pwc-archive/papers-with-abstracts huggingface dataset:
Discriminative Neighborhood Smoothing for Generative Anomalous Sound Detection
Authors: Takuya Fujimura, Keisuke Imoto, Tomoki Toda
Date: 2024-03-18 00:00:00

We propose discriminative neighborhood smoothing of generative anomaly scores for anomalous sound detection. While the discriminative approach is known to achieve better performance than generative approaches often, we have found that it sometimes causes significant performance degradation due to the discrepancy between the training and test data, making it less robust than the generative approach. Our proposed method aims to compensate for the disadvantages of generative and discriminative approaches by combining them. Generative anomaly scores are smoothed using multiple samples with similar discriminative features to improve the performance of the generative approach in an ensemble manner while keeping its robustness. Experimental results show that our proposed method greatly improves the original generative method, including absolute improvement of 22% in AUC and robustly works, while a discriminative method suffers from the discrepancy.
How to train your ears: Auditory-model emulation for large-dynamic-range inputs and mild-to-severe hearing losses
Authors: Peter Leer, Jesper Jensen, Zheng-Hua Tan, Jan Østergaard, Lars Bramsløw
Date: 2024-03-15 00:00:00
Tasks: Speech Enhancement

Advanced auditory models are useful in designing signal-processing algorithms for hearing-loss compensation or speech enhancement. Such auditory models provide rich and detailed descriptions of the auditory pathway, and might allow for individualization of signal-processing strategies, based on physiological measurements. However, these auditory models are often computationally demanding, requiring significant time to compute. To address this issue, previous studies have explored the use of deep neural networks to emulate auditory models and reduce inference time. While these deep neural networks offer impressive efficiency gains in terms of computational time, they may suffer from uneven emulation performance as a function of auditory-model frequency-channels and input sound pressure level, making them unsuitable for many tasks. In this study, we demonstrate that the conventional machine-learning optimization objective used in existing state-of-the-art methods is the primary source of this limitation. Specifically, the optimization objective fails to account for the frequency- and level-dependencies of the auditory model, caused by a large input dynamic range and different types of hearing losses emulated by the auditory model. To overcome this limitation, we propose a new optimization objective that explicitly embeds the frequency- and level-dependencies of the auditory model. Our results show that this new optimization objective significantly improves the emulation performance of deep neural networks across relevant input sound levels and auditory-model frequency channels, without increasing the computational load during inference. Addressing these limitations is essential for advancing the application of auditory models in signal-processing tasks, ensuring their efficacy in diverse scenarios.
Hearing-Loss Compensation Using Deep Neural Networks: A Framework and Results From a Listening Test
Authors: Peter Leer, Jesper Jensen, Laurel H. Carney, Zheng-Hua Tan, Jan Østergaard, Lars Bramsløw
Date: 2024-03-15 00:00:00
Tasks: Music Classification, Speaker Identification, speech-recognition, Speech Recognition

This article investigates the use of deep neural networks (DNNs) for hearing-loss compensation. Hearing loss is a prevalent issue affecting millions of people worldwide, and conventional hearing aids have limitations in providing satisfactory compensation. DNNs have shown remarkable performance in various auditory tasks, including speech recognition, speaker identification, and music classification. In this study, we propose a DNN-based approach for hearing-loss compensation, which is trained on the outputs of hearing-impaired and normal-hearing DNN-based auditory models in response to speech signals. First, we introduce a framework for emulating auditory models using DNNs, focusing on an auditory-nerve model in the auditory pathway. We propose a linearization of the DNN-based approach, which we use to analyze the DNN-based hearing-loss compensation. Additionally we develop a simple approach to choose the acoustic center frequencies of the auditory model used for the compensation strategy. Finally, we evaluate, to our knowledge for the first time, the DNN-based hearing-loss compensation strategies using listening tests with hearing impaired listeners. The results demonstrate that the proposed approach results in feasible hearing-loss compensation strategies. Our proposed approach was shown to provide an increase in speech intelligibility versus an unprocessed baseline and was found to outperform a conventional approach in terms of both intelligibility and preference.
SuperM2M: Supervised and Mixture-to-Mixture Co-Learning for Speech Enhancement and Noise-Robust ASR
Authors: Zhong-Qiu Wang
Date: 2024-03-15 00:00:00
Tasks: Speaker Separation, Speech Enhancement

