pwc-archive/papers-with-abstracts huggingface dataset:
Towards Bi-Hemispheric Emotion Mapping through EEG: A Dual-Stream Neural Network Approach
Authors: David Freire-Obregón, Daniel Hernández-Sosa, Oliverio J. Santana, Javier Lorenzo-Navarro, Modesto Castrillón-Santana
Date: 2024-04-06 00:00:00
Tasks: EEG, Emotion Classification, Emotion Recognition

Emotion classification through EEG signals plays a significant role in psychology, neuroscience, and human-computer interaction. This paper addresses the challenge of mapping human emotions using EEG data in the Mapping Human Emotions through EEG Signals FG24 competition. Subjects mimic the facial expressions of an avatar, displaying fear, joy, anger, sadness, disgust, and surprise in a VR setting. EEG data is captured using a multi-channel sensor system to discern brain activity patterns. We propose a novel two-stream neural network employing a Bi-Hemispheric approach for emotion inference, surpassing baseline methods and enhancing emotion recognition accuracy. Additionally, we conduct a temporal analysis revealing that specific signal intervals at the beginning and end of the emotion stimulus sequence contribute significantly to improve accuracy. Leveraging insights gained from this temporal analysis, our approach offers enhanced performance in capturing subtle variations in the states of emotions.
Asymptotic analysis of sum-rate under SIC
Authors: Andrea Baiocchi, Asmad Razzaque
Date: 2024-05-21 00:00:00

Limitation of the cost of coordination and contention among a large number of nodes calls for grant-free approaches, exploiting physical layer techniques to solve collisions. Successive Interference Cancellation (SIC) is becoming a key building block of multiple access channel receiver, in an effort to support massive Internet of Things (IoT). In this paper, we explore the large-scale performance of SIC in a theoretical framework. A general model of a SIC receiver is stated for a shared channel with $n$ transmitters. The asymptotic sum-rate performance is characterized as $n \rightarrow \infty$, for a suitably scaled target Signal to Noise Interference Ratio (SNIR). The probability distribution of the number of correctly decoded packets is shown to tend to a deterministic distribution asymptotically for large values of $n$. The asymptotic analysis is carried out for any probability distribution of the wireless channel gain, assuming that the average received power level is same for all nodes, through power control.
Underdetermined DOA Estimation of Off-Grid Sources Based on the Generalized Double Pareto Prior
Authors: Yongfeng Huang, Zhendong Chen, Kun Ye, Lang Zhou, Haixin Sun
Date: 2024-04-18 00:00:00
Tasks: Bayesian Inference

In this letter, we investigate a new generalized double Pareto based on off-grid sparse Bayesian learning (GDPOGSBL) approach to improve the performance of direction of arrival (DOA) estimation in underdetermined scenarios. The method aims to enhance the sparsity of source signal by utilizing the generalized double Pareto (GDP) prior. Firstly, we employ a first-order linear Taylor expansion to model the real array manifold matrix, and Bayesian inference is utilized to calculate the off-grid error, which mitigates the grid dictionary mismatch problem in underdetermined scenarios. Secondly, an innovative grid refinement method is introduced, treating grid points as iterative parameters to minimize the modeling error between the source and grid points. The numerical simulation results verify the superiority of the proposed strategy, especially when dealing with a coarse grid and few snapshots.
Sparse Regression Codes for Non-coherent SIMO channels
Authors: Sai Dinesh Kancharana, Madhusudan Kumar Sinha, Arun Pachai Kannu
Date: 2024-05-16 00:00:00
Tasks: Decoder, regression

