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NMF2D-based source separation using extreme learning machine

NMF2D-based source separation using extreme learning machine

引用

2nd IET international conference on Intelligent signal processing 2015 (ISP)

作者： Di Wu W. L. Woo S. S. Dlay School of Electrical and Electronic Engineering Newcastle University England United Kingdom Newcastle University Newcastle upon Tyne Tyne and Wear GB

ISBN: (纸本)9781510823440

In this paper, we study Non-negative Matrix Two-Dimensional Factorization (NMF2D) based Single Channel Source Separation (SCSS) using a newly proposed algorithm named Extreme learning machine (ELM). Compared with other machine learning algorithms such as Support Vector machines and Neural Networks, ELM can provide better generalization performance and a much faster learning speed. Unlike conventional researches that concentrate on generating masks for each source, we use ELM to classify estimated sources separated by NMF2D algorithm. We also explore Deep ELM which means more than one hidden layers to improve the performance. While training Deep ELM, a method named layer by layer pre-training is used, but unlike Deep Belief Networks (DNNs) that need to fine-tune the whole network at the end, Deep ELM can be used without iteration fine-tuning. The experiment results show that the performance of proposed method is improved not only in training and testing speed, but also in the quality of separated signal compared with using DNNs and NMF2D.

关键词： Nonnegative Matrix Two-Dimensional Factorizations Extreme learning machine Single channel source separation learning machines Ecosystem models Elm Source separation machine learning algorithms Support Vector Network

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An exploratory study on the use of convolutional neural networks for object grasp classification 2

An exploratory study on the use of convolutional neural netw...

引用

2nd IET international conference on Intelligent signal processing 2015, ISP 2015

作者： Ghazaei, G. Alameer, A. Degenaar, P. Morgan, G. Nazarpour, K. School of Electrical and Electronic Engineering Newcastle University NewcastleNE1 7RU United Kingdom Institute of Neuroscience Newcastle University NewcastleNE2 4HH United Kingdom School of Computing Science Newcastle University NewcastleNE1 7RU United Kingdom

ISBN: (纸本)9781785611360

The loss of hand profoundly affects an individual's quality of life. Prosthetic hands can provide a route to functional rehabilitation by allowing the amputees to undertake their daily activities. However, the performance of current artificial hands falls well short of the dexterity that natural hands offer. The aim of this study is to test whether an intelligent vision system could be used to enhance the grip functionality of prosthetic hands. To this end, a convolutional neural network (CNN) deep learning architecture was implemented to classify the objects in the COIL100 database in four basic grasp groups: Tripod, pinch, palmar and palmar with wrist rotation. Our preliminary, yet promising, results suggest that the additional machine vision system can provide prosthetic hands with the ability to detect object and propose the user an appropriate grasp.

关键词： Object recognition

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Direct signal calibration using learning systems

Direct signal calibration using learning systems

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2nd IET international conference on Intelligent signal processing 2015 (ISP)

作者： K D Dias EW Simulation Technology United Kingdom

ISBN: (纸本)9781510823440

In addition to proposing two novel ensemble learning methods, a novel method of using a learning paradigm for the calibration of nonlinear systems is also proposed in this paper. With this it addresses the non-existent use of learning systems to provide corrective measures in calibration. In this method the learned system provides corrections directly to the nonlinear device to linearize the system output without the need for an additional calculation to produce the corrections. In many calibration tasks a model of the nonlinear system is created and with its help corrections are calculated to linearize the system output. The proposed method makes these two steps transparent by learning the corrective step instead. Therefore the learned system is able to then directly linearize the nonlinear system output. By taking into consideration both the training and pruning aspects of ensemble neural network predictors, two dynamic ensemble methods have been proposed in this paper, one involving pruning and the other a hybrid approach. To enhance diversity the pruning or selection of predictors, and the training of predictors are performed in succession for every pattern in the training set. By ordering the predictors based on their performance on a training pattern, the first method trains only the most divers predictors, while the second method splits the ensemble into two sub-ensembles and applies the hybrid method of training the first sub-ensemble using Negative Correlation learning (NCL) while the second sub-ensemble independently. During the test phase of these methods a subset of the trained predictors are chosen differently depending on their performance on the test sample. Therefore the ensemble selection is dynamic during predicting the output, which improves the prediction accuracy.