The current dominant approach for neural speech enhancement is based on supervised learning by using simulated training data. The trained models, however, often exhibit limited generalizability to real-recorded data. To address this, this paper investigates training enhancement models directly on real target-domain data. We propose to adapt mixture-to-mixture (M2M) training, originally designed for speaker separation, for speech enhancement, by modeling multi-source noise signals as a single, combined source. In addition, we propose a co-learning algorithm that improves M2M with the help of supervised algorithms. When paired close-talk and far-field mixtures are available for training, M2M realizes speech enhancement by training a deep neural network (DNN) to produce speech and noise estimates in a way such that they can be linearly filtered to reconstruct the close-talk and far-field mixtures. This way, the DNN can be trained directly on real mixtures, and can leverage close-talk and far-field mixtures as a weak supervision to enhance far-field mixtures. To improve M2M, we combine it with supervised approaches to co-train the DNN, where mini-batches of real close-talk and far-field mixture pairs and mini-batches of simulated mixture and clean speech pairs are alternately fed to the DNN, and the loss functions are respectively (a) the mixture reconstruction loss on the real close-talk and far-field mixtures and (b) the regular enhancement loss on the simulated clean speech and noise. We find that, this way, the DNN can learn from real and simulated data to achieve better generalization to real data. We name this algorithm SuperM2M (supervised and mixture-to-mixture co-learning). Evaluation results on the CHiME-4 dataset show its effectiveness and potential.
WavCraft: Audio Editing and Generation with Large Language Models
Authors: Jinhua Liang, huan zhang, Haohe Liu, Yin Cao, Qiuqiang Kong, Xubo Liu, Wenwu Wang, Mark D. Plumbley, Huy Phan, Emmanouil Benetos
Date: 2024-03-14 00:00:00
Tasks: In-Context Learning

We introduce WavCraft, a collective system that leverages large language models (LLMs) to connect diverse task-specific models for audio content creation and editing. Specifically, WavCraft describes the content of raw audio materials in natural language and prompts the LLM conditioned on audio descriptions and user requests. WavCraft leverages the in-context learning ability of the LLM to decomposes users' instructions into several tasks and tackle each task collaboratively with the particular module. Through task decomposition along with a set of task-specific models, WavCraft follows the input instruction to create or edit audio content with more details and rationales, facilitating user control. In addition, WavCraft is able to cooperate with users via dialogue interaction and even produce the audio content without explicit user commands. Experiments demonstrate that WavCraft yields a better performance than existing methods, especially when adjusting the local regions of audio clips. Moreover, WavCraft can follow complex instructions to edit and create audio content on the top of input recordings, facilitating audio producers in a broader range of applications. Our implementation and demos are available at this https://github.com/JinhuaLiang/WavCraft.
Physics-Informed Neural Network for Volumetric Sound field Reconstruction of Speech Signals
Authors: Marco Olivieri, Xenofon Karakonstantis, Mirco Pezzoli, Fabio Antonacci, Augusto Sarti, Efren Fernandez-Grande
Date: 2024-03-14 00:00:00

