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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liu, Meng Lee, Kong Aik Wang, Longbiao Zhang, Hanyi Zeng, Chang Dang, Jianwu Tianjin University College of Intelligence and Computing Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Astar Institute for Infocomm Research Singapore Singapore Institute of Technology Singapore National Institute of Informatics Tokyo Japan

ISBN: (纸本)9781728163277

Visual speech (i.e., lip motion) is highly related to auditory speech due to the co-occurrence and synchronization in speech production. This paper investigates this correlation and proposes a cross-modal speech co-learning paradigm. The primary motivation of our cross-modal co-learning method is modeling one modality aided by exploiting knowledge from another modality. Specifically, two cross-modal boosters are introduced based on an audio-visual pseudo-siamese structure to learn the modality-transformed correlation. Inside each booster, a max-feature-map embedded Transformer variant is proposed for modality alignment and enhanced feature generation. The network is co-learned both from scratch and with pretrained models. Experimental results on the test scenarios demonstrate that our proposed method achieves around 60% and 20% average relative performance improvement over baseline unimodal and fusion systems, respectively. © 2023 IEEE.

关键词： co-learning cross-modal lip biometrics speaker verification visual speech

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Infusing Hierarchical Guidance into Prompt Tuning: A Parameter-Efficient Framework for Multi-level Implicit Discourse Relation Recognition

arXiv

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arXiv 2024年

作者： Zhao, Haodong He, Ruifang Xiao, Mengnan Xu, Jing College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China

Multi-level implicit discourse relation recognition (MIDRR) aims at identifying hierarchical discourse relations among arguments. Previous methods achieve the promotion through fine-tuning PLMs. However, due to the data scarcity and the task gap, the pre-trained feature space cannot be accurately tuned to the task-specific space, which even aggravates the collapse of the vanilla space. Besides, the comprehension of hierarchical semantics for MIDRR makes the conversion much harder. In this paper, we propose a prompt-based Parameter-Efficient Multilevel IDRR (PEMI) framework to solve the above problems. First, we leverage parameter-efficient prompt tuning to drive the inputted arguments to match the pre-trained space and realize the approximation with few parameters. Furthermore, we propose a hierarchical label refining (HLR) method for the prompt verbalizer to deeply integrate hierarchical guidance into the prompt tuning. Finally, our model achieves comparable results on PDTB 2.0 and 3.0 using about 0.1% trainable parameters compared with baselines and the visualization demonstrates the effectiveness of our HLR method. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Region-Specific Prototype Customization for Weakly Supervised Semantic Segmentation 26

Region-Specific Prototype Customization for Weakly Supervise...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Yu, Ruiguo Zhao, Yihang Yu, Mei Gao, Jie Wang, Chenhan Zhang, Ruixuan Li, Xuewei College of Intelligence and Computing Tianjin University Tianjin300350 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China Tianjin Key Laboratory of Advanced Networking Tianjin300350 China School of Future Technology Tianjin University Tianjin300350 China It Co. Ltd. Tianjin300456 China

ISBN: (纸本)9781643684369

It is well known that weakly supervised semantic segmentation requires only image-level labels for training, which greatly reduces the annotation cost. In recent years, prototype-based approaches, which prove to substantially improve the segmentation performance, have been favored by a wide range of researchers. However, we are surprised to find that there are semantic gaps between different regions within the same object, hindering the optimization of prototypes, so the traditional prototypes can not adequately represent the entire object. Therefore, we propose region-specific prototypes to adaptively describe the regions themselves, which alleviate the effect of semantic gap by separately obtaining prototypes for different regions of an object. In addition, to obtain more representative region-specific prototypes, a plug-and-play Spatially Fused Attention Module is proposed for combining the spatial correlation and the scale correlation of hierarchical features. Extensive experiments are conducted on PASCAL VOC 2012 and MS COCO 2014, and the results show that our method achieves state-of-the-art performance using only image-level labels. © 2023 The Authors.

关键词： Semantic Segmentation

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A Novel Network for Short-Term Wind Speed Prediction: Mitigating Distribution Shift and Feature Loss

A Novel Network for Short-Term Wind Speed Prediction: Mitiga...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mei Yu Shengkang Dong Xuewei Li Zewen Shang Yingzhou Sun Zhiqiang Liu College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Tianjin Key Laboratory of Advanced Networking Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurate wind speed forecasting is essential for mitigating the challenges of wind power grid integration. However, existing wind speed prediction models overlook the distributional shift problem within wind speed series, and this time-varying distribution can significantly impact wind prediction accuracy. In this paper, we propose the Distribution Shift and Feature Decoupling Network (DSFD-Net), which addresses the issue of distributional shifts occurring both within the input series and between the input and predicted series through a distribution matching model and distribution mapping module, respectively. Additionally, we introduce a feature decoupling module to mitigate the feature loss encountered in our work. We conduct extensive experiments on two datasets, and comprehensive experimental results demonstrate that DSFD-Net achieves at least a 4.1% reduction in error metrics compared to other wind speed forecasting models, indicating superior performance.

