pwc-archive/papers-with-abstracts huggingface dataset:
Green antitrust conundrum: Collusion with social goals
Authors: Nigar Hashimzade, Limor Hatsor, Artyom Jelnov
Date: 2024-11-09 00:00:00

Recent antitrust regulations in several countries have granted exemptions for collusion aimed at achieving environmental goals. Firms can apply for exemptions if collusion helps to develop or to implement costly clean technology, particularly in sectors like renewable energy, where capital costs are high and economies of scale are significant. However, if the cost of the green transition is unknown to the competition regulator, firms might exploit the exemption by fixing prices higher than necessary. The regulator faces the decision of whether to permit collusion and whether to commission an investigation of potential price fixing, which incurs costs. We fully characterise the equilibria in this scenario that depend on the regulator's belief about the high cost of green transition. If the belief is high enough, collusion will be allowed. We also identify conditions under which a regulator's commitment to always investigate price fixing is preferable to making discretionary decisions.
Study on Inter and Intra Speaker Variability in Speaker Recognition
Authors: Anton Okhotnikov, Nikita Torgashov, Ivan Yakovlev, Pavel Malov, Rostislav Makarov
Date: 2024-11-12 00:00:00
Tasks: Diversity, Speaker Recognition, Text-Independent Speaker Recognition

Optimization of a trade-off between the number of speakers and their temporal variability (or session diversity) is crucial for the development of a speaker recognition system together with making the data collection process feasible from a time perspective. In this article, we provide the analysis of dependency between inter and intra speaker variability in training data for the modern neural network-based speaker recognition system using the VoxTube dataset for text-independent speaker recognition task. Besides, an auxiliary contribution of this work is a release of upload date metadata per utterance in a VoxTube dataset. We want this article to contribute to guidelines and best practices for collecting and filtering data from media hosting platforms to facilitate the efforts of researchers in developing speaker recognition systems.
Debatts: Zero-Shot Debating Text-to-Speech Synthesis
Authors: Yiqiao Huang, Yuancheng Wang, Jiaqi Li, Haotian Guo, Haorui He, Shunsi Zhang, Zhizheng Wu
Date: 2024-11-10 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

In debating, rebuttal is one of the most critical stages, where a speaker addresses the arguments presented by the opposing side. During this process, the speaker synthesizes their own persuasive articulation given the context from the opposing side. This work proposes a novel zero-shot text-to-speech synthesis system for rebuttal, namely Debatts. Debatts takes two speech prompts, one from the opposing side (i.e. opponent) and one from the speaker. The prompt from the opponent is supposed to provide debating style prosody, and the prompt from the speaker provides identity information. In particular, we pretrain the Debatts system from in-the-wild dataset, and integrate an additional reference encoder to take debating prompt for style. In addition, we also create a debating dataset to develop Debatts. In this setting, Debatts can generate a debating-style speech in rebuttal for any voices. Experimental results confirm the effectiveness of the proposed system in comparison with the classic zero-shot TTS systems.
Selective State Space Model for Monaural Speech Enhancement
Authors: Moran Chen, Qiquan Zhang, Mingjiang Wang, Xiangyu Zhang, Hexin Liu, Eliathamby Ambikairaiah, Deying Chen
Date: 2024-11-09 00:00:00
Tasks: Mamba, Speech Enhancement

Voice user interfaces (VUIs) have facilitated the efficient interactions between humans and machines through spoken commands. Since real-word acoustic scenes are complex, speech enhancement plays a critical role for robust VUI. Transformer and its variants, such as Conformer, have demonstrated cutting-edge results in speech enhancement. However, both of them suffers from the quadratic computational complexity with respect to the sequence length, which hampers their ability to handle long sequences. Recently a novel State Space Model called Mamba, which shows strong capability to handle long sequences with linear complexity, offers a solution to address this challenge. In this paper, we propose a novel hybrid convolution-Mamba backbone, denoted as MambaDC, for speech enhancement. Our MambaDC marries the benefits of convolutional networks to model the local interactions and Mamba's ability for modeling long-range global dependencies. We conduct comprehensive experiments within both basic and state-of-the-art (SoTA) speech enhancement frameworks, on two commonly used training targets. The results demonstrate that MambaDC outperforms Transformer, Conformer, and the standard Mamba across all training targets. Built upon the current advanced framework, the use of MambaDC backbone showcases superior results compared to existing \textcolor{black}{SoTA} systems. This sets the stage for efficient long-range global modeling in speech enhancement.
Speech-Based Estimation of Schizophrenia Severity Using Feature Fusion
Authors: Gowtham Premananth, Carol Espy-Wilson
Date: 2024-11-09 00:00:00
Tasks: Representation Learning

