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arXiv 2019年

作者： Li, Bo Xu, Kele Feng, Dawei Mi, Haibo Wang, Huaimin Zhu, Jian Beijing University of Posts and Telecommunications Automation Dept. Beijing100876 China National Key Lab of Parallel and Distributed Processing Changsha China National University of Defense Technology Changsha China Department of Linguistics University of Michigan Ann Arbor United States

B-mode ultrasound tongue imaging is widely used in the speech production field. However, efficient interpretation is in a great need for the tongue image sequences. Inspired by the recent success of unsupervised deep learning approach, we explore unsupervised convolutional network architecture for the feature extraction in the ultrasound tongue image, which can be helpful for the clinical linguist and phonetics. By quantitative comparison between different unsupervised feature extraction approaches, the denoising convolutional autoencoder (DCAE)-based method outperforms the other feature extraction methods on the reconstruction task and the 2010 silent speech interface challenge. A Word Error Rate of 6.17% is obtained with DCAE, compared to the state-of-the-art value of 6.45% using Discrete cosine transform as the feature extractor. Our codes are availab.e at https://***/DeePBluE666/Source-code1. Copyright © 2019, The Authors. All rights reserved.

关键词： Feature extraction

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Proceedings - Fifth International Conference on Grid and Coo...

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Proceedings - Fifth International Conference on Grid and Cooperative Computing, GCC 2006 2006年 12-12页

作者： Xiao, Wong Buyya, Rajkumar Liu, Yunhao National Laboratory for Parallel and Distributed Processing Changsha China GRIDS Lab. Dept. of Computer Science and Software Engineering University of Melbourne Australia Dept. of Computer Science Hong Kong University of Science and Technology Hong Kong Hong Kong

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Proceedings - Fifth International Conference on Grid and Coo...

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Proceedings - Fifth International Conference on Grid and Cooperative Computing, GCC 2006 - Workshops 2006年 12-12页

作者： Nong, Xiao Buyya, Rajkumar Liu, Yunhao National Laboratory for Parallel and Distributed Processing Changsha China GRIDS Lab. Dept. of Computer Science and Software Engineering University of Melbourne Australia Dept. of Computer Science Hong Kong University of Science and Technology Hong Kong Hong Kong

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Trust-worth multi-representation learning for audio classification with uncertainty estimation

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The Journal of the Acoustical Society of America 2023年第3_SUPPLEMENT期153卷 A125-A125页

作者： Kele Xu Kang You Ming Feng Boqing Zhu National Key Lab. of Parallel and Distributed Processing (PDL) 107 Yanwachi Changsha 410073 China kelele.xu@*** Tongji Univ. Shanghai China National Key Lab. of Parallel and Distributed Processing (PDL) Changsha China

Multi-view learning has been explored for audio classification tasks, exploiting different representations of audio signals, ranging from MFCC, CQT, to raw signals. The quality of each view may vary for different audio signals, and the appropriate uncertainty quantification for each view has not been fully explored. In this work, we explore a trusted multi-view learning framework for classification tasks in order to fully incorporate different views. Our framework consists of three parallel branches of Transformer architectures (Gammatone spectrogram, log-Mel and CQT) and they are combined using the uncertainty estimation of different branch. In addition to computing the classification probabilities, the uncertainty of each representation can also be obtained using the framework. We firstly calculate the evidence based on feature vectors to obtain the probabilities and the uncertainty of classification problems for Gammatone, log-Mel and CQT branch. By integrating the confidence from each of the different representations using the Dempster–Shafer theory, the classification framework can provide higher accuracy and confidence. To demonstrate the effectiveness of the proposed framework, we conduct the experiments on the GTZAN dataset. The obtained results show that our method can reach the accuracy of 83.0%, which significantly outperforms single representation-based methods while providing uncertainty estimation for different views.

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PPSS: A Privacy-Preserving Supervision Scheme Based on Blockchain

PPSS: A Privacy-Preserving Supervision Scheme Based on Block...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Ming Chen Peichang Shi Xiang Fu Feng Jiang Fei Gao Penghui Ma Jinzhu Kong National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Kylin Software Co.LTD Changsha China Zhejiang Lab Hangzhou China

In JointCloud Computing, multi-party participation introduces complexity and uncertainty. For all participants in JointCloud Computing, both continuous supervision and necessary privacy protection are required. Traditional supervision methods usually adopt the centralized information interaction mode, which has such defects as collusion of interests, single point of failure, privacy disclosure, etc. Building a decentralized supervision mechanism has become a new research direction. In this paper, we propose PPSS, a privacy-preserving supervision scheme based on blockchain, which decentralizes the supervision of the participants in JointCloud Computing, and combines the “double encryptions” and “threshold encryption” technologies to provide privacy protection. While making full use of the decentralization of the blockchain, a committee is established to carry out the analysis and decision-making tasks in terms of supervision and privacy protection. Experimental results indicate that PPSS can balance performance and security by reasonably configuring the committee.