关键词： Measurement Accuracy Wind speed Predictive models Signal processing Acoustics Forecasting Wind forecasting Speech processing Wind energy integration

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Co-training with Progressive Distribution Alignment and Uncertainty-Interactive Relabeling for Semi-Supervised Domain Adaptive Semantic Segmentation

Co-training with Progressive Distribution Alignment and Unce...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ruiguo Yu Yida Wang Xuewei Li Xuzhou Fu Zijian Zhang Yuan Tian Jie Gao College of Intelligence and Computing Tianjin University China Tianjin Key Laboratory of Cognitive Computing and Application China Tianjin Key Laboratory of Advanced Networking China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Self-training is a strong baseline for semi-supervised domain adaptive semantic segmentation. However, it inevitably introduces biased links between features and concepts in the prediction of certain "hard pixels", which may mislead the generalization of models. We consider these hard pixels to come from two aspects: First, the labels are severely imbalanced and distributed across domains and classes, which may lead to features biased towards source domains and majority classes. Second, the naive threshold filtering pseudo-label methods limit the supervision of hard pixels. To address the above problems, we propose a novel co-training framework with progressive distribution alignment and uncertainty-interactive relabeling strategies. More concretely, a progressive distribution alignment strategy is proposed to match distribution across domains while providing additional supervision for tail class pixels. Additionally, an uncertainty-interactive relabeling strategy is proposed to retain more supervisory information for hard pixels and reduce the overall uncertainty of the pseudo-labels. Experiments on two widely-used benchmarks demonstrate the effectiveness of the proposed PDAUR, achieving state-of-the-art results.

关键词： Adaptation models Uncertainty Filtering Semantic segmentation Semantics Tail Signal processing Predictive models Benchmark testing Speech processing

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Characterising Effective Connectivity Changes in Patients with Hepatic Encephalopathy after Liver Transplantation: A Spectral Dynamic Casual Modeling Study 23

Characterising Effective Connectivity Changes in Patients wi...

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Proceedings of the 2023 4th International Symposium on Artificial Intelligence for Medicine Science

作者： Zhouhanyu Shen Jinghan Ouyang Yue Cheng Xiaodong Zhang Junhai Xu College of Intelligence and Computing Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University China College of Intelligence and Computing Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University China and Department of Radiology Tianjin First Central Hospital China College of Intelligence and Computing Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University China and China Xiongan Group Digital City Technology Co. Ltd China

ISBN: (纸本)9798400708138

Objective: The exact effect of overt hepatic encephalopathy (OHE) on the neural processes involved in cognitive enhancement after liver transplantation (LT) is not currently known and requires additional study. The study aims to probe into whether the causal influences within the default mode network (DMN) are altered in OHE patients after LT using resting state functional MRI (rs-fMRI) *** and Methods: In this study, a total of 21 cirrhotic patients with OHE were included. Each patient underwent rs-fMRI scans both prior to and one month post LT. Four regions of the DMN (medial frontal cortex (MFC), posterior cingulate cortex (PCC), left parietal cortex (LPC), and right parietal cortex (RPC)) were defined for each patient using a group independent component analysis (ICA) approach. Spectral dynamic causal modeling (DCM) was employed to estimate the coupling parameters. The coupling parameters provide insights into the directional and strength of the interconnections between the DMN regions, which represent the causal interactivities among the ***: After one month of LT, the influences from the PCC to RPC, RPC to PCC and from LPC to MFC are markedly ***: The findings indicate that the disrupted causal influences including RPC exerts on PCC, PCC exerts on RPC and LPC exerts on MFC are responsive for cirrhotic patients with OHE to operate LT. And these changes in the effective connectivity have the potential to be utilized as neuroimaging biomarkers, aiding in the assessment of cognitive function restoration post LT in patients with liver cirrhosis.

