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USED: Universal Speaker Extraction and Diarization

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arXiv 2023年

作者： Ao, Junyi Yıldırım, Mehmet Sinan Tao, Ruijie Ge, Meng Wang, Shuai Qian, Yanmin Li, Haizhou Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore Singapore119077 Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore117549 Singapore Shenzhen Research Institute of Big Data Shenzhen518172 China Auditory Cognition and Computational Acoustics Lab Department of Computer Science and Engineering China MoE Key Laboratory of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China

Speaker extraction and diarization are two enabling techniques for real-world speech applications. Speaker extraction aims to extract a target speaker’s voice from a speech mixture, while speaker diarization demarcates speech segments by speaker, annotating ‘who spoke when’. Previous studies have typically treated the two tasks independently. In practical applications, it is more meaningful to have knowledge about ‘who spoke what and when’, which is captured by the two tasks. The two tasks share a similar objective of disentangling speakers. Speaker extraction operates in the frequency domain, whereas diarization is in the temporal domain. It is logical to believe that speaker activities obtained from speaker diarization can benefit speaker extraction, while the extracted speech offers more accurate speaker activity detection than the speech mixture. In this paper, we propose a unified model called Universal Speaker Extraction and Diarization (USED) to address output inconsistency and scenario mismatch issues. It is designed to manage speech mixtures with varying overlap ratios and variable number of speakers. We show that the USED model significantly outperforms the competitive baselines for speaker extraction and diarization tasks on LibriMix and SparseLibriMix datasets. We further validate the diarization performance on CALLHOME, a dataset based on real recordings, and experimental results indicate that our model surpasses recently proposed approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Frequency domain analysis

Faster PAC Learning and Smaller Coresets via Smoothed Analysis

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arXiv 2020年

作者： Maalouf, Alaa Tukan, Murad Jubran, Ibrahim Feldman, Dan Robotics and Big Data Lab Department of Computer Science University of Haifa Haifa Israel

PAC-learning usually aims to compute a small subset (Ε-sample/net) from n items, that provably approximates a given loss function for every query (model, classifier, hypothesis) from a given set of queries, up to an additive error Ε ∈ (0, 1). Coresets generalize this idea to support multiplicative error 1 ± Ε. Inspired by smoothed analysis, we suggest a natural generalization: approximate the average (instead of the worst-case) error over the queries, in the hope of getting smaller subsets. The dependency between errors of different queries implies that we may no longer apply the Chernoff-Hoeffding inequality for a fixed query, and then use the VC-dimension or union bound. This paper provides deterministic and randomized algorithms for computing such coresets and Ε-samples of size independent of n, for any finite set of queries and loss function. Example applications include new and improved coreset constructions for e.g. streaming vector summarization [ICML’17] and k-PCA [NIPS’16]. Experimental results with open source code are provided. Copyright © 2020, The Authors. All rights reserved.

关键词： Errors

Coresets for Near-Convex Functions

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arXiv 2020年

作者： Tukan, Murad Maalouf, Alaa Feldman, Dan Robotics and Big Data Lab Department of Computer Science University of Haifa Haifa Israel

Coreset is usually a small weighted subset of n input points in Rd, that provably approximates their loss function for a given set of queries (models, classifiers, etc.). Coresets become increasingly common in machine learning since existing heuristics or inefficient algorithms may be improved by running them possibly many times on the small coreset that can be maintained for streaming distributed data. Coresets can be obtained by sensitivity (importance) sampling, where its size is proportional to the total sum of sensitivities. Unfortunately, computing the sensitivity of each point is problem dependent and may be harder to compute than the original optimization problem at hand. We suggest a generic framework for computing sensitivities (and thus coresets) for wide family of loss functions which we call near-convex functions. This is by suggesting the f-SVD factorization that generalizes the SVD factorization of matrices to functions. Example applications include coresets that are either new or significantly improves previous results, such as SVM, Logistic regression, M-estimators, and `z-regression. Experimental results and open source are also provided. Copyright © 2020, The Authors. All rights reserved.