Speech-based assessment of the schizophrenia spectrum has been widely researched over in the recent past. In this study, we develop a deep learning framework to estimate schizophrenia severity scores from speech using a feature fusion approach that fuses articulatory features with different self-supervised speech features extracted from pre-trained audio models. We also propose an auto-encoder-based self-supervised representation learning framework to extract compact articulatory embeddings from speech. Our top-performing speech-based fusion model with Multi-Head Attention (MHA) reduces Mean Absolute Error (MAE) by 9.18% and Root Mean Squared Error (RMSE) by 9.36% for schizophrenia severity estimation when compared with the previous models that combined speech and video inputs.
A Kalman Filter model for synchronization in musical ensembles
Authors: Hugo T. Carvalho, Min S. Li, Massimiliano Di Luca, Alan M. Wing
Date: 2024-11-08 00:00:00

The synchronization of motor responses to rhythmic auditory cues is a fundamental biological phenomenon observed across various species. While the importance of temporal alignment varies across different contexts, achieving precise temporal synchronization is a prominent goal in musical performances. Musicians often incorporate expressive timing variations, which require precise control over timing and synchronization, particularly in ensemble performance. This is crucial because both deliberate expressive nuances and accidental timing deviations can affect the overall timing of a performance. This discussion prompts the question of how musicians adjust their temporal dynamics to achieve synchronization within an ensemble. This paper introduces a novel feedback correction model based on the Kalman Filter, aimed at improving the understanding of interpersonal timing in ensemble music performances. The proposed model performs similarly to other linear correction models in the literature, with the advantage of low computational cost and good performance even in scenarios where the underlying tempo varies.
Classification of Adventitious Sounds Combining Cochleogram and Vision Transformers
Authors: Loredana Daria Mang, Francisco David Gonzalez Martinez, Damian Martinez Munoz, Sebastian Garcia Galan, Raquel Cortina
Date: 2024-11-08 00:00:00
Tasks: Audio Classification, Classification, Sound Classification

Early identification of respiratory irregularities is critical for improving lung health and reducing global mortality rates. The analysis of respiratory sounds plays a significant role in characterizing the respiratory system's condition and identifying abnormalities. The main contribution of this study is to investigate the performance when the input data, represented by cochleogram, is used to feed the Vision Transformer architecture, since this input classifier combination is the first time it has been applied to adventitious sound classification to our knowledge. Although ViT has shown promising results in audio classification tasks by applying self attention to spectrogram patches, we extend this approach by applying the cochleogram, which captures specific spectro-temporal features of adventitious sounds. The proposed methodology is evaluated on the ICBHI dataset. We compare the classification performance of ViT with other state of the art CNN approaches using spectrogram, Mel frequency cepstral coefficients, constant Q transform, and cochleogram as input data. Our results confirm the superior classification performance combining cochleogram and ViT, highlighting the potential of ViT for reliable respiratory sound classification. This study contributes to the ongoing efforts in developing automatic intelligent techniques with the aim to significantly augment the speed and effectiveness of respiratory disease detection, thereby addressing a critical need in the medical field.
CT-Mamba: A Hybrid Convolutional State Space Model for Low-Dose CT Denoising
Authors: Linxuan Li, Wenjia Wei, Luyao Yang, Wenwen Zhang, Jiashu Dong, Yahua Liu, Hongshi Huang, Wei Zhao
Date: 2024-11-12 00:00:00
Tasks: Denoising, Diagnostic, Image Denoising, Long-range modeling, Mamba