关键词： Calibration Neural Networks Ensemble learning machine learning learning systems output(systems) Neural network Nonlinear systems Training signal correction Calibration tree surgery nonlinear device Artificial Intelligence Functional training machine learning Performance Tests

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AUTOMATIC DETECTION OF INSPIRATION RELATED SNORING signalS FROM ORIGINAL AUDIO RECORDING 2

AUTOMATIC DETECTION OF INSPIRATION RELATED SNORING SIGNALS F...

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2nd IEEE China Summit / international conference on signal and Information processing (IEEE ChinaSIP)

作者： Qian, Kun Xu, Zhiyong Xu, Huijie Ng, Boon Poh Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing Peoples R China Beijing Hosp Dept Otolaryngol Beijing Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

ISBN: (纸本)9781479954032

Inspiration related snoring signals (IRSS) are essential for doctors and researchers to develop further study and establishment of personal health database. How to detect IRSS automatically from original audio recording is significant in methods of acoustic based Obstructive Sleep Apnea/Hypopnea Syndrome (OSAHS) diagnosis and monitoring. We proposed a systematic approach combining signal processing with machine learning techniques to detect IRSS from audio recording. Both the experimental results and computer studies demonstrate the efficiency of the proposed approach.

关键词： inspiration related snoring signals signal processing machine learning Obstructive Sleep Apnea/Hypopnea Syndrome

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A BAYESIAN NETWORK VIEW ON LINEAR And NONLINEAR ACOUSTIC ECHO CANCELLATION 2

A BAYESIAN NETWORK VIEW ON LINEAR AND NONLINEAR ACOUSTIC ECH...

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2nd IEEE China Summit / international conference on signal and Information processing (IEEE ChinaSIP)

作者： Maas, Roland Huemmer, Christian Schwarz, Andreas Hofmann, Christian Kellermann, Walter Univ Erlangen Nurnberg Multimedia Commun & Signal Proc D-91054 Erlangen Germany

ISBN: (纸本)9781479954032

In this contribution, we provide a new derivation of the normalized least mean square (NLMS) algorithm from a machine learning perspective. By applying the inference rules of Bayesian networks to a linear observation model, the NLMS can be shown to arise as a modification of the Kalman filter equations. Based on a nonlinear observation model, we exemplify the benefit of the Bayesian point of view by employing the technique of particle filtering to realize a tractable algorithm for nonlinear acoustic echo cancellation. Experiments carried out on real smartphone recordings reveal the remarkable performance of the new approach.

关键词： machine learning for signal processing Bayesian networks system identification adaptive filtering nonlinear acoustic echo cancellation

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HASHING BASED FEATURE AGGREGATING FOR FAST IMAGE COPY RETRIEVAL 2

HASHING BASED FEATURE AGGREGATING FOR FAST IMAGE COPY RETRIE...

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2nd IEEE China Summit / international conference on signal and Information processing (IEEE ChinaSIP)

作者： Yan, Lingyu Ling, Hefei Liu, Cong Ou, Xinyu Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 43000 Hubei Peoples R China

ISBN: (纸本)9781479954032

Recently the methods based on visual words have become very popular in near-duplicate retrieval and content identification. However, obtaining the visual vocabulary by quantization is very time-consuming and unscalable to large databases. In this paper, we propose a fast feature aggregating method for image representation which uses machine learning based hashing to achieve fast feature aggregation. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. The evaluation shows that our approach significantly outperforms state-of-the-art methods.