Motivated by hyper-reliable low-latency communication in 6G, we consider error control coding for short block lengths in multi-antenna fading channels. In general, the channel fading coefficients are unknown at both the transmitter and receiver, which is referred to as non-coherent channels. Conventionally, pilot symbols are transmitted to facilitate channel estimation, causing power and bandwidth overhead. Our paper considers sparse regression codes (SPARCs) for non-coherent flat-fading channels without using pilots. We develop a novel greedy decoder for SPARC using maximum likelihood principles, referred to as maximum likelihood matching pursuit (MLMP). MLMP works based on successive combining principles as opposed to conventional greedy algorithms, which are based on successive cancellation. We also obtain the noiseless perfect recovery condition for our successive combining algorithm. In addition, we develop an approximate message passing (AMP) SPARC decoder for the non-coherent flat fading model. Using simulation studies, we show that the MLMP decoder for SPARC outperforms AMP and other greedy decoders. Also, SPARC with MLMP decoder outperforms polar codes employing pilot-based channel estimation and polar codes with non-coherent decoders.
RA: A machine based rational agent, Part 1
Authors: G. Pantelis
Date: 2024-05-21 00:00:00

RA is a software package that couples machine learning with formal reasoning in an attempt to find the laws that generate the empirical data that it has been given access to. A brief outline of RA in its initial stage of development is presented. Particular emphasis is given to current design strategies that aim to endow RA with the ability to construct its own conjectures of which it constructs proofs.
Organizational Selection of Innovation
Authors: Lucas Böttcher, Ronald Klingebiel
Date: 2024-05-16 00:00:00

Budgetary constraints force organizations to pursue only a subset of possible innovation projects. Identifying which subset is most promising is an error-prone exercise, and involving multiple decision makers may be prudent. This raises the question of how to most effectively aggregate their collective nous. Our model of organizational portfolio selection provides some first answers. We show that portfolio performance can vary widely. Delegating evaluation makes sense when organizations employ the relevant experts and can assign projects to them. In most other settings, aggregating the impressions of multiple agents leads to better performance than delegation. In particular, letting agents rank projects often outperforms alternative aggregation rules -- including averaging agents' project scores as well as counting their approval votes -- especially when organizations have tight budgets and can select only a few project alternatives out of many.
Time-Varying Graph Signal Recovery Using High-Order Smoothness and Adaptive Low-rankness
Authors: Weihong Guo, Yifei Lou, Jing Qin, Ming Yan
Date: 2024-05-16 00:00:00

Time-varying graph signal recovery has been widely used in many applications, including climate change, environmental hazard monitoring, and epidemic studies. It is crucial to choose appropriate regularizations to describe the characteristics of the underlying signals, such as the smoothness of the signal over the graph domain and the low-rank structure of the spatial-temporal signal modeled in a matrix form. As one of the most popular options, the graph Laplacian is commonly adopted in designing graph regularizations for reconstructing signals defined on a graph from partially observed data. In this work, we propose a time-varying graph signal recovery method based on the high-order Sobolev smoothness and an error-function weighted nuclear norm regularization to enforce the low-rankness. Two efficient algorithms based on the alternating direction method of multipliers and iterative reweighting are proposed, and convergence of one algorithm is shown in detail. We conduct various numerical experiments on synthetic and real-world data sets to demonstrate the proposed method's effectiveness compared to the state-of-the-art in graph signal recovery.
A Survey of Integrating Wireless Technology into Active Noise Control
Authors: Xiaoyi Shen, Dongyuan Shi, Zhengding Luo, Junwei Ji, Woon-Seng Gan
Date: 2024-05-21 00:00:00
Tasks: Survey

Active Noise Control (ANC) is a widely adopted technology for reducing environmental noise across various scenarios. This paper focuses on enhancing noise reduction performance, particularly through the refinement of signal quality fed into ANC systems. We discuss the main wireless technique integrated into the ANC system, equipped with some innovative algorithms, in diverse environments. Instead of using microphone arrays, which increase the computation complexity of the ANC system, to isolate multiple noise sources to improve noise reduction performance, the application of the wireless technique avoids extra computation demand. Wireless transmissions of reference, error, and control signals are also applied to improve the convergence performance of the ANC system. Furthermore, this paper lists some wireless ANC applications, such as earbuds, headphones, windows, and headrests, underscoring their adaptability and efficiency in various settings.
Ensuring Safety at Intelligent Intersections: Temporal Logic Meets Reachability Analysis
Authors: Kaj Munhoz Arfvidsson, Frank J. Jiang, Karl H. Johansson, Jonas Mårtensson
Date: 2024-05-18 00:00:00