关键词： Privacy Uncertainty Decision making Buildings Reliability engineering Blockchains Complexity theory

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Underwater acoustic classification using masked modeling-based swin transformer

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The Journal of the Acoustical Society of America 2022年第4_SUPPLEMENT期152卷 A296-A296页

作者： Kang you Kele Xu Ming Feng Boqing Zhu Tonguing Univ. Shanghai China National Key Lab. of Parallel and Distributed Processing (PDL) Changsha China 107 Yanwachi Changsha 410073 China kelele.xu@*** National Key Lab. of Parallel and Distributed Processing (PDL) Changsha China Changsha China

Underwater acoustic classification is a challenging task due to complex background noise and complicated sound propagation patterns. How to represent the signals is important for the classification task. In this paper, we propose a novel representation learning method for the underwater acoustic signals, leveraging the mask modeling-based self-supervised learning paradigm. Specifically, we first explore modifying the Swin Transformer architecture to learn general representation for the audio signals, accompanied with random masking on the log-mel spectrogram. The main goal of the pretext task is to predict the masked parts of Log-mel spectrogram and the gamma-stone spectrogram, so that the model can not only learn the local and global features but also learn complementary information. For downstream task, we utilize the lab.lled datasets to fine-tune the pre-trained model. On DeepShip datasets which consist of 47 hand 4 minof ship sounds in four categories, our model achieves state-of-the-art performance compared with competitive approaches. Our method obtains a classification accuracy of 78.03%, which is better than the separable convolution autoencoder (SCAE) and using the constant-Q transform spectrogram. This work demonstrates the potential of the masked modeling based self-supervised learning for understanding and interpretation of underwater acoustic signals.

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Quantification of transducer misalignment in ultrasound tongue imaging

arXiv

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arXiv 2020年

作者： Csapó, Tamás Gábor Xu, Kele Department of Telecommunications and Media Informatics Budapest University of Technology and Economics Budapest Hungary MTA-ELTE Lendület Lingual Articulation Research Group Budapest Hungary National Key Lab of Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computer National University of Defense Technology Changsha China

In speech production research, different imaging modalities have been employed to obtain accurate information about the movement and shaping of the vocal tract. Ultrasound is an affordable and non-invasive imaging modality with relatively high temporal and spatial resolution to study the dynamic behavior of tongue during speech production. However, a longstanding problem for ultrasound tongue imaging is the transducer misalignment during longer data recording sessions. In this paper, we propose a simple, yet effective, misalignment quantification approach. The analysis employs MSE distance and two similarity measurement metrics to identify the relative displacement between the chin and the transducer. We visualize these measures as a function of the timestamp of the utterances. Extensive experiments are conducted on a Hungarian and Scottish English child dataset. The results suggest that large values of Mean Square Error (MSE) and small values of Structural Similarity Index (SSIM) and Complex Wavelet SSIM indicate corruptions or issues during the data recordings, which can either be caused by transducer misalignment or lack of gel. Copyright © 2020, The Authors. All rights reserved.

关键词： Ultrasonics

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Feature and Performance Comparison of FaaS Platforms

Feature and Performance Comparison of FaaS Platforms

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Penghui Ma Peichang Shi Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs.

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Atomic-action-based Contrastive Network for Weakly Supervised Temporal Language Grounding

Atomic-action-based Contrastive Network for Weakly Supervise...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Hongzhou Wu Yifan Lyu Xingyu Shen Xuechen Zhao Mengzhu Wang Xiang Zhang Zhigang Luo Parallel and Distributed Processing Lab National University of Defense Technology Changsha China Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China School of Computer National University of Defense Technology Changsha China Institute for Quantum Information and the State Key Lab of High Performance Computing National University of Defense Technology Changsha China

As one knows, an event often consists of several actions while each action is atomic. Inspired by this insight, we propose a novel framework named Atomic-action-based Contrastive Network model (ACN) for weakly supervised temporal language grounding task to localize the query-related event moment in an untrimmed video, without access to any temporal annotations. Specifically, ACN first determines the accurate moment boundary of each action in a query-agnostic way. This can adequately exploit homogeneous visual cues while impeding the heterogeneity of the query from hurting the atomicity of visual action, i.e., action boundary. To effectively localize the query-related event, we seek the discriminative words in the given query, and explore a composite-grained contrastive module to retrieve those corresponding atomic actions in the common latent space across modalities. This boosts feature discrimination of visual event segment to remove irrelevant action video segments. Experiments on two popular datasets show the efficacy of our model.

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Convolutional neural network-based age estimation using B-mode ultrasound tongue image

arXiv

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arXiv 2021年

作者： Xu, Kele Csapó, Tamás Gábor Feng, Ming National Key Lab of Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computer National University of Defense Technology Changsha China Department of Telecommunications and Media Informatics Budapest University of Technology and Economics Budapest Hungary MTA-ELTE Lendület Lingual Articulation Research Group Budapest Hungary Tongji University Shanghai China

Ultrasound tongue imaging is widely used for speech production research, and it has attracted increasing attention as its potential applications seem to be evident in many different fields, such as the visual biofeedback tool for second language acquisition and silent speech interface. Unlike previous studies, here we explore the feasibility of age estimation using the ultrasound tongue image of the speakers. Motivated by the success of deep learning, this paper leverages deep learning on this task. We train a deep convolutional neural network model on the UltraSuite dataset. The deep model achieves mean absolute error (MAE) of 2.03 for the data from typically developing children, while MAE is 4.87 for the data from the children with speech sound disorders, which suggest that age estimation using ultrasound is more challenging for the children with speech sound disorder. The developed method can be used a tool to evaluate the performance of speech therapy sessions. It is also worthwhile to notice that, although we leverage the ultrasound tongue imaging for our study, the proposed methods may also be extended to other imaging modalities (e.g. MRI) to assist the studies on speech production. © 2021, CC BY-NC-SA.

关键词： Ultrasonics

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