPC methods achieved up to 9% cost improvement over traditional rule-based controllers while better maintaining system constraints.
Efficient Calculation of Stabilization Parameters in RF Power Amplifiers
Authors: Libe Mori, Ibone Lizarraga, Aitziber Anakabe, Juan-Mari Collantes, Vincent Armengaud, Geoffroy Soubercaze-Pun
Date: 2025-01-29 00:00:00

This paper proposes an efficient method for the calculation of the stabilization parameters in RF power amplifiers operating in periodic large-signal regimes. Stabilization is achieved by applying the principles of linear control theory for Periodic Linear Time-Varying (PLTV) systems. A numerical method is proposed to obtain the Harmonic Transfer Function that represents the system linearized around the large-signal steady state. Then, a feedback analysis is performed to calculate the closed-loop poles of the PLTV system. The proposed approach is demonstrated with two examples. Firstly, a three-stage amplifier that exhibits a low-frequency oscillation for increasing values of input power is correctly stabilized. Next, the stabilization of an unstable design that exhibits an odd-mode parametric oscillation is presented. The results of the proposed technique are compared to those obtained with the conventional parametric stability simulation. These examples serve to illustrate the capability and efficiency of the proposed approach.
Pole-Zero Identification: Unveiling the Critical Dynamics of Microwave Circuits Beyond Stability Analysis
Authors: Juan-Mari Collantes, Libe Mori, Aitziber Anakabe, Nerea Otegi, Natanael Ayllon, Franco Ramirez, Vincent Armengaud, Geoffroy Soubercaze-Pun
Date: 2025-01-29 00:00:00

Pole-zero identification refers to the obtaining of the poles and zeros of a linear (or linearized) system described by its frequency response. This is usually done using optimization techniques (such as least squares, maximum likelihood estimation, or vector fitting) that fit a given frequency response of the linear system to a transfer function defined as the ratio of two polynomials. This kind of linear system identification in the frequency domain has numerous applications in a wide variety of engineering fields (such as mechanical systems, power systems and Electromagnetic Compatibility). In the microwave domain, rational approximation is increasingly used to obtain black-box models of complex passive structures for model order reduction and efficient transient simulation. In this paper we will focus on a different application of pole-zero identification. We will review the different ways in which pole-zero identification can be applied to nonlinear circuit design (for power amplifier stability analysis and beyond). We will give a comprehensive view on recent approaches through illustrative application examples. Other uses of rational approximation techniques are beyond the scope of this paper.
Accelerated DC loadflow solver for topology optimization
Authors: Nico Westerbeck, Joost van Dijk, Jan Viebahn, Christian Merz, Dirk Witthaut
Date: 2025-01-29 00:00:00
Tasks: Diversity

We present a massively parallel solver that accelerates DC loadflow computations for power grid topology optimization tasks. Our approach leverages low-rank updates of the Power Transfer Distribution Factors (PTDFs) to represent substation splits, line outages, and reconfigurations without ever refactorizing the system. Furthermore, we implement the core routines on Graphics Processing Units (GPUs), thereby exploiting their high-throughput architecture for linear algebra. A two-level decomposition separates changes in branch topology from changes in nodal injections, enabling additional speed-ups by an in-the-loop brute force search over injection variations at minimal additional cost. We demonstrate billion-loadflow-per-second performance on power grids of varying sizes in workload settings which are typical for gradient-free topology optimization such as Reinforcement Learning or Quality Diversity methods. While adopting the DC approximation sacrifices some accuracy and prohibits the computation of voltage magnitudes, we show that this sacrifice unlocks new scales of computational feasibility, offering a powerful tool for large-scale grid planning and operational topology optimization.
A Sampling Complexity-aware Framework for Discrete-time Fractional-Order Dynamical System Identification
Authors: Xiaole Zhang, Vijay Gupta, Paul Bogdan
Date: 2025-01-29 00:00:00
Tasks: parameter estimation

A variety of complex biological, natural and man-made systems exhibit non-Markovian dynamics that can be modeled through fractional order differential equations, yet, we lack sample comlexity aware system identification strategies. Towards this end, we propose an affine discrete-time fractional order dynamical system (FoDS) identification algorithm and provide a detailed sample complexity analysis. The algorithm effectively addresses the challenges of FoDS identification in the presence of noisy data. The proposed algorithm consists of two key steps. Firstly, it avoids solving higher-order polynomial equations, which would otherwise result in multiple potential solutions for the fractional orders. Secondly, the identification problem is reformulated as a least squares estimation, allowing us to infer the system parameters. We derive the expectation and probabilistic bounds for the FoDS parameter estimation error, assuming prior knowledge of the functions \( f \) and \( g \) in the FoDS model. The error decays at a rate of \( N = O\left( \frac{d}{\epsilon} \right) \), where \( N \) is the number of samples, \( d \) is the dimension of the state variable, and \( \epsilon \) represents the desired estimation accuracy. Simulation results demonstrate that our theoretical bounds are tight, validating the accuracy and robustness of this algorithm.