ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)最新文献

Achievable Error Exponents for Almost Fixed-Length M-Ary Hypothesis Testing 几乎固定长度M-Ary假设检验的可实现误差指数

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10095947

Jun Diao, Lin Zhou, Lin Bai

引用次数: 2

The Role of Memory in Social Learning When Sharing Partial Opinions 分享片面意见时记忆在社会学习中的作用

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10096186

Michele Cirillo, Virginia Bordignon, V. Matta, Ali H. Sayed

引用次数: 1

Interpretable Nonnegative Incoherent Deep Dictionary Learning for FMRI Data Analysis 用于FMRI数据分析的可解释非负非相干深度字典学习

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10095297

Manuel Morante, Jan Østergaard, S. Theodoridis

引用次数: 0

Complementary Learning System Based Intrinsic Reward in Reinforcement Learning 基于内在奖励的互补学习系统在强化学习中的应用

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10095379

Zijian Gao, Kele Xu, Hongda Jia, Tianjiao Wan, Bo Ding, Dawei Feng, Xinjun Mao, Huaimin Wang

{"title":"Complementary Learning System Based Intrinsic Reward in Reinforcement Learning","authors":"Zijian Gao, Kele Xu, Hongda Jia, Tianjiao Wan, Bo Ding, Dawei Feng, Xinjun Mao, Huaimin Wang","doi":"10.1109/ICASSP49357.2023.10095379","DOIUrl":"https://doi.org/10.1109/ICASSP49357.2023.10095379","url":null,"abstract":"Deep reinforcement learning has achieved encouraging performance in many realms. However, one of its primary challenges is the sparsity of extrinsic rewards, which is still far from solved. Complementary learning system theory suggests that effective human learning relies on two complementary learning systems utilizing short-term and long-term memories. Inspired by the fact that humans evaluate curiosity by comparing current observations with historical information, we propose a novel intrinsic reward, namely CLS-IR, which aims to address the problems caused by sparse extrinsic rewards. Specifically, we train a self-supervised predictive model with short-term and long-term memories via exponential moving averages. We employ the information gain between the two memories as the intrinsic reward, which does not incur additional training costs but leads to better exploration. To investigate the effectiveness of CLS-IR, we conduct extensive experimental evaluations; the results demonstrate that CLS-IR can achieve state-of-the-art performance on Atari games and DeepMind Control Suite.","PeriodicalId":113072,"journal":{"name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114670987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learned Kalman Filtering in Latent Space with High-Dimensional Data 高维数据隐空间学习卡尔曼滤波

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10096006

Itay Buchnik, Damiano Steger, Guy Revach, Ruud J. G. van Sloun, T. Routtenberg, Nir Shlezinger

{"title":"Learned Kalman Filtering in Latent Space with High-Dimensional Data","authors":"Itay Buchnik, Damiano Steger, Guy Revach, Ruud J. G. van Sloun, T. Routtenberg, Nir Shlezinger","doi":"10.1109/ICASSP49357.2023.10096006","DOIUrl":"https://doi.org/10.1109/ICASSP49357.2023.10096006","url":null,"abstract":"The Kalman filter (KF) is a widely-used algorithm for tracking dynamical systems that can be faithfully captured by state space (SS) models. The need to fully describe an SS model limits its applicability under complex settings, e.g., when tracking based on visual or graphical data. This challenge can be treated by mapping the measurements into latent features obeying some postulated closed-form SS model, and applying the KF in the latent space. However, the validity of this approximated SS model may constitute a limiting factor. In this work we tackle the challenges associated with tracking from high-dimensional measurements by jointly learning the KF along with the latent space mapping. Our proposed approach combines a learned encoder while tracking in the latent space using the recently proposed data-driven Kalman-Net, and having both modules jointly tuned from data. Our empirical results demonstrate that the proposed approach achieves improved performance over both model-based and data-driven techniques, by learning a surrogate latent representation that most facilitates tracking.","PeriodicalId":113072,"journal":{"name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121811062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Double Compression Detection Based on the De-Blocking Filtering of HEVC Videos 基于去块滤波的HEVC视频双压缩检测