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OCLNet: Obfuscation feature Contrastive Learning Network for Weakly Supervised Semantic Segmentation on Ultrasound Images

OCLNet: Obfuscation feature Contrastive Learning Network for...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jie Gao Xianzhi Zhang Zijian Zhang Xuewei Li Mei Yu Ruiguo Yu Zhiqiang Liu College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Tianjin Key Laboratory of Advanced Networking Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep learning-based semantic segmentation technology has become a critical tool in assisting doctors with automatic lesion segmentation in medical images. However, the high cost of acquiring large-scale, pixel-level annotations poses a significant challenge, limiting the scalability and application of fully supervised semantic segmentation models. To address this, weakly supervised learning-based semantic segmentation models have emerged as a promising solution. These models can accurately segment lesion regions using only weak annotations, such as image-level or frame-level labels, significantly reducing the annotation burden. This approach has gained substantial attention in current *** various medical imaging modalities, ultrasound imaging stands out as a primary diagnostic tool due to its rapid imaging speed, ease of use, and accessibility. This paper focuses on the study of thyroid ultrasound imaging, aiming to achieve accurate classification of nodule regions. The goal is to provide clinicians with more precise diagnostic information, improving decision-making in thyroid disease diagnosis.

关键词： Ultrasonic imaging Accuracy Annotations Semantic segmentation Scalability Contrastive learning Lesions Speech processing Medical diagnostic imaging Thyroid

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Augmenting Short Enrollment Speech via Synthesis for Target Speaker Extraction

Augmenting Short Enrollment Speech via Synthesis for Target ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Zikang Lin, Jingru Ge, Meng Jiang, Yu Wang, Xiaobao Wang, Longbiao Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Shenzhen China Department of Electrical and Computer and Engineering National University of Singapore Singapore Co. Ltd. Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9798350368741

A high-quality enrollment speech is crucial to target speaker extraction (TSE), since it provides essential cues for identifying the target speaker in the mixture. However, real applications usually only permit a short enrollment speech, e.g. a wakeup word for a mobile device, that provides limited cues. To address this issue, we propose an enrollment augmentation strategy that allows us to enrich the limited enrollment speech with massive text data through speech synthesis. By doing so, the extended enrollment speech contains enhanced speaker timbre and phonetic content which leads to better extraction quality. Furthermore, we propose a training data augmentation strategy to improve the model's robustness and generalization in short enrollment speech scenarios. Experiments on Libri2Mix demonstrate that our proposed strategies bring a significant improvement in extreme scenarios where only 0.5s and 1-word enrollment speech is provided. We also release our code at https://***/HuangZikang-TJU/Aug4TSE. © 2025 IEEE.

关键词： continue speaking data augmentation short enrollment speech synthesis Target speaker extraction

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Research on the classification method of knowledge question intention for Tibetan language curriculum 5

Research on the classification method of knowledge question ...

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5th ACM International Conference on Multimedia in Asia, MMAsia 2023 Workshops

作者： Qie, Yangzhuoma Khysru, Kuntharrgyal Wan, Maji Wei, Jianguo Key Laboratory of Artificial Intelligence Application Technology Qinghai Minzu University Xining China Henan county middle school for Nationalities Henan Qinghai China Key Laboratory of Artificial Intelligence Application Technology State Ethnic Affairs Commission Qinghai Minzu University Xining China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University Tianjin China

ISBN: (纸本)9798400703263

As an important part of the intelligent question-answering system, intention classification has attracted more and more researchers’ attention. Tibetan corpus is different from other common corpora. Its grammatical structure is relatively complex, and there is no large amount of labeled data, which makes the research of intention classification in this field scarce. Given the above problems, this paper takes Tibetan curriculum knowledge as an example and introduces the ZangBert-BiLSTM modeling question intention classification task. Firstly, ZangBert encodes the semantic information in the text sequence. In modeling the semantic information, the function word information is introduced by inserting special flag bits into the sequence. Then, the BiLSTM model is used to model the long-distance dependence information in the semantic information sequence. Finally, these text features are sent to the downstream classifier for intention classification. In order to verify the validity of the question intention classification model of primary school Tibetan curriculum knowledge, the corresponding question intention classification data set is labeled, and good intention classification performance is obtained on the data set, which verifies the validity of the work done in this paper. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Classification (of information)

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Stream Attention Based U-Net for L3DAS23 Challenge 48

Stream Attention Based U-Net for L3DAS23 Challenge

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Honglong Fu, Yanjie Li, Junjie Ge, Meng Wang, Longbiao Qian, Xinyuan Tianjin University Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin China The Chinese University of Hong Kong Shenzhen China Co. Ltd Tianjin China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9781728163277

Machine learning applications of 3D audio are gaining increasing interest in recent years. In this paper, we propose a stream attention based U-Net to remove background noise and reverberation based on ICASSP Signal Processing Grand Challenge 2023: L3DAS23 Challenge1 Audio-only track task1 3D Speech Enhancement. Results show that proposed method achieves superior performance than the official baseline model. © 2023 IEEE.

关键词： 3D Speech enhancement L3DAS23 Challenge

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