IEEE Signal Processing Letters最新文献

KFA: Keyword Feature Augmentation for Open Set Keyword Spotting KFA：用于发现开放集关键词的关键词特征增强技术

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-22 DOI: 10.1109/LSP.2024.3484932

Kyungdeuk Ko;Bokyeung Lee;Jonghwan Hong;Hanseok Ko

{"title":"KFA: Keyword Feature Augmentation for Open Set Keyword Spotting","authors":"Kyungdeuk Ko;Bokyeung Lee;Jonghwan Hong;Hanseok Ko","doi":"10.1109/LSP.2024.3484932","DOIUrl":"https://doi.org/10.1109/LSP.2024.3484932","url":null,"abstract":"In recent years, with the advancement of deep learning technology and the emergence of smart devices, there has been a growing interest in keyword spotting (KWS), which is used to activate AI systems with automatic speech recognition and text-to-speech. However, smart devices with KWS often encounter false alarm errors when inputting unexpected words. To address this issue, existing KWS methods typically train non-target words as an \u0000<italic>unknown</i>\u0000 class. Despite these efforts, there is still a possibility that unseen words not trained as part of the \u0000<italic>unknown</i>\u0000 class could be misclassified as one of the target words. To overcome this limitation, we propose a new method named Keyword Feature Augmentation (KFA) for open-set KWS. KFA performs feature augmentation through adversarial learning to increase the loss. The augmented features are constrained within a limited space using label smoothing. Unlike other generative model-based open set recognition (OSR) methods, KFA does not require any additional training parameters or repeated operation for inference. As a result, KFA has achieved a 0.955 AUROC score and 97.34% target class accuracy for Google Speech Commands V1, and a 0.959 AUROC score and 98.17% target class accuracy for Google Speech Commands V2, which is the highest performance when compared to various OSR methods.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524238","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

Audio Mamba: Bidirectional State Space Model for Audio Representation Learning 音频曼巴用于音频表征学习的双向状态空间模型

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-17 DOI: 10.1109/LSP.2024.3483009

Mehmet Hamza Erol;Arda Senocak;Jiu Feng;Joon Son Chung

引用次数: 0

System-Informed Neural Network for Frequency Detection 用于频率检测的系统信息神经网络

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-17 DOI: 10.1109/LSP.2024.3483036

Sunyoung Ko;Myoungin Shin;Geunhwan Kim;Youngmin Choo

引用次数: 0

RFI-Aware and Low-Cost Maximum Likelihood Imaging for High-Sensitivity Radio Telescopes 用于高灵敏度射电望远镜的射频干扰感知和低成本最大似然成像技术

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-17 DOI: 10.1109/LSP.2024.3483011

J. Wang;M. N. El Korso;L. Bacharach;P. Larzabal

引用次数: 0

Order Estimation of Linear-Phase FIR Filters for DAC Equalization in Multiple Nyquist Bands 用于多奈奎斯特频带 DAC 均衡的线性相位 FIR 滤波器的阶次估计

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-17 DOI: 10.1109/LSP.2024.3483008

Deijany Rodriguez Linares;Håkan Johansson;Yinan Wang

引用次数: 0

Learning Noise Adapters for Incremental Speech Enhancement 学习噪声适配器以增强语音效果

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-16 DOI: 10.1109/LSP.2024.3482171

Ziye Yang;Xiang Song;Jie Chen;Cédric Richard;Israel Cohen

引用次数: 0

Maximum Entropy and Quantized Metric Models for Absolute Category Ratings 绝对类别评级的最大熵和量化度量模型

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-15 DOI: 10.1109/LSP.2024.3480832

Dietmar Saupe;Krzysztof Rusek;David Hägele;Daniel Weiskopf;Lucjan Janowski

{"title":"Maximum Entropy and Quantized Metric Models for Absolute Category Ratings","authors":"Dietmar Saupe;Krzysztof Rusek;David Hägele;Daniel Weiskopf;Lucjan Janowski","doi":"10.1109/LSP.2024.3480832","DOIUrl":"https://doi.org/10.1109/LSP.2024.3480832","url":null,"abstract":"The datasets of most image quality assessment studies contain ratings on a categorical scale with five levels, from bad (1) to excellent (5). For each stimulus, the number of ratings from 1 to 5 is summarized and given in the form of the mean opinion score. In this study, we investigate families of multinomial probability distributions parameterized by mean and variance that are used to fit the empirical rating distributions. To this end, we consider quantized metric models based on continuous distributions that model perceived stimulus quality on a latent scale. The probabilities for the rating categories are determined by quantizing the corresponding random variables using threshold values. Furthermore, we introduce a novel discrete maximum entropy distribution for a given mean and variance. We compare the performance of these models and the state of the art given by the generalized score distribution for two large data sets, KonIQ-10k and VQEG HDTV. Given an input distribution of ratings, our fitted two-parameter models predict unseen ratings better than the empirical distribution. In contrast to empirical distributions of absolute category ratings and their discrete models, our continuous models can provide fine-grained estimates of quantiles of quality of experience that are relevant to service providers to satisfy a certain fraction of the user population.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524236","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

