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共 29921 条记录，以下是81-90 订阅

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Li, Xuewei Yang, Kunyi Zhao, Yue Xu, Tianyi Yu, Jian Yu, Mei Zhao, Mankun School of Future Technology Tianjin University Tianjin300350 China College of Intelligence and Computing Tianjin University Tianjin300350 China Information and Network Center Tianjin University Tianjin300350 China School of New Media and Communication Tianjin University Tianjin300350 China

ISBN: (纸本)9798350368741

Sequential recommender systems aim to learn user preferences through historical interaction sequences. User interactions are driven both by popular trends and personal interests, introducing two types of noise: popular choices triggered by conformist behavior and irrelevant terms that do not reflect the user's true interests. These noises inhibit the model's ability to learn user representations. However, the lack of explicit noise labels makes the sequence denoising problem extremely challenging. We propose a new sequence denoising model (PISD), which extracts both popularity signal and interest signal to accurately identify and remove noisy items while maximizing the retention of personalized preference information. This approach generates clean subsequences for sequential recommendation tasks. Extensive experiments on three publicly available datasets show that our model is compatible with most sequential recommendations and significantly outperforms state-of-the-art denoising methods. © 2025 ieee.

关键词： contrastive learning popularity debias sequence denoising sequential recommendation

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AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models

AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Larg...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Hong, Xin Gong, Yuan Sethu, Vidhyasaharan Dang, Ting School of Computing and Information Systems University of Melbourne Melbourne Australia CSAIL SLS Massachusetts Institute of Technology Boston United States School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia

ISBN: (纸本)9798350368741

Recent advancements in Large Language Models (LLMs) have demonstrated great success in many Natural Language processing (NLP) tasks. In addition to their cognitive intelligence, exploring their capabilities in emotional intelligence is also crucial, as it enables more natural and empathetic conversational AI. Recent studies have shown LLMs' capability in recognizing emotions, but they often focus on single emotion labels and overlook the complex and ambiguous nature of human emotions. This study is the first to address this gap by exploring the potential of LLMs in recognizing ambiguous emotions, leveraging their strong generalization capabilities and in-context learning. We design zero-shot and few-shot prompting and incorporate past dialogue as context information for ambiguous emotion recognition. Experiments conducted using three datasets indicate significant potential for LLMs in recognizing ambiguous emotions, and highlight the substantial benefits of including context information. Furthermore, our findings indicate that LLMs demonstrate a high degree of effectiveness in recognizing less ambiguous emotions and exhibit potential for identifying more ambiguous emotions, paralleling human perceptual capabilities. © 2025 ieee.

关键词： ambiguous emotion emotion recognition large language models multimodal prompt design

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Fading-Invariant Adversarial Attacks on Neural Modulation Recognition

Fading-Invariant Adversarial Attacks on Neural Modulation Re...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Xinze Zhu, Dengao Dong, Xiyao He, Kun School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350368741

Despite significant advances in Deep Neural Networks (DNNs) for Neural Modulation Recognition (NMR) in wireless communications, recent research highlights their vulnerability to adversarial attacks. However, in practical wireless communication scenarios, adversarial signals transmitted by attackers are subject to channel effects, resulting in random fading at the receiver, which diminishes attack effectiveness. To address this challenge, we propose a novel Fading-Invariant Method (FIM) for adversarial attacks on NMR under wireless channel effects. FIM leverages a Neural Inverse Model (NIM) to counteract the random transformations introduced by channel effects, ensuring that the received adversarial signals closely resemble the originally crafted waveform. Additionally, we devise a Transmit Power Estimation (TPE) method to appropriately amplify the transmitted perturbation power, mitigating the fading effect while maintaining the imperceptibility of received adversarial signals. Extensive experiments demonstrate that our method outperforms all baselines in attacking state-of-the-art NMR models under channel effects. © 2025 ieee.

关键词： adversarial attacks channel effects fading-invariant method neural modulation recognition signal perturbation

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Advanced Comparative Analysis of Time-Frequency signal processing Techniques and Machine Learning Models for Optimized Biomedical Classification

Advanced Comparative Analysis of Time-Frequency Signal Proce...