Low-dose CT (LDCT) significantly reduces the radiation dose received by patients, however, dose reduction introduces additional noise and artifacts. Currently, denoising methods based on convolutional neural networks (CNNs) face limitations in long-range modeling capabilities, while Transformer-based denoising methods, although capable of powerful long-range modeling, suffer from high computational complexity. Furthermore, the denoised images predicted by deep learning-based techniques inevitably exhibit differences in noise distribution compared to normal-dose CT (NDCT) images, which can also impact the final image quality and diagnostic outcomes. This paper proposes CT-Mamba, a hybrid convolutional State Space Model for LDCT image denoising. The model combines the local feature extraction advantages of CNNs with Mamba's strength in capturing long-range dependencies, enabling it to capture both local details and global context. Additionally, we introduce an innovative spatially coherent 'Z'-shaped scanning scheme to ensure spatial continuity between adjacent pixels in the image. We design a Mamba-driven deep noise power spectrum (NPS) loss function to guide model training, ensuring that the noise texture of the denoised LDCT images closely resembles that of NDCT images, thereby enhancing overall image quality and diagnostic value. Experimental results have demonstrated that CT-Mamba performs excellently in reducing noise in LDCT images, enhancing detail preservation, and optimizing noise texture distribution, and exhibits higher statistical similarity with the radiomics features of NDCT images. The proposed CT-Mamba demonstrates outstanding performance in LDCT denoising and holds promise as a representative approach for applying the Mamba framework to LDCT denoising tasks. Our code will be made available after the paper is officially published: https://github.com/zy2219105/CT-Mamba/.
MaDiNet: Mamba Diffusion Network for SAR Target Detection
Authors: Jie zhou, Chao Xiao, Bowen Peng, Tianpeng Liu, Zhen Liu, Yongxiang Liu, Li Liu
Date: 2024-11-12 00:00:00
Tasks: Mamba

The fundamental challenge in SAR target detection lies in developing discriminative, efficient, and robust representations of target characteristics within intricate non-cooperative environments. However, accurate target detection is impeded by factors including the sparse distribution and discrete features of the targets, as well as complex background interference. In this study, we propose a \textbf{Ma}mba \textbf{Di}ffusion \textbf{Net}work (MaDiNet) for SAR target detection. Specifically, MaDiNet conceptualizes SAR target detection as the task of generating the position (center coordinates) and size (width and height) of the bounding boxes in the image space. Furthermore, we design a MambaSAR module to capture intricate spatial structural information of targets and enhance the capability of the model to differentiate between targets and complex backgrounds. The experimental results on extensive SAR target detection datasets achieve SOTA, proving the effectiveness of the proposed network. Code is available at \href{https://github.com/JoyeZLearning/MaDiNet}{https://github.com/JoyeZLearning/MaDiNet}.
360-Degree Video Super Resolution and Quality Enhancement Challenge: Methods and Results
Authors: Ahmed Telili, Wassim Hamidouche, Ibrahim Farhat, Hadi Amirpour, Christian Timmerer, Ibrahim Khadraoui, Jiajie Lu, The Van Le, Jeonneung Baek, Jin Young Lee, Yiying Wei, Xiaopeng Sun, Yu Gao, JianCheng Huangl, Yujie Zhong
Date: 2024-11-11 00:00:00
Tasks: Computational Efficiency, Super-Resolution, Video Super-Resolution

Omnidirectional (360-degree) video is rapidly gaining popularity due to advancements in immersive technologies like virtual reality (VR) and extended reality (XR). However, real-time streaming of such videos, especially in live mobile scenarios like unmanned aerial vehicles (UAVs), is challenged by limited bandwidth and strict latency constraints. Traditional methods, such as compression and adaptive resolution, help but often compromise video quality and introduce artifacts that degrade the viewer experience. Additionally, the unique spherical geometry of 360-degree video presents challenges not encountered in traditional 2D video. To address these issues, we initiated the 360-degree Video Super Resolution and Quality Enhancement Challenge. This competition encourages participants to develop efficient machine learning solutions to enhance the quality of low-bitrate compressed 360-degree videos, with two tracks focusing on 2x and 4x super-resolution (SR). In this paper, we outline the challenge framework, detailing the two competition tracks and highlighting the SR solutions proposed by the top-performing models. We assess these models within a unified framework, considering quality enhancement, bitrate gain, and computational efficiency. This challenge aims to drive innovation in real-time 360-degree video streaming, improving the quality and accessibility of immersive visual experiences.
CASC: Condition-Aware Semantic Communication with Latent Diffusion Models
Authors: Weixuan Chen, Qianqian Yang
Date: 2024-11-10 00:00:00
Tasks: Decoder, Image Reconstruction, Semantic Communication

