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检索条件"任意字段=International Conference on Signal and Data Processing, ICSDP 2019"

共 2690 条记录，以下是51-60 订阅

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THE DISCRETE COSINE TRANSFORM ON TRIANGLES 44

THE DISCRETE COSINE TRANSFORM ON TRIANGLES

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Seifert, Bastian Hueper, Knut Ansbach Univ Appl Sci Fac Engn Sci Ansbach Germany Univ Wurzburg Inst Math Wurzburg Germany

ISBN: (纸本)9781479981311

The discrete cosine transform is a valuable tool in analysis of data on undirected rectangular grids, like images. In this paper it is shown how one can define an analogue of the discrete cosine transform on triangles. This is done by combining algebraic signal processing theory with a specific kind of multivariate Chebyshev polynomials. Using a multivariate Christoffel-Darboux formula it is shown how to derive an orthogonal version of the transform.

关键词： discret cosine transform algebraic signal processing Christoffel-Darboux formula multivariate Chebyshev polynomials lattice of triangles

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PERFORMANCE ENHANCEMENT OF THE MEASURE-TRANSFORMED MUSIC ALGORITHM VIA MSE BASED OPTIMIZATION 44

PERFORMANCE ENHANCEMENT OF THE MEASURE-TRANSFORMED MUSIC ALG...

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Halay, Nir Todros, Koby Ben Gurion Univ Negev Beer Sheva Israel

ISBN: (纸本)9781479981311

The measure-transformed (MT) MUltiple signal Classification (MUSIC) algorithm is a robust MUSIC generalization that operates by applying a transform to the probability measure (distribution) of the data. In this paper, we first provide an asymptotic mean-squared-error (MSE) performance analysis of the MT-MUSIC algorithm. Under some mild assumptions, we show that the MT-MUSIC estimator is asymptotically normal and unbiased, and obtain an analytic expression for the asymptotic MSE matrix. We then proceed to develop a strongly consistent estimator for the asymptotic MSE matrix that is constructed from the same data samples being used for implementation of the MT-MUSIC. This paves the way for development of a data-driven procedure for optimal selection of the measure transformation parameters that minimizes an empirical estimate of the asymptotic average root MSE (RMSE). Simulation examples illustrate the performance advantage of the proposed MSE based optimization of the MT-MUSIC.

关键词： Array processing DOA estimation probability measure transform robust statistics signal subspace estimation

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FROM LOCAL TO GLOBAL SUBSPACE CLUSTERING FOR IMAGE data 44

FROM LOCAL TO GLOBAL SUBSPACE CLUSTERING FOR IMAGE DATA

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Abdolali, Maryam Rahmati, Mohammad Amirkabir Univ Technol Dept Comp Engn & Informat Technol Tehran Iran

ISBN: (纸本)9781479981311

The subspace clustering problem arises in many applications that involve processing high-dimensional data, i.e. images and videos. In many of these applications, high dimensional data is often well approximated by union of low-dimensional subspaces. This motivated the development of various algorithms to cluster high dimensional data based on the underlying intrinsic low-dimensional subspaces. However, the existing approaches are based on global representation of data whereas this representation can be easily affected by errors, occlusions and severe illumination conditions. Here, we propose a multi-scale approach based on extracting local patches from different scales and then merging the shared information using a weighted scheme based on Grassmann manifolds. This approach not only benefits from the discriminative information from global representation of data but also makes the clustering task more robust using the information from local representations. Numerical results show that the proposed approach significantly outperforms existing subspace clustering algorithms.

关键词： Subspace clustering Grassmann manifold Multi-Scale approximation Multilayer graph

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A Multi-source Based Coupled Tensors Completion Algorithm for Incomplete Traffic data Imputation 11

A Multi-source Based Coupled Tensors Completion Algorithm fo...

引用

11th IEEE international conference on Wireless Communications and signal processing (WCSP)

作者： Zhou, Weiming Zheng, Haifeng Feng, Xinxin Lin, Dong Fuzhou Univ Coll Phys & Informat Engn Fuzhou Fujian Peoples R China

ISBN: (纸本)9781728135557

Missing data is an inevitable and ubiquitous problem in the data-driven Intelligent Transportation System (ITS), which seriously affects the accuracy of urban traffic planning and management. Most existing traffic data processing methods often only exploit the characteristics of single source data. In this paper, we present a novel coupled tensors model by using multi-source traffic data for missing data imputation, and propose a tensor completion algorithm based on a modified CMTF-WOPT(Coupled Matrix and Tensor Factorization-Weighted OP-Timization) algorithm to recover the missing traffic data. We also present extensive simulation results by using real world traffic datasets to evaluate the performance of the proposed algorithm. The simulation results show that the proposed coupled tensor completion algorithm makes a significant improvement on the recovery accuracy compared with existing tensor completion algorithms, especially under high missing rates.

关键词： Intelligent Transportation System data recovery tensor completion multi-source data

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DEEP EMBEDDINGS FOR RARE AUDIO EVENT DETECTION WITH IMBALANCED data 44

DEEP EMBEDDINGS FOR RARE AUDIO EVENT DETECTION WITH IMBALANC...