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2025 international conference on Intelligent Control, computing and communications, IC3 2025

作者： Reddy, Uma Lakhanpal, Sorabh Singh, Jay Meena, Talari Shakir, Sarah Abbas Sakthi, G. Mallireddy, Lohitha New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India Lovely Professional University Phagwara India Gl Bajaj Institute of Technology and Management Dept. of Eee U.P. Gr. Noida India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Griet Department of Cse Telangana Hyderabad India

ISBN: (纸本)9798331527495

This research aims at comparing the performance of TF analysis in classifying biomedical signals, with special reference to ECG and EEG signals using PhysioNet database as the major source of data. Fourier Transform and Wavelet Transform, are used to extract the feature from signals for the time-frequency analysis. These features are then used for classification using the algorithms like Random Forest and Neural Networks. All computations are performed with SciPy, NumPy, and Scikit-learn libraries. The study assesses the methods considering the classification performance, computational cost, and interpretability toward determining which approaches offer the best solution for the biomedical signal classification. It needs to be mentioned that the presented results are aimed at helping researchers and practitioners in choosing proper methods for biomedical applications of ECG and EEG signals, focused on the PhysioNet database. © 2025 ieee.

关键词： Wavelet decomposition

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Multi-modal Salient Object Detection via a Unified Diffusion Model

Multi-modal Salient Object Detection via a Unified Diffusion...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Shuo Huang, Jiaming Tang, Wenbing Tian, Lili Wei, Yuang Liu, Jing Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Huolala Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science and Technology East China Normal University China

ISBN: (纸本)9798350368741

Salient Object Detection (SOD) aims to identify and segment the most striking elements within an image. Salient object detection methods can be differentiated into several types according to the input data, such as RGB-D (Depth) and RGB-T (Thermal). Previous research primarily focused on saliency detection for single data types. However, forcing an RGB-D SOD model to process RGB-T data will degrade its performance significantly. In addition, current methods still face challenges in detecting fine edge details of salient objects and achieving end-to-end training. To address these issues, we introduce diffSOD, which leverages stable diffusion and cross-modal feature rectification and fusion module for saliency detection by transforming salient object detection into a denoising process from a noisy mask to an object mask. It offers a unified solution for salient object detection that seamlessly spans both RGB-D SOD and RGB-T SOD. Extensive experiments validate the effectiveness of the proposed diffSOD, demonstrating its ability to efficiently detect salient objects across both RGB-D and RGB-T data, while achieving superior performance over state-of-the-art methods. © 2025 ieee.

关键词： attention interaction fusion latent diffusion model multi-modal salient object detection

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Seg-diffusion: Text-to-Image Diffusion Model for Open-Vocabulary Semantic Segmentation

Seg-diffusion: Text-to-Image Diffusion Model for Open-Vocabu...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Shuo Huang, Jiaming Wu, Yan Hu, Tao Tang, Wenbing Liu, Jing Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Huolala Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798350368741

Open-vocabulary semantic segmentation (OVSS) is a challenging computer vision task that labels each pixel within an image based on text descriptions. Recent advancements in OVSS are largely attributed to the increased model capacity. However, these models often struggle with unfamiliar images or unseen text, as their visual language understanding is limited to training data. Text-to-image (T2I) diffusion models have demonstrated strong image generation with diverse open-vocabulary descriptions. It prompted us to explore whether the comprehensive priors in T2I diffusion models could enhance the zero-shot generalization of OVSS. In this study, we define OVSS as a denoising diffusion task from noisy to object mask and introduce Seg-diffusion, a novel method based on Stable Diffusion that utilizes its extensive visual and linguistic prior knowledge. Specifically, the object mask diffuses from ground-truth to a random distribution in latent space. The model learns to reverse this noisy process to reconstruct object mask to segment objectives using text embeddings with our proposed Content Attention Module (CAM). Extensive experiments on popular OVSS benchmarks show that Seg-diffusion outperforms previous well-established methods and achieves impressive zero-shot generalization to unseen datasets. © 2025 ieee.

关键词： content attention module fine-tuning protocol open-vocabulary semantic segmentation text-to-image LDM

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CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

CroPrompt: Cross-task Interactive Prompting for Zero-shot Sp...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Qin, Libo Wei, Fuxuan Chen, Qiguang Zhou, Jingxuan Huang, Shijue Si, Jiasheng Lu, Wenpeng Che, Wanxiang School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

ISBN: (纸本)9798350368741

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU. © 2025 ieee.

