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检索条件"主题词=detection algorithms"

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Some comparisons among several pitch detection algorithms

Some comparisons among several pitch detection algorithms

引用

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

作者： M. Cheng L. Rabiner A. Rosenberg C. McGonegal Bell Laboratories Murray Hill NJ USA

A comparative performance study of seven pitch detection algorithms was conducted. A speech data base, consisting of eight utterances spoken by 3 males, 3 females, and 1 child was constructed. Telephone, close talking microphone, and wideband recordings were made of each of the utterances. For each of the utterances in the data base a "standard" pitch contour was semiautomaticallly measured using a highly sophisticated interactive pitch detection program. The "standard" pitch contour was then compared with the pitch contour that was obtained from each of the seven programmed pitch detectors. The algorithms used in this study were (1) a center clipping, infinite-peak clipping, modified autocorrelation method, (2) the cepstral method, (3) the SIFT method, (4) the parallel processing time domain method, (5) the data reduction method, (6) a spectral flattening LPC method, and (7) the AMDF method. A set of measurements was made on the pitch contours to quantify the various types of errors which occur in each of the above methods. Included among the error measurements were the average and standard deviation of the error in pitch period during voiced regions, the number of gross errors in the pitch period, and the number of voiced-unvoiced classification errors. For each of the error measurements, the individual pitch detectors could be rank ordered as a measure of their relative performance as a function of recording condition, and pitch range of the various speakers. Results are presented on rankings based on one category of errors.

关键词： detection algorithms Measurement standards Detectors Speech Telephony Microphones Wideband Autocorrelation Cepstral analysis Parallel processing

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Static community detection algorithms for evolving networks

Static community detection algorithms for evolving networks

引用

International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Thomas Aynaud Jean-Loup Guillaume LIP6/CNRS-Université Pierre et Marie Curiee Paris France

Complex networks can often be divided in dense sub-networks called communities. Using a partition edit distance, we study how three community detection algorithms transform their outputs if the input network is slightly modified. The instabilities appear to be important and we propose a modification of one algorithm to stabilize it and to allow the tracking of the communities in an evolving network. This modification has one parameter which is a tradeoff between stability and quality. The resulting algorithm appears to be very effective. We finally use it on an evolving network of blogs.

关键词： detection algorithms Stability Partitioning algorithms Blogs Complex networks IP networks Computer networks Physics computing Web pages Social network services

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A Regularized Least Squares-Based Method for Optimal Fusion of Speech Pitch detection algorithms

A Regularized Least Squares-Based Method for Optimal Fusion ...

引用

International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Ziba Imani Seyed Jahanshah Kabudian Department of Computer Engineering and Information Technology Razi University Kermanshah Iran

ISBN: (纸本)9781728111957;9781728111940

Fundamental frequency estimation is one of the most important issues in the field of speech processing. An accurate estimate of the fundamental frequency plays a key role in the field of speech and music analysis. So far, various methods have been proposed in the time- and frequency-domain. However, the main challenge is the strong noises in speech signals. In this paper, to improve the accuracy of fundamental frequency estimation, we propose a method for optimal combination of fundamental frequency estimation methods, in noisy signals. In this method, to discriminate voiced frames from unvoiced frames in a better way, the Voiced/Unvoiced (V/U) scores of four pitch detection methods are combined linearly. These methods are: Autocorrelation, Yin, YAAPT and SWIPE. After identifying the Voiced/Unvoiced label of each frame, the fundamental frequency (F 0 ) of the frame is estimated using the SWIPE method. The optimal coefficients for linear combination are determined using the regularized least squares method with Tikhonov regularization. To evaluate the proposed method, 10 speech files (5 female and 5 male voices) are selected from the PTDB-TUG standard database and the results are presented in terms of SDFPE, GPE, VDE, PTE and FFE standard error criteria. The results indicate that our proposed method relatively reduced the aforementioned criteria (averaged in various SNRs) by 27.13%, 22.14%, 17.40% and 26.74% respectively, which demonstrate the effectiveness of the proposed method in comparison to state-of-the-art methods.