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Arora, Vipul Sun, Ming Wang, Chao Indian Inst Technol Kanpur Kanpur Uttar Pradesh India Amazon Alexa Cambridge MA USA

ISBN: (纸本)9781479981311

In this paper, we present a method to handle data imbalance for classification with neural networks, and apply it to acoustic event detection (AED) problem. The common approach to tackle data imbalance is to use class-weights in the objective function while training. An existing more sophisticated approach is to map the input to clusters in an embedding space, so that learning is locally balanced by incorporating inter-cluster and inter-class margins. On these lines, we propose a method to learn the embedding using a novel objective function, called triple-header cross entropy. Our scheme integrates in a simple way with back-propagation based training, and is computationally more efficient than general hinge-loss based embedding learning schemes. The empirical evaluation results demonstrate the effectiveness of the proposed method for AED with imbalanced training data.

关键词： data imbalance embedding space acoustic event detection neural networks classification

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USING RFID TECHNOLOGY TO INTRODUCE PROPERTIES OF LMS 44

USING RFID TECHNOLOGY TO INTRODUCE PROPERTIES OF LMS

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Li, Zhe Chen, Xiangyu Deng, Wei Pei, Wenjiang Xia, Yili Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9781479981311

This paper presents a new course design to enhance students' interest in our graduate curriculum of morden digital signal processing (MDSP). The familiar radio frequency identification (RFID) technology is introduced to reveal the fundamentals of the least-mean-square (LMS) algorithm. Students are encouraged to encode their customised data into their respective RFID tags, and to use the software-defined radio (SDR) platform to monitor the inventory process integrated within RFID communication systems. After acquiring the digital signal from the SDR hardware, they need to perform adaptive channel estimation and signal detection to identify the customised data. By applying appropriate mathematical tools in the Matlab programming environment to solve the signal detection problem in two different communication scenarios, students reported to have had a deeper understanding of the signal processing concepts of adaptive filtering techniques in a self-directed manner.

关键词： least-mean-square (LMS) radio frequency identification (RFID) adaptive filters signal processing education

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CMU WILDERNESS MULTILINGUAL SPEECH dataSET 44

CMU WILDERNESS MULTILINGUAL SPEECH DATASET

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Black, Alan W. Carnegie Mellon Univ Language Technol Inst Pittsburgh PA 15213 USA

ISBN: (纸本)9781479981311

This paper describes the CMU Wilderness Multilingual Speech dataset. A dataset of over 700 different languages providing audio, aligned text and word pronunciations. On average each language provides around 20 hours of sentence-lengthed transcriptions. We describe our multi-pass alignment techniques and evaluate the results by building speech synthesizers on the aligned data. Most of the resulting synthesizers are good enough for deployment and use. The tools to do this work are released as open source, and instructions on how to apply such alignment for novel languages are given.

关键词： found speech data multilingual speech synthesis speech recognition

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RESIDUAL INTEGRATION NEURAL NETWORK 44

RESIDUAL INTEGRATION NEURAL NETWORK

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Ouala, Said Pascual, Ananda Fablet, Ronan IMT Atlantique Brest France Lab STICC Brest France UIB CSIC IMEDEA Esporles Spain

ISBN: (纸本)9781479981311

In this work, we investigate residual neural network representations for the identification and forecasting of dynamical systems. We propose a novel architecture that jointly learns the dynamical model and the associated Runge-Kutta integration scheme. We demonstrate the relevance of the proposed architecture with respect to learning-based state-of-the-art approaches in the identification and forecasting of chaotic dynamics when provided with training data with low temporal sampling rates.

关键词： Dynamical systems data-driven models Neural networks Forecasting Runge-Kutta methods

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Mobile User Capacity Analysis of LTE-A Systems Based on Field Measurement data 11

Mobile User Capacity Analysis of LTE-A Systems Based on Fiel...

引用

11th international conference on Wireless Communications and signal processing (WCSP)

作者： Song, Jong Seob Jin, Hu Kwon, Hyeyeon Park, Seungkeun Hanyang Univ Dept Elect & Elect Engn Ansan South Korea Elect & Telecommun Res Inst Daejeon South Korea

ISBN: (纸本)9781728135557

In order to analyze the mobile user capacity supportable by a cell in LTE-A systems, we propose a methodology of combining a mathematical model and field measurement data. One important phenomenon considered by the proposed model is that the informations of resource assignment for the both downlink and uplink are transmitted on the same Physical Downlink Control Channel (PDCCH), i.e., the both downlink and uplink controls share the same PDCCH resource which may induce congestions. We perform field measurement on LTE-A cells to collect measurement data to support the parameters utilized by the proposed model. Through comparing the numerical results and the data from field measurement, we confirm the accuracy of the proposed mathematical model.

关键词： LTE-A mobile traffic user capacity measurement

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TRAINING NEURAL AUDIO CLASSIFIERS WITH FEW data 44

TRAINING NEURAL AUDIO CLASSIFIERS WITH FEW DATA

引用

44th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Pons, Jordi Serra, Joan Serra, Xavier Telefon Res Barcelona Spain Univ Pompeu Fabra Mus Technol Grp Barcelona Spain

ISBN: (纸本)9781479981311

We investigate supervised learning strategies that improve the training of neural network audio classifiers on small annotated collections. In particular, we study whether (i) a naive regularization of the solution space, (ii) prototypical networks, (iii) transfer learning, or (iv) their combination, can foster deep learning models to better leverage a small amount of training examples. To this end, we evaluate (i-iv) for the tasks of acoustic event recognition and acoustic scene classification, considering from 1 to 100 labeled examples per class. Results indicate that transfer learning is a powerful strategy in such scenarios, but prototypical networks show promising results when one does not count with external or validation data.

关键词： prototypical networks transfer learning audio classification small data

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