2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)最新文献_第7页

Cryptosentiment: A Dataset and Baseline for Sentiment-Aware Deep Reinforcement Learning for Financial Trading Cryptosentiment:面向金融交易的情绪感知深度强化学习的数据集和基线

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193330

Loukia Avramelou, P. Nousi, N. Passalis, S. Doropoulos, A. Tefas

引用次数: 2

Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals 基于脑电图和外周生理信号的超复杂多模态情绪识别

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193329

E. Lopez, Eleonora Chiarantano, Eleonora Grassucci, D. Comminiello

{"title":"Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals","authors":"E. Lopez, Eleonora Chiarantano, Eleonora Grassucci, D. Comminiello","doi":"10.1109/ICASSPW59220.2023.10193329","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10193329","url":null,"abstract":"Multimodal emotion recognition from physiological signals is receiving an increasing amount of attention due to the impossibility to control them at will unlike behavioral reactions, thus providing more reliable information. Existing deep learning-based methods still rely on extracted handcrafted features, not taking full advantage of the learning ability of neural networks, and often adopt a single-modality approach, while human emotions are inherently expressed in a multimodal way. In this paper, we propose a hypercomplex multimodal network equipped with a novel fusion module comprising parameterized hypercomplex multiplications. Indeed, by operating in a hypercomplex domain the operations follow algebraic rules which allow to model latent relations among learned feature dimensions for a more effective fusion step. We perform classification of valence and arousal from electroencephalogram (EEG) and peripheral physiological signals, employing the publicly available database MAHNOB-HCI surpassing a multimodal state-of-the-art network. The code of our work is freely available at https://github.com/ispamm/MHyEEG.","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":"128534249","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 Individualised Speech Enhancement: An SNR Preference Learning System for Multi-Modal Hearing Aids 面向个性化语音增强:多模态助听器的信噪比偏好学习系统

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193122

Jasper Kirton-Wingate, Shafique Ahmed, M. Gogate, Yu-sheng Tsao, Amir Hussain

{"title":"Towards Individualised Speech Enhancement: An SNR Preference Learning System for Multi-Modal Hearing Aids","authors":"Jasper Kirton-Wingate, Shafique Ahmed, M. Gogate, Yu-sheng Tsao, Amir Hussain","doi":"10.1109/ICASSPW59220.2023.10193122","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10193122","url":null,"abstract":"Since the advent of deep learning (DL), speech enhancement (SE) models have performed well under a variety of noise conditions. However, such systems may still introduce sonic artefacts, sound unnatural, and restrict the ability for a user to hear ambient sound which may be of importance. Hearing Aid (HA) users may wish to customise their SE systems to suit their personal preferences and day-to-day lifestyle. In this paper, we introduce a preference learning based SE (PLSE) model for future multi-modal HAs that can contextually exploit audio and visual information to improve listening comfort (LC). The proposed system estimates the Signal-to-noise ratio (SNR) as a basic objective speech quality measure which quantifies the relative amount of background noise present in speech, and directly correlates to the intelligibility of the signal. This is used alongside a preference elicitation framework which learns a predictive function to determine the target SNR. The system is novel, scaling the output of an AudioVisual (AV) DL-based SE model to provide HA users with individualised SE. Preliminary results support the hypothesis of improving the overall subjective LC, without significantly impeding the speech intelligibility.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","volume":"16 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":"129338536","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

Energy-Efficient UAV Trajectories: Simulation vs Emulation 节能无人机轨迹:仿真vs仿真

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193652

N. Babu, Kimon Karathanasopoulos, G. Vardoulias, C. Papadias

引用次数: 0

Improved Calibration Method For CML Humidity Retrievals Over Complex Terrain 复杂地形CML湿度反演的改进定标方法

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193335

Y. Rubin, P. Alpert

引用次数: 0

Learning Multi-Rate Vector Quantization for Remote Deep Inference

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193526

M. Malka, Shai Ginzach, Nir Shlezinger

引用次数: 0

Multi-Modal Deep Learning on Imaging Genetics for Schizophrenia Classification 影像遗传学多模态深度学习用于精神分裂症分类