Pose-Promote: Progressive Visual Perception for Activities of Daily Living 姿势-促进：日常生活活动的渐进式视觉感知

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-14 DOI: 10.1109/LSP.2024.3480046

Qilang Ye;Zitong Yu

引用次数: 0

Learning Multidimensional Spatial Attention for Robust Nighttime Visual Tracking 学习多维空间注意力，实现稳健的夜间视觉跟踪

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-14 DOI: 10.1109/LSP.2024.3480831

Qi Gao;Mingfeng Yin;Yuanzhi Ni;Yuming Bo;Shaoyi Bei

{"title":"Learning Multidimensional Spatial Attention for Robust Nighttime Visual Tracking","authors":"Qi Gao;Mingfeng Yin;Yuanzhi Ni;Yuming Bo;Shaoyi Bei","doi":"10.1109/LSP.2024.3480831","DOIUrl":"https://doi.org/10.1109/LSP.2024.3480831","url":null,"abstract":"The recent development of advanced trackers, which use nighttime image enhancement technology, has led to marked advances in the performance of visual tracking at night. However, the images recovered by currently available enhancement methods still have some weaknesses, such as blurred target details and obvious image noise. To this end, we propose a novel method for learning multidimensional spatial attention for robust nighttime visual tracking, which is developed over a spatial channel transformer based low light enhancer (SCT), named MSA-SCT. First, a novel multidimensional spatial attention (MSA) is designed. Additional reliable feature responses are generated by aggregating channel and multi-scale spatial information, thus making the model more adaptable to illumination conditions and noise levels in different regions of the image. Second, with optimized skip connections, the effects of redundant information and noise can be limited, which is more useful for the propagation of fine detail features in nighttime images from low to high level features and improves the enhancement effect. Finally, the tracker with enhancers was tested on multiple tracking benchmarks to fully demonstrate the effectiveness and superiority of MSA-SCT.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524166","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

A Recurrent Spatio-Temporal Graph Neural Network Based on Latent Time Graph for Multi-Channel Time Series Forecasting 基于潜在时间图的循环时空图神经网络用于多通道时间序列预测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-14 DOI: 10.1109/LSP.2024.3479917

Linzhi Li;Xiaofeng Zhou;Guoliang Hu;Shuai Li;Dongni Jia

{"title":"A Recurrent Spatio-Temporal Graph Neural Network Based on Latent Time Graph for Multi-Channel Time Series Forecasting","authors":"Linzhi Li;Xiaofeng Zhou;Guoliang Hu;Shuai Li;Dongni Jia","doi":"10.1109/LSP.2024.3479917","DOIUrl":"https://doi.org/10.1109/LSP.2024.3479917","url":null,"abstract":"With the advancement of technology, the field of multi-channel time series forecasting has emerged as a focal point of research. In this context, spatio-temporal graph neural networks have attracted significant interest due to their outstanding performance. An established approach involves integrating graph convolutional networks into recurrent neural networks. However, this approach faces difficulties in capturing dynamic spatial correlations and discerning the correlation of multi-channel time series signals. Another major problem is that the discrete time interval of recurrent neural networks limits the accuracy of spatio-temporal prediction. To address these challenges, we propose a continuous spatio-temporal framework, termed Recurrent Spatio-Temporal Graph Neural Network based on Latent Time Graph (RST-LTG). RST-LTG incorporates adaptive graph convolution networks with a time embedding generator to construct a latent time graph, which subtly captures evolving spatial characteristics by aggregating spatial information across multiple time steps. Additionally, to improve the accuracy of continuous time modeling, we introduce a gate enhanced neural ordinary differential equation that effectively integrates information across multiple scales. Empirical results on four publicly available datasets demonstrate that the RST-LTG model outperforms 19 competing methods in terms of accuracy.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452677","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