Diffusion-based semantic communication methods have shown significant advantages in image transmission by harnessing the generative power of diffusion models. However, they still face challenges, including generation randomness that leads to distorted reconstructions and high computational costs. To address these issues, we propose CASC, a condition-aware semantic communication framework that incorporates a latent diffusion model (LDM)-based denoiser. The LDM denoiser at the receiver utilizes the received noisy latent codes as the conditioning signal to reconstruct the latent codes, enabling the decoder to accurately recover the source image. By operating in the latent space, the LDM reduces computational complexity compared to traditional diffusion models (DMs). Additionally, we introduce a condition-aware neural network (CAN) that dynamically adjusts the weights in the hidden layers of the LDM based on the conditioning signal. This enables finer control over the generation process, significantly improving the perceptual quality of the reconstructed images. Experimental results show that CASC significantly outperforms DeepJSCC in both perceptual quality and visual effect. Moreover, CASC reduces inference time by 51.7% compared to existing DM-based semantic communication systems, while maintaining comparable perceptual performance. The ablation studies also validate the effectiveness of the CAN module in improving the image reconstruction quality.
A Diffuse Light Field Imaging Model for Forward-Scattering Photon-Coded Signal Retrieval
Authors: Hongkun Cao, Xin Jin, Junjie Wei, Yihui Fan, Dongyu Du
Date: 2024-11-10 00:00:00
Tasks: Retrieval

Scattering imaging is often hindered by extremely low signal-to-noise ratios (SNRs) due to the prevalence of scattering noise. Light field imaging has been shown to be effective in suppressing noise and collect more ballistic photons as signals. However, to overcome the SNR limit in super-strong scattering environments, even with light field framework, only rare ballistic signals are insufficient. Inspired by radiative transfer theory, we propose a diffuse light field imaging model (DLIM) that leverages light field imaging to retrieve forward-scattered photons as signals to overcome the challenges of low-SNR imaging caused by super-strong scattering environments. This model aims to recover the ballistic photon signal as a source term from forward-scattered photons based on diffusion equations. The DLIM consists of two main processes: radiance modeling and diffusion light-field approximation. Radiate modeling analyzes the radiance distribution in scattering light field images using a proposed three-plane parameterization, which solves a 4-D radiate kernel describing the impulse function of scattering light field. Then, the scattering light field images synthesize a diffuse source satisfying the diffusion equation governing forward scattering photons, solved under Neumann boundary conditions in imaging space. This is the first physically-aware scattering light field imaging model, extending the conventional light field imaging framework from free space into diffuse space. The extensive experiments confirm that the DLIM can reconstruct the target objects even when scattering light field images are reduced as random noise at extremely low SNRs.
Navigating Distribution Shifts in Medical Image Analysis: A Survey
Authors: Zixian Su, Jingwei Guo, Xi Yang, Qiufeng Wang, Frans Coenen, Kaizhu Huang
Date: 2024-11-05 00:00:00
Tasks: Domain Generalization, Federated Learning, Medical Image Analysis, Navigate, Survey