关键词： Mathematical model Estimation detection algorithms Training Frequency estimation Noise measurement Standards

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Estimating network parameters for selecting community detection algorithms

Estimating network parameters for selecting community detect...

引用

International Conference on Information Fusion

作者： Leto Peel Advanced Technology Centre BAE Systems Bristol UK

This paper considers the problem of algorithm selection for community detection. The aim of community detection is to identify sets of nodes in a network which are more interconnected relative to their connectivity to the rest of the network. A large number of algorithms have been developed to tackle this problem, but as with any machine learning task there is no “one-size-fits-all” and each algorithm excels in a specific part of the problem space. This paper examines the performance of algorithms developed for weighted networks against those using unweighted networks for different parts of the problem space (parameterised by the intra/inter community links). It is then demonstrated how the choice of algorithm (weighted/unweighted) can be made based only on the observed network.

关键词： Communities Classification algorithms Prediction algorithms Algorithm design and analysis Mutual information Machine learning algorithms detection algorithms

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Performance evaluation of V-BLAST detection algorithms and a novel segmented detection algorithm

Performance evaluation of V-BLAST detection algorithms and a...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Bo Gui Daiming Qu Department Electron. & Information Engineering Huazhong University of Science and Technology Wuhan Hubei China

ISBN: (纸本)0780382552

Several detection algorithms for the vertical Bell Laboratories layered space time (V-BLAST) system are compared. We propose the concept of segmented detection on the observation that the overall performance of decision feedback equalization (DFE) is limited by the performance of the first detected subchannel. We perform maximum-likelihood (ML) detection for the first several subchannels and use the DFE procedure to detect the remaining subchannels. Also, we propose a novel segmented detection algorithm based on the minimum mean square error (MMSE) criterion. Compared with DFEs, segmented detection improves the detection performance with little increase of complexity. Computer simulation verifies it.

关键词： detection algorithms Decision feedback equalizers MIMO Receiving antennas Space technology Maximum likelihood detection Mean square error methods Computer simulation Transmitting antennas Interference suppression

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Supervised evaluation methodology for curvilinear structure detection algorithms

Supervised evaluation methodology for curvilinear structure ...

引用

International Conference on Pattern Recognition

作者： Xiaoyi Jiang D. Mojon Department of Electrical Engineering and Computer Science Technical University Berlin Germany Department of Neuro Ophthalmology and Strabismus Switzerland

ISBN: (纸本)076951695X

Curvilinear structures are useful features in a variety of applications. Compared to other commonly used features such as edges, there are relatively few work on curvilinear structure detection and its performance evaluation. In this paper we propose a novel supervised methodology for evaluating the performance of curvilinear structure detection algorithms. We consider the two aspects of performance, namely the detection rate and detection accuracy, respectively in contrast to their mixed handling in earlier approaches that typically produces biased impression of detection quality. By doing so, the proposed performance measures lead to a more informative and precise performance characterization. We demonstrate the advantages of our approach using both synthetic and real examples.

关键词： detection algorithms Image edge detection Biomedical imaging Pixel Blood vessels Application software Roads Rivers Bones Humans

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On the Robustness of Machine Learning Based Malware detection algorithms

On the Robustness of Machine Learning Based Malware Detectio...

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International Joint Conference on Neural Networks

作者： Weiwei Hu Ying Tan Key Laboratory of Machine Perception (MOE) and Department of Machine Intelligence School of Electronics Engineering and Computer Science Peking University

ISBN: (纸本)9781509061839

With the rapid popularity of the Internet, a large amount of new malware is produced every day, while the traditional signature based malware detection algorithm is unable to detect such unseen malware. In recent years, many machine learning based algorithms have been proposed to detect new malware, and several of these algorithms are able to achieve quite good detection performance when supplied with plenty of training data. However, most of these algorithms just focus on how to improve the classification performance, while the robustness is not taken into consideration. This paper performs a detailed analysis on the robustness of four well-known machine learning based malware detection approaches, i.e. the DLL and API feature, the string feature, PE-Miner and the byte level N-Gram feature. We proposed two pretense approaches under which malware is able to pretend to be benign and bypass the detection algorithms. Experimental results show that the performances of these detection algorithms decline greatly under the pretense approaches. The lack of robustness makes these algorithms unable to be used in real world applications. In future works of machine learning based malware detection, researchers have to take the problem of robustness seriously.