关键词： Functions

Coresets for near-convex functions 20

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Coresets for near-convex functions

Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Murad Tukan Alaa Maalouf Dan Feldman The Robotics and Big Data Lab Department of Computer Science University of Haifa Haifa Israel

ISBN: (纸本)9781713829546

Coreset is usually a small weighted subset of n input points in ℝd, that provably approximates their loss function for a given set of queries (models, classifiers, etc.). Coresets become increasingly common in machine learning since existing heuristics or inefficient algorithms may be improved by running them possibly many times on the small coreset that can be maintained for streaming distributed data. Coresets can be obtained by sensitivity (importance) sampling, where its size is proportional to the total sum of sensitivities. Unfortunately, computing the sensitivity of each point is problem dependent and may be harder to compute than the original optimization problem at hand. We suggest a generic framework for computing sensitivities (and thus coresets) for wide family of loss functions which we call near-convex functions. This is by suggesting the ƒ-SVD factorization that generalizes the SVD factorization of matrices to functions. Example applications include coresets that are either new or significantly improves previous results, such as SVM, Logistic regression, M-estimators, and ℓz-regression. Experimental results and open source are also provided.

关键词：

Retrieval-Augmented and Knowledge-Grounded Language Models for Faithful Clinical Medicine

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arXiv 2022年

作者： Liu, Fenglin Yang, Bang You, Chenyu Wu, Xian Ge, Shen Liu, Zhangdaihong Sun, Xu Yang, Yang Clifton, David A. Department of Engineering Science University of Oxford United Kingdom School of ECE Peking University China Department of Electrical Engineering Yale University United States Tencent JARVIS Lab China Oxford-Suzhou Centre for Advanced Research China MOE Key Lab of Computational Linguistics School of Computer Science Peking University China School of Public Health Shanghai Jiao Tong University School of Medicine China

Language models (LMs), including large language models (such as ChatGPT), have the potential to assist clinicians in generating various clinical notes. However, LMs are prone to produce "hallucinations", i.e., generated content that is not aligned with facts and knowledge. In this paper, we propose the Re3Writer method with retrieval-augmented generation and knowledge-grounded reasoning to enable LMs to generate faithful clinical texts. We demonstrate the effectiveness of our method in generating patient discharge instructions. It requires the LMs not to only understand the patients’ long clinical documents, i.e., the health records during hospitalization, but also to generate critical instructional information provided both to carers and to the patient at the time of discharge. The proposed Re3Writer imitates the working patterns of physicians to first retrieve related working experience from historical instructions written by physicians, then reason related medical knowledge. Finally, it refines the retrieved working experience and reasoned medical knowledge to extract useful information, which is used to generate the discharge instructions for previously-unseen patients. Our experiments show that, using our method, the performance of five representative LMs can be substantially boosted across all metrics. Meanwhile, we show results from human evaluations to measure the effectiveness in terms of fluency, faithfulness, and comprehensiveness. © 2022, CC BY-NC-SA.

关键词： Electronic health record

Enhancing Motor Imagery EEG Signal Classification with Simplified GoogLeNet

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Enhancing Motor Imagery EEG Signal Classification with Simpl...

International Winter Workshop on Brain-computer Interface (BCI)

作者： Lu Wang Junkongshuai Wang Bo Wen Wei Mu Lusheng Liu Jiaguan Han Lihua Zhang Jie Jia Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical NeurobiologyAcademy for Engineering & Technology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of AI & Robotics Institute of Meta-MedicalFudan University Shanghai China Ji Hua Laboratory Guangdong Province Foshan China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

The brain-computer interface is a novel tool for interaction between the brain and external devices, and is an important tool at the forefront of international brain science research. Motor imagery does not require external stimulation and is one of the most widely used BCI paradigms, and is now widely used to address the treatment of neurological impairments such as paralysis, stroke, and acromegaly. Electrophysiological source imaging is a technique based on the observation of electromagnetic signals from the cerebral cortex, reflecting the electrophysiological activity of brain nerve cells on the scalp surface of the brain. The wavelet transform, on the other hand, is commonly used for the processing of non-smooth signals and aims to extract excellent signal features. This paper combines these two algorithms as feature extraction methods. In recent years, deep learning has been increasingly combined with MI-EEG-based BCI. GoogLeNet is one of the most classical convolutional neural networks and has made a splash in ImageNet image recognition. However, using GoogLeNet to train the BCI public dataset BCI IV IIa and dataset HGD has shown problems such as long training time, overfitting, and gradient disappearance. In response, this paper proposes an improved and streamlined version of GoogLeNet for training based on this dataset to achieve an overall improvement in accuracy, training time, number of parameters, and other indicators. It is useful for the realistic application of MI-based BCI systems.