Medical Image Analysis (MedIA) has become indispensable in modern healthcare, enhancing clinical diagnostics and personalized treatment. Despite the remarkable advancements supported by deep learning (DL) technologies, their practical deployment faces challenges due to distribution shifts, where models trained on specific datasets underperform across others from varying hospitals, regions, or patient populations. To navigate this issue, researchers have been actively developing strategies to increase the adaptability and robustness of DL models, enabling their effective use in unfamiliar and diverse environments. This paper systematically reviews approaches that apply DL techniques to MedIA systems affected by distribution shifts. Unlike traditional categorizations based on technical specifications, our approach is grounded in the real-world operational constraints faced by healthcare institutions. Specifically, we categorize the existing body of work into Joint Training, Federated Learning, Fine-tuning, and Domain Generalization, with each method tailored to distinct scenarios caused by Data Accessibility, Privacy Concerns, and Collaborative Protocols. This perspective equips researchers with a nuanced understanding of how DL can be strategically deployed to address distribution shifts in MedIA, ensuring diverse and robust medical applications. By delving deeper into these topics, we highlight potential pathways for future research that not only address existing limitations but also push the boundaries of deployable MedIA technologies.
3-DUSSS: 3-Dimensional Ultrasonic Self Supervised Segmentation
Authors: Shaun McKnight, Vedran Tunukovic, Amine Hifi, Gareth Pierce, Ehsan Mohseni, Charles MacLeod, Tom OHare
Date: 2024-11-12 00:00:00
Tasks: Defect Detection, Self-Supervised Learning

This study introduces a novel self-supervised learning approach for volumetric segmentation of defect indications captured by phased array ultrasonic testing data from Carbon Fiber Reinforced Polymers (CFRPs). By employing this self-supervised method, defect segmentation is achieved automatically without the need for labelled training data or examples of defects. The approach has been tested using artificially induced defects, including back-drilled holes and Polytetrafluoroethylene (PTFE) inserts, to mimic different defect responses. Additionally, it has been evaluated on stepped geometries with varying thickness, demonstrating impressive generalization across various test scenarios. Minimal preprocessing requirements are needed, with no removal of geometric features or Time-Compensated Gain (TCG) necessary for applying the methodology. The model's performance was evaluated for defect detection, in-plane and through-thickness localisation, as well as defect sizing. All defects were consistently detected with thresholding and different processing steps able to remove false positive indications for a 100% detection accuracy. Defect sizing aligns with the industrial standard 6 dB amplitude drop method, with a Mean Absolute Error (MAE) of 1.41 mm. In-plane and through-thickness localisation yielded comparable results, with MAEs of 0.37 and 0.26 mm, respectively. Visualisations are provided to illustrate how this approach can be utilised to generate digital twins of components.
Hybrid Channel Modeling and Environment Reconstruction for Terahertz Monostatic Sensing
Authors: Yejian Lyu, Zeyu Huang, Stefan Schwarz, Chong Han
Date: 2024-11-12 00:00:00
Tasks: ISAC

THz ISAC aims to integrate novel functionalities, such as positioning and environmental sensing, into communication systems. Accurate channel modeling is crucial for the design and performance evaluation of future ISAC systems. In this paper, a THz measurement campaign for monostatic sensing is presented. VNA-based channel measurements are conducted in a laboratory scenario, where the transmitter and receiver are positioned together to mimic monostatic sensing. The centering frequency and measured bandwidth for these measurements are 300 GHz and 20 GHz, respectively. A DSS scheme is employed to capture spatial sensing channel profiles. Measurements are conducted across 28 transceiver locations arranged along an 'L'-shaped route. Then, an element-wise SAGE algorithm is used to estimate the MPC parameters, i.e., amplitude and delay. Specular and diffuse reflections are analyzed based on geometric principles and the estimated MPC parameters, where the effects from the radiation pattern are observed. A geometry-based MPC trajectory tracking algorithm is then proposed to classify the MPCs and de-embed the effects of the radiation pattern. Following this algorithm, a hybrid channel model is proposed based on the de-embedded MPC parameters. In this hybrid channel model for monostatic sensing, the MPCs are categorized into target-related and environment-related components. The target-related components are utilized for target detection and identification, while the environment-related ones focus on geometrical scenario reconstruction. A demonstration of geometrical environment reconstruction, along with an analysis of reflection loss for target identification, is subsequently presented. This work offers valuable insights into THz monostatic sensing channel modeling and the design of future THz ISAC systems.