IEEE Signal Processing Letters最新文献

Diagnosis of Parkinson's Disease Based on Hybrid Fusion Approach of Offline Handwriting Images 基于离线手写图像的混合融合方法诊断帕金森病

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-11-12 DOI: 10.1109/LSP.2024.3496579

Shanyu Dong;Jin Liu;Jianxin Wang

{"title":"Diagnosis of Parkinson's Disease Based on Hybrid Fusion Approach of Offline Handwriting Images","authors":"Shanyu Dong;Jin Liu;Jianxin Wang","doi":"10.1109/LSP.2024.3496579","DOIUrl":"https://doi.org/10.1109/LSP.2024.3496579","url":null,"abstract":"Handwriting images are commonly used to diagnose Parkinson's disease due to their intuitive nature and easy accessibility. However, existing methods have not explored the potential of the fusion of different handwriting image sources for diagnosis. To address this issue, this study proposes a hybrid fusion approach that makes use of the visual information derived from different handwriting images and handwriting templates, significantly enhancing the performance in diagnosing Parkinson's disease. The proposed method involves several key steps. Initially, different preprocessed handwriting images undergo pixel-level fusion using Laplacian transformation. Subsequently, the fused and original images are fed into a pre-trained CNN separately to extract visual features. Finally, feature-level fusion is performed by concatenating the feature vectors extracted from the flatten layer, and the fused feature vectors are input into SVM to obtain classification results. Our experimental results validate that the proposed method achieves excellent performance by only utilizing visual features from images, with 95.45% accuracy on the NewHandPD. Furthermore, the results obtained on our dataset verify the strong generalizability of the proposed approach.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"31 ","pages":"3179-3183"},"PeriodicalIF":3.2,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142671993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Multi-Prototypes Aware Integration for Zero-Shot Cross-Domain Slot Filling 用于零点跨域插槽填充的鲁棒多原型感知集成技术

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-11-11 DOI: 10.1109/LSP.2024.3495561

Shaoshen Chen;Peijie Huang;Zhanbiao Zhu;Yexing Zhang;Yuhong Xu

{"title":"Robust Multi-Prototypes Aware Integration for Zero-Shot Cross-Domain Slot Filling","authors":"Shaoshen Chen;Peijie Huang;Zhanbiao Zhu;Yexing Zhang;Yuhong Xu","doi":"10.1109/LSP.2024.3495561","DOIUrl":"https://doi.org/10.1109/LSP.2024.3495561","url":null,"abstract":"Cross-domain slot filling is a widely explored problem in spoken language understanding (SLU), which requires the model to transfer between different domains under data sparsity conditions. Dominant two-step hierarchical models first extract slot entities and then calculate the similarity score between slot description-based prototypes and the last hidden layer of the slot entity, selecting the closest prototype as the predicted slot type. However, these models only use slot descriptions as prototypes, which lacks robustness. Moreover, these approaches have less regard for the inherent knowledge in the slot entity embedding to suffer from the issue of overfitting. In this letter, we propose a Robust Multi-prototypes Aware Integration (RMAI) method for zero-shot cross-domain slot filling. In RMAI, more robust slot entity-based prototypes and inherent knowledge in the slot entity embedding are utilized to improve the classification performance and alleviate the risk of overfitting. Furthermore, a multi-prototypes aware integration approach is proposed to effectively integrate both our proposed slot entity-based prototypes and the slot description-based prototypes. Experimental results on the SNIPS dataset demonstrate the well performance of RMAI.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"31 ","pages":"3169-3173"},"PeriodicalIF":3.2,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SoLAD: Sampling Over Latent Adapter for Few Shot Generation SoLAD：在潜在适配器上采样，生成少量镜头

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-11-11 DOI: 10.1109/LSP.2024.3496822

Arnab Kumar Mondal;Piyush Tiwary;Parag Singla;Prathosh A.P.

引用次数: 0

Differentiable Duration Refinement Using Internal Division for Non-Autoregressive Text-to-Speech 利用内分法对非自回归文本到语音进行可变时长细化

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-11-11 DOI: 10.1109/LSP.2024.3495578

Jaeuk Lee;Yoonsoo Shin;Joon-Hyuk Chang

{"title":"Differentiable Duration Refinement Using Internal Division for Non-Autoregressive Text-to-Speech","authors":"Jaeuk Lee;Yoonsoo Shin;Joon-Hyuk Chang","doi":"10.1109/LSP.2024.3495578","DOIUrl":"https://doi.org/10.1109/LSP.2024.3495578","url":null,"abstract":"Most non-autoregressive text-to-speech (TTS) models acquire target phoneme duration (target duration) from internal or external aligners. They transform the speech-phoneme alignment produced by the aligner into the target duration. Since this transformation is not differentiable, the gradient of the loss function that maximizes the TTS model's likelihood of speech (e.g., mel spectrogram or waveform) cannot be propagated to the target duration. In other words, the target duration is produced regardless of the TTS model's likelihood of speech. Hence, we introduce a differentiable duration refinement that produces a learnable target duration for maximizing the likelihood of speech. The proposed method uses an internal division to locate the phoneme boundary, which is determined to improve the performance of the TTS model. Additionally, we propose a duration distribution loss to enhance the performance of the duration predictor. Our baseline model is JETS, a representative end-to-end TTS model, and we apply the proposed methods to the baseline model. Experimental results show that the proposed method outperforms the baseline model in terms of subjective naturalness and character error rate.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"31 ","pages":"3154-3158"},"PeriodicalIF":3.2,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142671988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LFSamba: Marry SAM With Mamba for Light Field Salient Object Detection LFSamba：将 SAM 与 Mamba 相结合，用于光场显著目标检测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-11-07 DOI: 10.1109/LSP.2024.3493799