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Conditional-Balanced Adversarial Delta Tuning for Cross-Domain Implicit Discourse Relation Recognition

Conditional-Balanced Adversarial Delta Tuning for Cross-Doma...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Liu, Chang Sun, HaoWen Zhao, Haodong He, Ruifang He, Xu Wang, Bo College of intelligence and Computing Tianjin University Tianjin China

ISBN: (纸本)9798350368741

Implicit discourse relation recognition (IDRR) is faced with a domain dilemma. Recent studies have achieved breakthroughs in standard datasets, while they are not appropriate in domains with insufficient data, such as bio-medicine. In this paper, we treat this problem as a cross-domain IDRR task, which transfers knowledge from the source domain to improve the understanding of the target domain. However, cross-domain IDRR is supervised and suffers from striking domain gaps, and general domain adaptation methods are not applicable. Therefore, we propose a Conditional-Balanced Adversarial Delta Tuning (CBADT) framework, which 1) leverages delta tuning to mine the dense domain-specific knowledge in low-resource scenarios;2) builds a conditional adversarial verbalizer to inject domain-invariant knowledge into soft prompts;3) proposes a domain label fusion method to balance domain-specific label words and fit the hybrid features from two domains. Experiments on both Chinese and English corpora demonstrate the transferability of our model. © 2025 ieee.

关键词： Domain Adaptation Implicit Discourse Relation Recognition Prompt Learning

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Generalize Audio Deepfake Algorithm Recognition via Attribution Enhancement

Generalize Audio Deepfake Algorithm Recognition via Attribut...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Wang, Zhigang Ye, Dengpan Li, Jingyang Deng, Jiacheng Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9798350368741

The development of voice cloning techniques has made forgery audios indistinguishable, posing an urgency to trace their sources. Many existing works focus on improving identification accuracy for audio deepfake algorithm recognition. However, most methods ignore the impact of complex information in audio signals on attribution. In this paper, we propose an audio deepfake attribution enhancement (ADAE) strategy, which aims to magnify the fingerprints of generation styles by removing the speaker information. This is achieved through an information disentangle block with an extra speaker encoder. In addition, we propose the FakeSource dataset, a novel audio deepfake algorithm recognition dataset that contains 25 different voice cloning algorithms, to address the constraint of data scarcity. Experiments on the FakeSource dataset demonstrate that ADAE improves the performance of unseen algorithm detection. We also assess ADAE on a more challenging training-free task which shows competitive performance. © 2025 ieee.

关键词： Audio deepfake algorithm recognition feature enhancement representation disentangle

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DA-LIF: Dual Adaptive Leaky Integrate-and-Fire Model for Deep Spiking Neural Networks

DA-LIF: Dual Adaptive Leaky Integrate-and-Fire Model for Dee...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Tianqing Yu, Kairong Zhang, Jian Wang, Hongwei College of Computer Science and Technology Zhejiang University Hangzhou China ZJU-UIUC Institute Zhejiang University Haining China

ISBN: (纸本)9798350368741

Spiking Neural Networks (SNNs) are valued for their ability to process spatio-temporal information efficiently, offering biological plausibility, low energy consumption, and compatibility with neuromorphic hardware. However, the commonly used Leaky Integrate-and-Fire (LIF) model overlooks neuron heterogeneity and independently processes spatial and temporal information, limiting the expressive power of SNNs. In this paper, we propose the Dual Adaptive Leaky Integrate- and-Fire (DA-LIF) model, which introduces spatial and temporal tuning with independently learnable decays. Evaluations on both static (CIFAR10/100, ImageNet) and neuromorphic datasets (CIFAR10-DVS, DVS128 Gesture) demonstrate superior accuracy with fewer timesteps compared to state-of-the-art methods. Importantly, DA-LIF achieves these improvements with minimal additional parameters, maintaining low energy consumption. Extensive ablation studies further highlight the robustness and effectiveness of the DA-LIF model. © 2025 ieee.

关键词： Brain-inspired Learning Neuromorphic computing Spiking Neural Networks

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