In this work, we propose an approach for ensuring the safety of vehicles passing through an intelligent intersection. There are many proposals for the design of intelligent intersections that introduce central decision-makers to intersections for enhancing the efficiency and safety of the vehicles. To guarantee the safety of such designs, we develop a safety framework for intersections based on temporal logic and reachability analysis. We start by specifying the required behavior for all the vehicles that need to pass through the intersection as linear temporal logic formula. Then, using temporal logic trees, we break down the linear temporal logic specification into a series of Hamilton-Jacobi reachability analyses in an automated fashion. By successfully constructing the temporal logic tree through reachability analysis, we verify the feasibility of the intersection specification. By taking this approach, we enable a safety framework that is able to automatically provide safety guarantees on new intersection behavior specifications. To evaluate our approach, we implement the framework on a simulated T-intersection, where we show that we can check and guarantee the safety of vehicles with potentially conflicting paths.
Mamba in Speech: Towards an Alternative to Self-Attention
Authors: Xiangyu Zhang, Qiquan Zhang, Hexin Liu, Tianyi Xiao, Xinyuan Qian, Beena Ahmed, Eliathamby Ambikairajah, Haizhou Li, Julien Epps
Date: 2024-05-21 00:00:00
Tasks: Mamba, Speech Enhancement, speech-recognition, Speech Recognition, State Space Models

Transformer and its derivatives have achieved success in diverse tasks across computer vision, natural language processing, and speech processing. To reduce the complexity of computations within the multi-head self-attention mechanism in Transformer, Selective State Space Models (i.e., Mamba) were proposed as an alternative. Mamba exhibited its effectiveness in natural language processing and computer vision tasks, but its superiority has rarely been investigated in speech signal processing. This paper explores solutions for applying Mamba to speech processing by discussing two typical speech processing tasks: speech recognition, which requires semantic and sequential information, and speech enhancement, which focuses primarily on sequential patterns. The experimental results confirm that bidirectional Mamba (BiMamba) consistently outperforms vanilla Mamba, highlighting the advantages of a bidirectional design for speech processing. Moreover, experiments demonstrate the effectiveness of BiMamba as an alternative to the self-attention module in the Transformer model and its derivates, particularly for the semantic-aware task. The crucial technologies for transferring Mamba to speech are then summarized in ablation studies and the discussion section, offering insights for extending this research to a broader scope of tasks.
Enhancing the analysis of murine neonatal ultrasonic vocalizations: Development, evaluation, and application of different mathematical models
Authors: Rudolf Herdt, Louisa Kinzel, Johann Georg Maaß, Marvin Walther, Henning Fröhlich, Tim Schubert, Peter Maass, Christian Patrick Schaaf
Date: 2024-05-17 00:00:00

Rodents employ a broad spectrum of ultrasonic vocalizations (USVs) for social communication. As these vocalizations offer valuable insights into affective states, social interactions, and developmental stages of animals, various deep learning approaches have aimed to automate both the quantitative (detection) and qualitative (classification) analysis of USVs. Here, we present the first systematic evaluation of different types of neural networks for USV classification. We assessed various feedforward networks, including a custom-built, fully-connected network and convolutional neural network, different residual neural networks (ResNets), an EfficientNet, and a Vision Transformer (ViT). Paired with a refined, entropy-based detection algorithm (achieving recall of 94.9% and precision of 99.3%), the best architecture (achieving 86.79% accuracy) was integrated into a fully automated pipeline capable of analyzing extensive USV datasets with high reliability. Additionally, users can specify an individual minimum accuracy threshold based on their research needs. In this semi-automated setup, the pipeline selectively classifies calls with high pseudo-probability, leaving the rest for manual inspection. Our study focuses exclusively on neonatal USVs. As part of an ongoing phenotyping study, our pipeline has proven to be a valuable tool for identifying key differences in USVs produced by mice with autism-like behaviors.
Monaural speech enhancement on drone via Adapter based transfer learning
Authors: Xingyu Chen, Hanwen Bi, Wei-Ting Lai, Fei Ma
Date: 2024-05-16 00:00:00
Tasks: Speech Enhancement, Transfer Learning