Recent developments in acoustic signal processing have seen the integration of deep learning methodologies, alongside the continued prominence of classical wave expansion-based approaches, particularly in sound field reconstruction. Physics-Informed Neural Networks (PINNs) have emerged as a novel framework, bridging the gap between data-driven and model-based techniques for addressing physical phenomena governed by partial differential equations. This paper introduces a PINN-based approach for the recovery of arbitrary volumetric acoustic fields. The network incorporates the wave equation to impose a regularization on signal reconstruction in the time domain. This methodology enables the network to learn the underlying physics of sound propagation and allows for the complete characterization of the sound field based on a limited set of observations. The proposed method's efficacy is validated through experiments involving speech signals in a real-world environment, considering varying numbers of available measurements. Moreover, a comparative analysis is undertaken against state-of-the-art frequency-domain and time-domain reconstruction methods from existing literature, highlighting the increased accuracy across the various measurement configurations.
Overfitted image coding at reduced complexity
Authors: Théophile Blard, Théo Ladune, Pierrick Philippe, Gordon Clare, Xiaoran Jiang, Olivier Déforges
Date: 2024-03-18 00:00:00
Tasks: CPU, Decoder, Image Compression

Overfitted image codecs offer compelling compression performance and low decoder complexity, through the overfitting of a lightweight decoder for each image. Such codecs include Cool-chic, which presents image coding performance on par with VVC while requiring around 2000 multiplications per decoded pixel. This paper proposes to decrease Cool-chic encoding and decoding complexity. The encoding complexity is reduced by shortening Cool-chic training, up to the point where no overfitting is performed at all. It is also shown that a tiny neural decoder with 300 multiplications per pixel still outperforms HEVC. A near real-time CPU implementation of this decoder is made available at https://orange-opensource.github.io/Cool-Chic/.
Diffusion and Multi-Domain Adaptation Methods for Eosinophil Segmentation
Authors: Kevin Lin, Donald Brown, Sana Syed, Adam Greene
Date: 2024-03-17 00:00:00
Tasks: Domain Adaptation

Eosinophilic Esophagitis (EoE) represents a challenging condition for medical providers today. The cause is currently unknown, the impact on a patient's daily life is significant, and it is increasing in prevalence. Traditional approaches for medical image diagnosis such as standard deep learning algorithms are limited by the relatively small amount of data and difficulty in generalization. As a response, two methods have arisen that seem to perform well: Diffusion and Multi-Domain methods with current research efforts favoring diffusion methods. For the EoE dataset, we discovered that a Multi-Domain Adversarial Network outperformed a Diffusion based method with a FID of 42.56 compared to 50.65. Future work with diffusion methods should include a comparison with Multi-Domain adaptation methods to ensure that the best performance is achieved.
A Dynamically Weighted Loss Function for Unsupervised Image Segmentation
Authors: Boujemaa Guermazi, Riadh Ksantini, Naimul Khan
Date: 2024-03-17 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Unsupervised Image Segmentation

Image segmentation is the foundation of several computer vision tasks, where pixel-wise knowledge is a prerequisite for achieving the desired target. Deep learning has shown promising performance in supervised image segmentation. However, supervised segmentation algorithms require a massive amount of data annotated at a pixel level, thus limiting their applicability and scalability. Therefore, there is a need to invest in unsupervised learning for segmentation. This work presents an improved version of an unsupervised Convolutional Neural Network (CNN) based algorithm that uses a constant weight factor to balance between the segmentation criteria of feature similarity and spatial continuity, and it requires continuous manual adjustment of parameters depending on the degree of detail in the image and the dataset. In contrast, we propose a novel dynamic weighting scheme that leads to a flexible update of the parameters and an automatic tuning of the balancing weight between the two criteria above to bring out the details in the images in a genuinely unsupervised manner. We present quantitative and qualitative results on four datasets, which show that the proposed scheme outperforms the current unsupervised segmentation approaches without requiring manual adjustment.
Multiple Teachers-Meticulous Student: A Domain Adaptive Meta-Knowledge Distillation Model for Medical Image Classification
Authors: Shahabedin Nabavi, Kian Anvari Hamedani, Mohsen Ebrahimi Moghaddam, Ahmad Ali Abin, Alejandro F. Frangi
Date: 2024-03-17 00:00:00
Tasks: image-classification, Image Classification, Knowledge Distillation, Medical Image Analysis, Medical Image Classification, Privacy Preserving