关键词： Image Copy Retrieval Visual Words Feature Aggregation machine learning base hashing

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2014 IEEE China Summit and international conference on signal and Information processing, IEEE ChinaSIP 2014 - proceedings

2014 IEEE China Summit and International Conference on Signa...

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2nd IEEE China Summit and international conference on signal and Information processing, IEEE ChinaSIP 2014

ISBN: (纸本)9781479954032

The proceedings contain 165 papers. The topics discussed include: beyond semantics: computable affective attributes from social multimedia content;deep learning: from speech recognition to language and multimodal processing;convex optimization and its applications in signal processing;deep learning: from speech recognition to language and multimodal processing;beyond semantics: computable affective attributes from social multimedia content;hand segmentation with metric learning superpixels;exposing the double compression in MP3 audio by frequency vibration;histogram-based retrieval for encrypted JPEG images;hashing based feature aggregating for fast image copy retrieval;PCA-based denoising of sensor pattern noise for source camera identification;and efficient digital fingerprints tracing.

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EFFICIENT DEPTH ESTIMATION FROM SINGLE IMAGE 2

EFFICIENT DEPTH ESTIMATION FROM SINGLE IMAGE

引用

2nd IEEE China Summit / international conference on signal and Information processing (IEEE ChinaSIP)

作者： Zhou, Wei Dai, Yuchao He, Renjie Australian Natl Univ Coll Engn & Comp Sci Canberra ACT 0200 Australia Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China

ISBN: (纸本)9781479954032

Single image depth estimation, which aims at estimating 3D depth from a single image, is a challenging task in computer vision since a single image does not provide any depth cue itself. machine learning-based methods transfer depth from a pool of images with available depth maps to query image in parametric and non-parametric manners. However, these methods generally involve processing a large dataset, therefore are rather time-consuming. This paper proposes to speed up the whole implementation in a hierarchical way. First, feature extraction based methods are utilized to evaluate image similarities. Then, clustering methods are performed on the image dataset to partition the dataset into several groups. Finally, instead of searching the whole dataset, the query image only compares with each cluster's representative image and regards the most similar group as the final training dataset. Experiments show that the proposed method achieves significant speed up while keeping similar depth estimation performance compared with the state-of-the-art method.

关键词： depth estimation image clustering machine learning image analysis image dataset

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learning LATENT VARIABLE GRAMMARS FROM COMPLEMENTARY PERSPECTIVES 2

LEARNING LATENT VARIABLE GRAMMARS FROM COMPLEMENTARY PERSPEC...

引用

2nd IEEE China Summit / international conference on signal and Information processing (IEEE ChinaSIP)

作者： Li, Dongchen Zhang, Xiantao Wu, Xihong Peking Univ Speech & Hearing Res Ctr Key Lab Machine Percept & Intelligence Beijing 100871 Peoples R China

ISBN: (纸本)9781479954032

The corpus for training a parser consists of sentences of heterogeneous grammar usages. Previous parser domain adaptation work has concentrated on adaptation to the shifts in vocabulary rather than grammar usage. In this paper, we focus on exploiting the diversity of training date separately and then accumulates their advantages. We propose an approach that grammar is biased toward relevant syntactic style, and the complementary grammar usage are combined for inference. Multiple grammars with partly complementary points of strength are induced individually. They capture complementary data representation, and we accumulates their advantages in a joint model to assemble the complementary depicting powers. Despite its compatibility with many other methods, out product model achieves 85.20% F-1 score on Penn Chinese Treebank, higher than previous systems.

关键词： Parsing sparsity PCFGLA

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A new UWB target detection and identification method based on extreme learning machine

A new UWB target detection and identification method based o...

引用

2nd international conference on Communications, signal processing, and Systems, CSPS 2013

作者： Shen, Hao Jiang, Ting Key Lab of Universal Wireless Communication Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9783319005355

UWB is a short-range wireless communication technology with strong resolution, detection and anti-jamming capability. UWB radar has been widely used in the transportation detection, bridge detection [1], medical detection [2], etc. However traditional UWB radar is used for target detection and identification by analyzing the echo signals. It cannot content the requirement of accomplishing communication and target detection at the same time which is significant in individual combat and intrusion detection. In this paper, we propose a novel target detection and recognition method which is different from the traditional UWB radar to content the above requirement. This method extracts the characteristic parameters of the received signals instead of the echo signals, and employ the extreme learning machine (ELM) to identify target. According to Matlab simulation result, the new method is quite fast and effective in target identification. © 2014 Springer international Publishing Switzerland.

关键词： Intrusion detection

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