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10095600

Xiangui Kang, Pengcheng Su, Zisheng Huang, Yifang Chen, Jie Wang

{"title":"Double Compression Detection Based on the De-Blocking Filtering of HEVC Videos","authors":"Xiangui Kang, Pengcheng Su, Zisheng Huang, Yifang Chen, Jie Wang","doi":"10.1109/ICASSP49357.2023.10095600","DOIUrl":"https://doi.org/10.1109/ICASSP49357.2023.10095600","url":null,"abstract":"Instead of detecting whether the whole video sequence is double compressed, a frame-level detection result can provide more precise information for video forensic tasks, such as locate tamper point and restore compression history, et al. But the research on frame-level double compression detection is still in its infancy. Therefore we aim to provide a frame-level detection method for HEVC videos in this paper. The relocated I(RI) frame belongs to different GOP groups from its reference frame at the first compression and may cause more severe blocking effects than other types of P frames. Hence, this paper proposes an algorithm based on the de-blocking filtering feature mode to detect RI frames in the double compressed HEVC videos with shifted GOP structure. Firstly, the abnormal traces of the de-blocking filtering parameters, such as boundary strength, filtering switch and filtering mode, in the RI frame are analyzed. Then, the de-blocking filtering feature is constructed by mapping the different combinations of the three parameters into a single numerical value. Finally, the de-blocking filtering feature of the video clips is adopted as the input of the proposed mini_MobileViT network, which is the combination of Convolutional Neural Network (CN-N) and Transformer, to learn spatial and temporal representations to identify the RI frames. Experimental results demonstrate the advantages of the proposed algorithm in detecting RI frames in the double compressed HEVC videos. Compared with the state-of-art work He’s method, the proposed method has a 1.72% improvement in the accuracy of detecting RI frames. Compared with other traditional methods, there is a more than 10% improvement.","PeriodicalId":113072,"journal":{"name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117206904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Order Reduction of Multi-Channel FIR Filters by Balanced Truncation 多通道FIR滤波器的平衡截断降阶

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10096830

Florian Hilgemann, P. Jax

{"title":"Order Reduction of Multi-Channel FIR Filters by Balanced Truncation","authors":"Florian Hilgemann, P. Jax","doi":"10.1109/ICASSP49357.2023.10096830","DOIUrl":"https://doi.org/10.1109/ICASSP49357.2023.10096830","url":null,"abstract":"Modern digital signal processing increasingly leverages multi-channel systems and algorithms to tackle emerging challenges, e.g., in beamforming, crosstalk cancellation (CTC) or active noise control. Commonly, the modeling, design and simulation of these systems involves the use of multiple-input multiple-output (MIMO) finite impulse response (FIR) filters. To increase the practical feasibility, these potentially resource-intensive FIR filters can be approximated by infinite impulse response (IIR) filters of lower order. Balanced truncation (BT) can be used for the approximation, but is not used as widely in these areas. A possible reason for this is the state space formulation of the algorithm, which does not scale well for general systems of high order. In this contribution, we present a BT algorithm which is specifically tailored to the approximation of MIMO FIR filters which avoids most of the usually needed computations. This algorithm can be applied to a number of current problems and we study two examples in detail. We find the numerical accuracy to be satisfactory even with high-order systems.","PeriodicalId":113072,"journal":{"name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117272331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Sentiment and Syntactic-Aware Graph Convolutional Network for Aspect-Level Sentiment Classification 面向方面级情感分类的情感和句法感知图卷积网络

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10096326

Yuxin Yang, Xia Sun, Qiang Lu, R. Sutcliffe, Jun Feng

{"title":"A Sentiment and Syntactic-Aware Graph Convolutional Network for Aspect-Level Sentiment Classification","authors":"Yuxin Yang, Xia Sun, Qiang Lu, R. Sutcliffe, Jun Feng","doi":"10.1109/ICASSP49357.2023.10096326","DOIUrl":"https://doi.org/10.1109/ICASSP49357.2023.10096326","url":null,"abstract":"Aspect-level sentiment classification (ASC) is a significant problem in fine-grained sentiment analysis, which automatically predicts the sentiment polarity of a given aspect in a sentence. Dependency tree-based graph convolutional networks have been widely studied for their ability to effectively capture the dependencies of aspect words with other words. However, constructing more accurate syntactic trees by introducing external knowledge has limited improvement on ungrammatical informal texts and has led to over-parameterization of the model. To alleviate this problem, we propose a sentiment and syntactic-aware graph convolutional network (SaS-GCN) that combines syntactic and sentiment relations. We use an attention mechanism and the Sparsemax activation function to construct a sparse sentiment-dependent graph. Compared with existing methods that use LSTM or CNN to obtain semantics from text directly, this graph, combined with a GCN, contains more semantic features. Moreover, we redesign the network structure of GCN, calling it EN-GCN, to make it sensitive to node dimensional features and hence to have a strong feature mining ability. The experimental results indicate that our model outperforms state-of-the-art methods. In particular, when evaluated on the Rest15 and Rest16 datasets, the F1 scores of the proposed lightweight model are 4.15% and 3.77% better than BERT respectively.","PeriodicalId":113072,"journal":{"name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121050696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Lightweight Convolutional Neural Network using Feature Filtering Module 基于特征滤波模块的轻量级卷积神经网络

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/icassp49357.2023.10096547

Nan Jing, Yu Zhang

引用次数: 0

Image Source Method Based on the Directional Impulse Responses 基于定向脉冲响应的图像源方法

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10095916

Jiarui Wang, P. Samarasinghe, T. Abhayapala, J. Zhang

引用次数: 2