Background: Image classification can be considered one of the key pillars of medical image analysis. Deep learning (DL) faces challenges that prevent its practical applications despite the remarkable improvement in medical image classification. The data distribution differences can lead to a drop in the efficiency of DL, known as the domain shift problem. Besides, requiring bulk annotated data for model training, the large size of models, and the privacy-preserving of patients are other challenges of using DL in medical image classification. This study presents a strategy that can address the mentioned issues simultaneously. Method: The proposed domain adaptive model based on knowledge distillation can classify images by receiving limited annotated data of different distributions. The designed multiple teachers-meticulous student model trains a student network that tries to solve the challenges by receiving the parameters of several teacher networks. The proposed model was evaluated using six available datasets of different distributions by defining the respiratory motion artefact detection task. Results: The results of extensive experiments using several datasets show the superiority of the proposed model in addressing the domain shift problem and lack of access to bulk annotated data. Besides, the privacy preservation of patients by receiving only the teacher network parameters instead of the original data and consolidating the knowledge of several DL models into a model with almost similar performance are other advantages of the proposed model. Conclusions: The proposed model can pave the way for practical clinical applications of deep classification methods by achieving the mentioned objectives simultaneously.
A Spectrum-based Image Denoising Method with Edge Feature Enhancement
Authors: Peter Luvton, Alfredo Castillejos, Jim Zhao, Christina Chajo
Date: 2024-03-16 00:00:00
Tasks: Denoising, Diagnostic, Image Denoising, Image Registration, Image Restoration, Visual Tracking

Image denoising stands as a critical challenge in image processing and computer vision, aiming to restore the original image from noise-affected versions caused by various intrinsic and extrinsic factors. This process is essential for applications that rely on the high quality and clarity of visual information, such as image restoration, visual tracking, and image registration, where the original content is vital for performance. Despite the development of numerous denoising algorithms, effectively suppressing noise, particularly under poor capture conditions with high noise levels, remains a challenge. Image denoising's practical importance spans multiple domains, notably medical imaging for enhanced diagnostic precision, as well as surveillance and satellite imagery where it improves image quality and usability. Techniques like the Fourier transform, which excels in noise reduction and edge preservation, along with phase congruency-based methods, offer promising results for enhancing noisy and low-contrast images common in modern imaging scenarios.
Tensor Star Tensor Decomposition and Its Applications to Higher-order Compression and Completion
Authors: Wuyang Zhou, Yu-Bang Zheng, Qibin Zhao, Danilo Mandic
Date: 2024-03-15 00:00:00
Tasks: Tensor Decomposition

A novel tensor decomposition framework, termed Tensor Star (TS) decomposition, is proposed which represents a new type of tensor network decomposition based on tensor contractions. This is achieved by connecting the core tensors in a ring shape, whereby the core tensors act as skip connections between the factor tensors and allow for direct correlation characterisation between any two arbitrary dimensions. Uniquely, this makes it possible to decompose an order-$N$ tensor into $N$ order-$3$ factor tensors $\{\mathcal{G}_{k}\}_{k=1}^{N}$ and $N$ order-$4$ core tensors $\{\mathcal{C}_{k}\}_{k=1}^{N}$, which are arranged in a star shape. Unlike the class of Tensor Train (TT) decompositions, these factor tensors are not directly connected to one another. The so obtained core tensors also enable consecutive factor tensors to have different latent ranks. In this way, the TS decomposition alleviates the "curse of dimensionality" and controls the "curse of ranks", exhibiting a storage complexity which scales linearly with the number of dimensions and as the fourth power of the ranks.
Task-Oriented Hybrid Beamforming for OFDM-DFRC Systems with Flexibly Controlled Space-Frequency Spectra
Authors: Lingyun Xu, Bowen Wang, Ziyang Cheng
Date: 2024-03-18 00:00:00