Monaural Speech enhancement on drones is challenging because the ego-noise from the rotating motors and propellers leads to extremely low signal-to-noise ratios at onboard microphones. Although recent masking-based deep neural network methods excel in monaural speech enhancement, they struggle in the challenging drone noise scenario. Furthermore, existing drone noise datasets are limited, causing models to overfit. Considering the harmonic nature of drone noise, this paper proposes a frequency domain bottleneck adapter to enable transfer learning. Specifically, the adapter's parameters are trained on drone noise while retaining the parameters of the pre-trained Frequency Recurrent Convolutional Recurrent Network (FRCRN) fixed. Evaluation results demonstrate the proposed method can effectively enhance speech quality. Moreover, it is a more efficient alternative to fine-tuning models for various drone types, which typically requires substantial computational resources.
Data-Efficient Low-Complexity Acoustic Scene Classification in the DCASE 2024 Challenge
Authors: Florian Schmid, Paul Primus, Toni Heittola, Annamaria Mesaros, Irene Martín-Morató, Khaled Koutini, Gerhard Widmer
Date: 2024-05-16 00:00:00
Tasks: Acoustic Scene Classification, Scene Classification

This article describes the Data-Efficient Low-Complexity Acoustic Scene Classification Task in the DCASE 2024 Challenge and the corresponding baseline system. The task setup is a continuation of previous editions (2022 and 2023), which focused on recording device mismatches and low-complexity constraints. This year's edition introduces an additional real-world problem: participants must develop data-efficient systems for five scenarios, which progressively limit the available training data. The provided baseline system is based on an efficient, factorized CNN architecture constructed from inverted residual blocks and uses Freq-MixStyle to tackle the device mismatch problem. The task received 37 submissions from 17 teams, with the large majority of systems outperforming the baseline. The top-ranked system's accuracy ranges from 54.3% on the smallest to 61.8% on the largest subset, corresponding to relative improvements of approximately 23% and 9% over the baseline system on the evaluation set.
Evaluating Text-to-Speech Synthesis from a Large Discrete Token-based Speech Language Model
Authors: Siyang Wang, Éva Székely
Date: 2024-05-16 00:00:00
Tasks: Hallucination, Language Modeling, Language Modelling, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Recent advances in generative language modeling applied to discrete speech tokens presented a new avenue for text-to-speech (TTS) synthesis. These speech language models (SLMs), similarly to their textual counterparts, are scalable, probabilistic, and context-aware. While they can produce diverse and natural outputs, they sometimes face issues such as unintelligibility and the inclusion of non-speech noises or hallucination. As the adoption of this innovative paradigm in speech synthesis increases, there is a clear need for an in-depth evaluation of its capabilities and limitations. In this paper, we evaluate TTS from a discrete token-based SLM, through both automatic metrics and listening tests. We examine five key dimensions: speaking style, intelligibility, speaker consistency, prosodic variation, spontaneous behaviour. Our results highlight the model's strength in generating varied prosody and spontaneous outputs. It is also rated higher in naturalness and context appropriateness in listening tests compared to a conventional TTS. However, the model's performance in intelligibility and speaker consistency lags behind traditional TTS. Additionally, we show that increasing the scale of SLMs offers a modest boost in robustness. Our findings aim to serve as a benchmark for future advancements in generative SLMs for speech synthesis.
Modeling for Non-exponential Production Systems Using Parts Flow Data: Model Parameter Estimation and Performance Analysis
Authors: Yuting Sun, Liang Zhang
Date: 2024-05-21 00:00:00
Tasks: parameter estimation