Zhengyi Liu;Longzhen Wang;Xianyong Fang;Zhengzheng Tu;Linbo Wang

引用次数: 0

Binomial Harmonic Approximation Double-Phase Estimator Tracking for BOC Modulated Signals 用于 BOC 调制信号的二项式谐波近似双相估计跟踪器

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-11-07 DOI: 10.1109/LSP.2024.3493793

Xiangjie Ding;Zhi Zhao;Ying Yang

引用次数: 0

CADeTT: Context-Adaptive Deep-Trinary-Tree Lossless Compression of Event Camera Frames CADeTT：事件摄像机帧的上下文自适应深度二叉树无损压缩

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-11-07 DOI: 10.1109/LSP.2024.3493801

Ionut Schiopu;Radu Ciprian Bilcu

引用次数: 0

Multiple Subspace-Based Target Detection in Deterministic Interference 确定性干扰中基于多个子空间的目标探测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-11-04 DOI: 10.1109/LSP.2024.3491012

Mengru Sun;Weijian Liu;Jun Liu;Chengpeng Hao;Kefei Li

引用次数: 0

Precise Analysis of Covariance Identifiability for Activity Detection in Grant-Free Random Access 无赠予随机存取中活动检测的协方差可识别性精确分析

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-11-04 DOI: 10.1109/LSP.2024.3491018

Shengsong Luo;Junjie Ma;Chongbin Xu;Xin Wang

{"title":"Precise Analysis of Covariance Identifiability for Activity Detection in Grant-Free Random Access","authors":"Shengsong Luo;Junjie Ma;Chongbin Xu;Xin Wang","doi":"10.1109/LSP.2024.3491018","DOIUrl":"https://doi.org/10.1109/LSP.2024.3491018","url":null,"abstract":"We consider the identifiability issue of maximum-likelihood based activity detection in massive MIMO-based grant-free random access. An intriguing observation by (Chen et al., 2022) indicates that the identifiability undergoes a phase transition for commonly-used random user signatures as \u0000<inline-formula><tex-math>$L^{2}$</tex-math></inline-formula>\u0000, \u0000<inline-formula><tex-math>$N$</tex-math></inline-formula>\u0000 and \u0000<inline-formula><tex-math>$K$</tex-math></inline-formula>\u0000 tend to infinity with fixed ratios, where \u0000<inline-formula><tex-math>$L$</tex-math></inline-formula>\u0000, \u0000<inline-formula><tex-math>$N$</tex-math></inline-formula>\u0000 and \u0000<inline-formula><tex-math>$K$</tex-math></inline-formula>\u0000 denote the user signature length, the total number of users, and the number of active users, respectively. In this letter, we provide a precise analytical characterization of the phase transition based on a spectral universality conjecture. Numerical results demonstrate excellent agreement between our theoretical predictions and the empirical phase transitions.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"31 ","pages":"3184-3188"},"PeriodicalIF":3.2,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142671996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gradient-Level Differential Privacy Against Attribute Inference Attack for Speech Emotion Recognition 针对语音情感识别的属性推理攻击的渐变级差异隐私保护

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-11-01 DOI: 10.1109/LSP.2024.3490379

Haijiao Chen;Huan Zhao;Zixing Zhang

{"title":"Gradient-Level Differential Privacy Against Attribute Inference Attack for Speech Emotion Recognition","authors":"Haijiao Chen;Huan Zhao;Zixing Zhang","doi":"10.1109/LSP.2024.3490379","DOIUrl":"https://doi.org/10.1109/LSP.2024.3490379","url":null,"abstract":"The Federated Learning (FL) paradigm for distributed privacy preservation is valued for its ability to collaboratively train Speech Emotion Recognition (SER) models while keeping data localized. However, recent studies reveal privacy leakage in the model sharing process. Existing differential privacy schemes face increasing inference attack risks as clients expose more model updates. To address these challenges, we propose a \u0000<underline>G</u>\u0000radient-level \u0000<underline>H</u>\u0000ierarchical \u0000<underline>D</u>\u0000ifferential \u0000<underline>P</u>\u0000rivacy (GHDP) strategy to mitigate attribute inference attacks. GHDP employs normalization to distinguish gradient importance, clipping significant gradients and filtering out sensitive information that may lead to privacy leaks. Additionally, increased random perturbations are applied to early model layers during backpropagation, achieving hierarchical differential privacy through layered noise addition. This theoretically grounded approach offers enhanced protection for critical information. Our experiments show that GHDP maintains stable SER performance while providing robust privacy protection, unaffected by the number of model updates.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"31 ","pages":"3124-3128"},"PeriodicalIF":3.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142671123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0