This paper investigates the issues of the hybrid beamforming design for the orthogonal frequency division multiplexing dual-function radar-communication (DFRC) system in multiple task scenarios involving the radar scanning and detection task and the target tracking task. To meet different task requirements of the DFRC system, we introduce two novel radar beampattern metrics, the average integrated sidelobe to minimum mainlobe ratio (AISMMR) and average peak sidelobe to integrated mainlobe ratio (APSIMR), to characterize the space-frequency spectra in different scenarios. Then, two HBF design problems are formulated for two task scenarios by minimizing the AISMMR and APSIMR respectively subject to the constraints of communication quality-of-service (QoS), power budget, and hardware. Due to the non-linearity and close coupling between the analog and digital beamformers in both the objective functions and QoS constraint, the resultant formulated problems are challenging to solve. Towards that end, a unified optimization algorithm based on a consensus alternating direction method of multipliers (CADMM) is proposed to solve these two problems. Moreover, under the unified CADMM framework, the closed-form solutions of primal variables in the original two problems are obtained with low complexity. Numerical simulations are provided to demonstrate the feasibility and effectiveness of the proposed algorithm.
Specific Emitter Identification Handling Modulation Variation with Margin Disparity Discrepancy
Authors: Yezhuo Zhang, Zinan Zhou, Xuanpeng Li
Date: 2024-03-18 00:00:00

In the domain of Specific Emitter Identification (SEI), it is recognized that transmitters can be distinguished through the impairments of their radio frequency front-end, commonly referred to as Radio Frequency Fingerprint (RFF) features. However, modulation schemes can be deliberately coupled into signal-level data to confound RFF information, often resulting in high susceptibility to failure in SEI. In this paper, we propose a domain-invariant feature oriented Margin Disparity Discrepancy (MDD) approach to enhance SEI's robustness in rapidly modulation-varying environments. First, we establish an upper bound for the difference between modulation domains and define the loss function accordingly. Then, we design an adversarial network framework incorporating MDD to align variable modulation features. Finally, We conducted experiments utilizing 7 HackRF-One transmitters, emitting 11 types of signals with analog and digital modulations. Numerical results indicate that our approach achieves an average improvement of over 20\% in accuracy compared to classical SEI methods and outperforms other UDA techniques. Codes are available at https://github.com/ZhangYezhuo/MDD-SEI.
STAR-RIS Aided Integrated Sensing and Communication over High Mobility Scenario
Authors: Muye Li, Shun Zhang, Yao Ge, Zan Li, Feifei Gao, Pingzhi Fan
Date: 2024-03-18 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has become a promising technology for future communication system. In this paper, we consider a millimeter wave system over high mobility scenario, and propose a novel simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) aided ISAC scheme. To improve the communication service of the in-vehicle user equipment (UE) and simultaneously track and sense the vehicle with the help of nearby roadside units (RSUs), a STAR-RIS is equipped on the outside surface of the vehicle. Firstly, an efficient transmission structure is developed, where a number of training sequences with orthogonal precoders and combiners are respectively utilized at BS and RSUs for channel parameter extraction. Then, the near-field static channel model between the STAR-RIS and in-vehicle UE as well as the far-field time-frequency selective BS-RIS-RSUs channel model are characterized. By utilizing the multidimensional orthogonal matching pursuit (MOMP) algorithm, the cascaded channel parameters of the BS-RIS-RSUs links can be obtained at the RSUs. Thus, the vehicle localization and its velocity measurement can be acquired by jointly utilizing these extracted cascaded channel parameters of all RSUs. Note that the MOMP algorithm can be further utilized to extract the channel parameters of the BS-RIS-UE link for communication. With the help of sensing results, the phase shifts of the STAR-RIS are delicately designed, which can significantly improve the received signal strength for both the RSUs and the in-vehicle UE, and can finally enhance the sensing and communication performance. Moreover, the trade-off for sensing and communication is designed by optimizing the energy splitting factors of the STAR-RIS. Finally, simulation results are provided to validate the feasibility and effectiveness of our proposed STAR-RIS aided ISAC scheme.