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193352

Ayush Kanyal, S. Kandula, Vince D. Calhoun, Dong Hye Ye

{"title":"Multi-Modal Deep Learning on Imaging Genetics for Schizophrenia Classification","authors":"Ayush Kanyal, S. Kandula, Vince D. Calhoun, Dong Hye Ye","doi":"10.1109/ICASSPW59220.2023.10193352","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10193352","url":null,"abstract":"Schizophrenia (SZ) is a severe, chronic mental condition that impacts one’s capacity to think, act, and interact with others. It has been established that SZ patients have morphological changes in their brains, along with decreased hippocampal and thalamic volume. Also, it is known that patients with SZ have irregular functional brain connectivity. Furthermore, because SZ is a genetic illness, genetic markers such as single nucleotide polymorphisms (SNP) can be useful to characterize SZ patients. We propose an automatic method to detect changes in SZ patients’ brains considering its heterogeneous multi-modal nature. We present a novel deep-learning method to classify SZ subjects with morphological features from structural MRI (sMRI), brain connectivity features from functional MRI (fMRI), and genetic features from SNPs. For sMRI, we used a pre-trained DenseNet to extract convolutional features which encode the morphological changes induced by SZ. For fMRI, we choose the important connections in functional network connection (FNC) matrix by applying layer-wise relevance propagation (LRP). We also detect SZ-linked SNPs using LRP on a pre-trained 1-dimensional convolutional neural network. Combined features from these three modalities are then fed to an extreme gradient boosting (XGBoost) tree classifier for SZ diagnosis. The experiments using the clinical dataset have shown that our multi-modal approach significantly improved SZ classification accuracy compared with uni-modal deep learning methods.","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":"130163404","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

Multi-Task Learning For Radar Signal Characterisation 雷达信号表征的多任务学习

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193318

Z. Huang, Akila Pemasiri, S. Denman, C. Fookes, Terrence Martin

引用次数: 0

Optimal Sparse MIMO Transceiver Design for Joint Automotive Sensing and Communications 面向汽车传感与通信的最优稀疏MIMO收发器设计

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193486

Weitong Zhai, Xiangrong Wang, Xianghua Wang, M. Amin, T. Shan

{"title":"Optimal Sparse MIMO Transceiver Design for Joint Automotive Sensing and Communications","authors":"Weitong Zhai, Xiangrong Wang, Xianghua Wang, M. Amin, T. Shan","doi":"10.1109/ICASSPW59220.2023.10193486","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10193486","url":null,"abstract":"Joint automotive sensing and communication assisted by optimal sparse MIMO transceiver design is a promising technology for autonomous driving as it reduces hardware cost while preserving high angular resolution. In this paper, we propose to co-design a shared sparse MIMO transceiver within the paradigm of joint sensing and communication (JSAC). Antenna selection is performed to minimize the Cramer–Rao bound (CRB) for accurate tracking with enhanced direction of arrival (DOA) estimation. Meanwhile, the spatial precoding matrix for communications, which exhibits the same sparsity structure with the shared transmitter for automotive sensing, is optimized to deliver a desired quality of service. A solution of this problem requires the application of a series of convex relaxation strategies to transform the resultant non-convex co-design problem into a convex form. The fractional inequality is transformed into the denominator inequality with a constrained numerator and reweighted l1-norm minimization is utilized to promote binary sparsity. Simulations are provided to demonstrate the effectiveness of the optimal sparse MIMO transceiver obtained by the proposed method.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","volume":"42 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":"127664674","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

Lowbit Neural Network Quantization for Speaker Verification 说话人验证的低比特神经网络量化

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193337

Haoyu Wang, Bei Liu, Yifei Wu, Zhengyang Chen, Y. Qian

{"title":"Lowbit Neural Network Quantization for Speaker Verification","authors":"Haoyu Wang, Bei Liu, Yifei Wu, Zhengyang Chen, Y. Qian","doi":"10.1109/ICASSPW59220.2023.10193337","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10193337","url":null,"abstract":"With the continuous development of deep neural networks (DNN) in recent years, the performance of speaker verification systems has been significantly improved with the application of Deeper ResNet architectures. However, these deeper models occupy more storage space in application. In this paper, we adopt Alternate Direction Methods of Multipliers (ADMM) to realize low-bit quantization on the original ResNets. Our goal is to explore the maximal quantization compression without evident degradation in model performance. We implement different uniform quantization for each convolution layer to achieve mixed precision quantization of the entire model. Moreover, the impact of batch normalization layers in ADMM training and layer sensibility to quantization are explored. In our experiments, the 8 bit quantized ResNetl52 achieved comparable results to the full-precision one on Voxceleb 1, with only 45% of original model size. Besides, we find that shallow convolution layers are more sensitive to quantization. In addition, experimental results indicate that the model performance will be severely degraded if batch normalization layers are integrated into the convolution layer before the quantization training starts.","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":"130476752","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