Mathematical modeling of production systems is the foundation of all model-based approaches for production system analysis, design, improvement, and control. To construct such a model for the stochastic process of the production system more efficiently, a new modeling approach has been proposed that reversely identifies the model parameters using system performance metrics (e.g., production rate, work-in-process, etc.) derived from the parts flow data. This paper extends this performance metrics-based modeling approach to non-exponential serial production lines. Since no analytical expressions of performance metrics are available for non-exponential systems, we use neural network surrogate models to calculate those performance metrics as functions in terms of the system parameters. Then, based on the surrogate models and given performance metrics, the machine parameters are estimated by solving a constrained optimization problem that minimizes the mean square error of the performance metrics resulting from the estimated parameters compared to the true ones. With the designed multi-start particle swarm optimization algorithm, we find that multiple non-unique combinations of machine parameters can lead to practically the same system performance metrics and a linear relationship of the reliability parameters from these obtained estimations is observed. Besides, model sensitivity analysis is implemented to verify the robustness of the different combinations of machine parameters even under the potential improvement scenarios.
Spatial Mode Multiplexing for Fiber-Coupled IM/DD Optical Wireless Links with Misalignment
Authors: Jinzhe Che, Shenjie Huang, Majid Safari
Date: 2024-05-21 00:00:00

Optical wireless communication (OWC) emerges as a pivotal solution for achieving terabit-level aggregate throughput in next-generation wireless networks. With the mature high-speed transceivers and advanced (de)multiplexing techniques designed for fiber optics, fiber-coupled OWC can be seamlessly integrated into existing ultra-high-speed networks such as data centres. In particular, OWC leveraging spatial mode multiplexing (SMM) and few-mode fiber (FMF) coupling can significantly increase capacity, though misalignment may reduce performance. This paper presents a thorough investigation into the SMM-enabled FMF coupling OWC systems affected by link misalignment, specifically focusing on systems with intensity modulation with direct detection (IM/DD) receivers. A theoretical analysis is conducted to assess the fiber coupling efficiency of the considered system in the presence of both pointing error and angle of arrival (AOA) fluctuations caused by random device vibrations. Our model elucidates the dependence of coupling efficiency to the order of the incident modes, highlighting the critical role of beam properties in system performance. To mitigate the intermodal crosstalk arising from link misalignment, we employ zero-forcing beamforming (ZFBF) to enhance the overall aggregated data rate. Through extensive numerical results, we identify optimal system configurations encompassing aperture design and mode selection, leading to a capacity boost exceeding 200%.
A Local Gaussian Process Regression Approach to Frequency Response Function Estimation
Authors: Xiaozhu Fang, Yu Xu, Tianshi Chen
Date: 2024-05-21 00:00:00
Tasks: regression

Frequency response function (FRF) estimation is a classical subject in system identification. In the past two decades, there have been remarkable advances in developing local methods for this subject, e.g., the local polynomial method, local rational method, and iterative local rational method. The recent concentrations for local methods are two issues: the model order selection and the identification of lightly damped systems. To address these two issues, we propose a new local method called local Gaussian process regression (LGPR). We show that the frequency response function locally is either analytic or resonant, and this prior knowledge can be embedded into a kernel-based regularized estimate through a dot-product kernel plus a resonance kernel induced by a second-order resonant system. The LGPR provides a new route to tackle the aforementioned issues. In the numerical simulations, the LGPR shows the best FRF estimation accuracy compared with the existing local methods, and moreover, the LGPR is more robust with respect to sample size and noise level.
An Improved Robust Total Logistic Distance Metric algorithm for Generalized Gaussian Noise and Noisy Input
Authors: Haiquan Zhao, Yi Peng, Zian Cao
Date: 2024-05-21 00:00:00

Although the known maximum total generalized correntropy (MTGC) and generalized maximum blakezisserman total correntropy (GMBZTC) algorithms can maintain good performance under the errors-in-variables (EIV) model disrupted by generalized Gaussian noise, their requirement for manual ad-justment of parameters is excessive, greatly increasing the practical difficulty of use. To solve this problem, the total arctangent based on logical distance metric (TACLDM) algo-rithm is proposed by utilizing the advantage of few parameters in logical distance metric (LDM) theory and the convergence behavior is improved by the arctangent function. Compared with other competing algorithms, the TACLDM algorithm not only has fewer parameters, but also has better robustness to generalized Gaussian noise and significantly reduces the steady-state error. Furthermore, the analysis of the algorithm in the generalized Gaussian noise environment is analyzed in detail in this paper. Finally, computer simulations demonstrate the outstanding performance of the TACLDM algorithm and the rigorous theoretical deduction in this paper.