关键词： Training Wavelet transforms Surface waves Scalp Pattern classification Electrophysiology Streaming media

Modern deep learning in bioinformatics

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Journal of Molecular Cell Biology 2020年第11期12卷 823-827页

作者： Haoyang Li Shuye Tian Yu Li Qiming Fang Renbo Tan Yijie Pan Chao Huang Ying Xu Xin Gao Cancer Systems Biology Center The China-Japan Union HospitalJilin UniversityChangchun 130033China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Biology Southern University of Science and TechnologyShenzhen 518055China Computational Bioscience Research Center(CBRC) Computer Electrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Ningbo Institute of Information Technology Application Chinese Academy of SciencesNingbo 315040China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensGA 30602USA

Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A number of comprehensive reviews have been published on such applications,ranging from high-level reviews with future perspectives to those mainly serving as tutorials.

关键词： has such she

A Human-Human Physical Interaction System for Training Handwriting

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A Human-Human Physical Interaction System for Training Handw...

IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： S. Buscaglione A. Noccaro C. Provenzale C. Bonsignori E. Ivanova L. Sparaci F. Taffoni E. Burdet D. Formica NEXT: Neurophysiology and Neuroengineering of Human-Technology Interaction Research Unit Università Campus Bio-Medico di Roma Italy Neurorobotics Lab Newcastle University Newcastle Upon Tyne UK Advanced Robotics and Human-Centred Technologies-CREO Laboratory Università Campus Bio-Medico di Roma Italy Department of Bioengineering Imperial College of Science Technology and Medicine UK School of Electric Engineering and Computer Science Queen Mary University of London UK Institute of Cognitive Sciences and Technologies (ISTC) National Research Council of Italy (CNR) Italy

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Handwriting training in childhood is known to influence academic success, foster self-esteem, and contribute to overall development. Previous studies underscored the positive impact of haptic feedback on handwriting learning using one-way communication systems that provided the trainee with haptic feedback based on pre-recorded trainer's movements. In this study, we propose a system involving bidirectional physical coupling (haptic communication) between a trainee and a trainer to exploit the advantages of haptic communication for motor task improvement, proved in tracking tasks. The system, composed of an interactive display and a coupling spring, was tested in a handwriting task performed by a dyad of healthy adults with different cursive handwriting backgrounds. The participants' performance demonstrated the feasibility of using this system for handwriting, opening the way for further studies to assess its benefits in handwriting improvement and learning.

关键词： Training Couplings Tracking Communication systems Biomechatronics Motors Haptic interfaces Springs Robots

A spiking neural network emulating the structure of the oculomotor system requires no learning to control a biomimetic robotic head

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arXiv 2020年

作者： Balachandar, Praveenram Michmizos, Konstantinos P. Computational Brain Lab Department of Computer Science Rutgers University NJ United States

Robotic vision introduces requirements for realtime processing of fast-varying, noisy information in a continuously changing environment. In a real-world environment, convenient assumptions, such as static camera systems and deep learning algorithms devouring high volumes of ideally slightlyvarying data are hard to survive. Leveraging on recent studies on the neural connectome associated with eye movements, we designed a neuromorphic oculomotor controller and placed it at the heart of our in-house biomimetic robotic head prototype. The controller is unique in the sense that (1) all data are encoded and processed by a spiking neural network (SNN), and (2) by mimicking the associated brain areas' connectivity, the SNN required no training to operate. A biologically-constrained Hebbian learning further improved the SNN performance in tracking a moving target. Here, we report the tracking performance of the robotic head and show that the robotic eye kinematics are similar to those reported in human eye studies. This work contributes to our ongoing effort to develop energy-efficient neuromorphic SNN and harness their emerging intelligence to control biomimetic robots with versatility and robustness. Copyright © 2020, The Authors. All rights reserved.

关键词： Neural networks