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

Exploitation Of Single-Channel Space-Borne SAR Data for Ship Targets Imaging and Motion Parameters Estimation 单通道星载SAR数据在舰船目标成像和运动参数估计中的应用

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193026

A. Testa, D. Pastina, M. Zavagli, F. Santi, C. Pratola, M. Corvino

引用次数: 0

A Health Profiling Framework for Children Leveraging Multimodal Learning Based on Ambient Sensor Signals 基于环境传感器信号的儿童多模式学习健康分析框架

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10192968

Zhihan Jiang, Cong Xie, Edith C. H. Ngai

{"title":"A Health Profiling Framework for Children Leveraging Multimodal Learning Based on Ambient Sensor Signals","authors":"Zhihan Jiang, Cong Xie, Edith C. H. Ngai","doi":"10.1109/ICASSPW59220.2023.10192968","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10192968","url":null,"abstract":"Traditional methods for health profiling are usually expensive and require specialized expertise. The growing prevalence and development of wearable devices have made it feasible to collect ambient sensor signals, providing us with new opportunities to profile children’s health in a cost-effective and comprehensive manner. Inspired by recent works in multimodal learning, we propose a health profiling framework for children. First, we extract context and motion patterns from their personal and family characteristics and acceleration signals. Then, context and motion embeddings are generated by two encoders and input into a lightweight neural network to profile children’s health from the perspectives of physical activity intensity, physical functioning, health confidence, psychosocial functioning, resilience, and connectedness. We evaluate the proposed method on real-world datasets, and the results show its outstanding performance. Specifically, the context pattern is effective in profiling children’s health, while the motion pattern is significantly effective in assessing children’s physical activity intensity.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","volume":"140 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":"127275741","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

Towards an FPGA Implementation of IOT-Based Multi-Modal Hearing AID System 基于物联网的多模态助听器系统的FPGA实现

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10192936

Godwin Enemali, A. Bishnu, T. Ratnarajah, T. Arslan

引用次数: 0

Automatic Alignment Between Sign Language Videos And Motion Capture Data: A Motion Energy-Based Approach 手语视频和动作捕捉数据之间的自动对齐:一种基于运动能量的方法

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193528

Fabrizio Nunnari, Mina Ameli, Shailesh Mishra

引用次数: 0

Enabling Large-Scale Probabilistic Seizure Detection with a Tensor-Network Kalman Filter for LS-SVM 基于LS-SVM的张量网络卡尔曼滤波实现大规模概率癫痫检测

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193615

S.J.S. de Rooij, K. Batselier, B. Hunyadi

{"title":"Enabling Large-Scale Probabilistic Seizure Detection with a Tensor-Network Kalman Filter for LS-SVM","authors":"S.J.S. de Rooij, K. Batselier, B. Hunyadi","doi":"10.1109/ICASSPW59220.2023.10193615","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10193615","url":null,"abstract":"Recent advancements in wearable EEG devices have highlighted the importance of accurate seizure detection algorithms, yet the ever-increasing size of the generated datasets poses a significant challenge to existing seizure detection methods based on kernel machines. Typically, this problem is mitigated by significantly undersampling the majority class, but in practice, these methods tend to suffer from too many false alarms. Recent works have proposed tensor networks to enable large-scale classification with kernel machines. In this paper, we explore the use of a probabilistic tensor method, the tensor-network Kalman filter for LS-SVMs (TNKF-LSSVM), for seizure detection, as we hypothesize that using more data will improve the detection performance. We show that the TNKF-LSSVM performs comparably to a regular LSSVM in detecting seizures when both are trained on the same dataset. Additionally, the TNKF-LSSVM can provide meaningful uncertainty quantification, and it is able to handle large-scale datasets beyond the capabilities of the LS-SVM (i.e., $N gt 10 ^{5})$. However, for the presented model configuration detection performance does not seem to improve with more input data.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","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":"125568594","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

Air-To-Ground Communications Beyond 5G: The Formation Control of UAV Swarm 超越5G的空对地通信:无人机群的编队控制

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193143

Xiao Fan, Peiran Wu, M. Xia

引用次数: 0

On The Complexity of Non-Coherent Acquisition of Chirp Spread Spectrum Signals 啁啾扩频信号非相干采集的复杂性研究

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193476

D. Egea, J. López-Salcedo, G. Seco-Granados

引用次数: 0

Resource-Efficient Federated Clustering with Past Negatives Pool 具有过去否定池的资源高效联邦聚类

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193449

Runxuan Miao, Erdem Koyuncu

{"title":"Resource-Efficient Federated Clustering with Past Negatives Pool","authors":"Runxuan Miao, Erdem Koyuncu","doi":"10.1109/ICASSPW59220.2023.10193449","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10193449","url":null,"abstract":"Federated learning (FL) provides a global model over data distributed to multiple clients. However, most recent work on FL focuses on supervised learning, and a fully unsupervised federated clustering scheme has remained an open problem. In this context, Contrastive learning (CL) trains distinguishable instance embeddings without labels. However, most CL techniques are restricted to centralized data. In this work, we consider the problem of clustering data that is distributed to multiple clients using FL and CL. We propose a federated clustering framework with a novel past negatives pool (PNP) for intelligently selecting positive and negative samples for CL. PNP benefits FL and CL simultaneously, specifically, alleviating class collision for CL and reducing client-drift in FL. PNP thus provides a higher accuracy for a given constraint on the communication rounds, which makes it suitable for networks with limited communication and computation resources. Numerical results show that the resulting FedPNP scheme achieves superior performance in solving federated clustering problems on benchmark datasets including CIFAR-10 and CIFAR-100, especially in non-iid settings.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","volume":"28 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":"116719957","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

Towards Pose-Invariant Audio-Visual Speech Enhancement in the Wild for Next-Generation Multi-Modal Hearing Aids 面向新一代多模态助听器的姿态不变的野外视听语音增强

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10192961

M. Gogate, K. Dashtipour, Amir Hussain

{"title":"Towards Pose-Invariant Audio-Visual Speech Enhancement in the Wild for Next-Generation Multi-Modal Hearing Aids","authors":"M. Gogate, K. Dashtipour, Amir Hussain","doi":"10.1109/ICASSPW59220.2023.10192961","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10192961","url":null,"abstract":"Classical audio-visual (AV) speech enhancement (SE) and separation methods have been successful at operating under constrained environments; however, the speech quality and intelligibility improvement is significantly reduced in unconstrained real-world environments where variation in pose and illumination are encountered. In this paper, we present a novel privacy-preserving approach for real world unconstrained pose-invariant AV SE and separation that contextually exploits pose-invariant 3D landmark flow features and noisy speech features to selectively suppress unwanted background speech and non-speech noises. In addition, we present a unified architecture that integrates state-of-the-art transformers with temporal convolution neural networks for effective pose-invariant AV SE. The preliminary systematic experimentation on benchmark multi-pose OuluVS2 and LRS3-TED corpora demonstrate that the privacy preserving 3D landmark flow features are effective for pose-invariant SE and separation. In addition, the proposed AV SE model significantly outperforms state-of-the-art audio-only SE model, oracle ideal binary mask, and A-only variant of the proposed model in speaker and noise independent settings.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","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":"124619564","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

Scalable Missing Data Imputation With Graph Neural Networks 基于图神经网络的可伸缩缺失数据输入

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193535

Guillaume Lachaud, Patricia Conde Céspedes, M. Trocan

引用次数: 0