关键词： detection algorithms Spyware Robustness Artificial Intelligence Machine Learning algorithms Dynamic link libraries

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Optimized Pan-Tompkins Based Heartbeat detection algorithms

Optimized Pan-Tompkins Based Heartbeat Detection Algorithms

引用

Chinese Control and Decision Conference, CCDC

作者： Xueyu Wu Zhonghua Wang Bo Xu Xibo Ma College of Electrical Engineering University of Jinan Jinan CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Science Beijing China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

The whole physiological state of cardiac electrical activity is reflected by electrocardiogram (ECG) signals collected from the body surface. ECG signals can be used as important basis for the diagnosis of heart disease. However, when analyzing ECG signals, people often process the signals in a record into units of heartbeats. Therefore, accurate heartbeat detection is an important prerequisite for the correct diagnosis of heart disease by the machine. The traditional heartbeat detection only obtains the QRS complex based on the Pan-Tompkins algorithm, and then performs time interception based on previous experience (forward and backward). The accuracy of heartbeats detection will be affected by individual differences. In this paper, an optimized Pan-Tompkins algorithm based on adding P-wave and T-wave location is proposed for the detection of ECG heartbeats. This algorithm was evaluated using 109966 samples from the MIT- BIH Arrhythmia Database including a variety of arrhythmia data (such as atrioventricular block, ventricular tachycardia, early ventricular contraction, atrial flutter, ventricular premature beats, ventricular fibrillation). 109519 samples were correctly detected from 109966 heartbeats. This method achieved 99.68% positive prediction rate(P +) with 99.90% sensitivity)SE). Our approach will lay a foundation for the processing of ECG signals and therefore improve the accuracy for the diagnosis of heart diseases.

关键词： Electrocardiography Heart beat Band-pass filters Low pass filters detection algorithms Interference

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Novel Index for Objective Evaluation of Road detection algorithms

Novel Index for Objective Evaluation of Road Detection Algor...

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International Conference on Intelligent Transportation

作者： J.M. Alvarez A. Lopez Department of Computer Science Universitat Autònoma Barcelona Barcelona Spain

Road detection is a relevant task within vision-based systems devoted to assist the driver. Although they have been improved during the last decade, these algorithms are usually validated using qualitative results. Nonetheless, quantitative evaluation is necessary either to enable the comparison between different algorithms or to achieve the optimal performance of a given one. In this paper we present a composite index to quantitatively assess the performance of road detection algorithms. The measure is based on a weighted combination of different evaluations which use a trade-off between precision and recall scores. Obtaining a single index score is a major benefit. It can be used to easily compare algorithms or to properly set their parameters. Moreover, innovatively our proposal includes a human perception criterion to improve its usefulness. Experiments on real-world data corroborate the usefulness of the proposed index.

关键词： detection algorithms Image edge detection Layout Intelligent transportation systems Proposals Humans Cameras Object detection Road transportation Safety

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Effects of signature mismatch on hyperspectral detection algorithms

Effects of signature mismatch on hyperspectral detection alg...

引用

Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： D. Manolakis T. Cooley J. Jacobson MIT Lincoln Laboratories Lexington MA USA Air Force Research Laboratory NM USA National Air and Space Intelligence Center OH USA

The main objective of this paper is to discuss the effects of signature mismatch on hyperspectral target detection algorithms. The main causes of mismatch are atmospheric propagation, intrinsic spectral variability, sensor noise, and sensor artifacts. We provide a theoretical analysis that shows the effects of mismatch on adaptive detection algorithms, which use estimates of background covariance matrix, and we present a systematic diagonal loading technique which provides controlled robustness to mismatch.

关键词： Hyperspectral imaging Pixel Detectors detection algorithms Covariance matrix Robustness

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