The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform
Ahmed K. Elmagarmid - One of the best experts on this subject based on the ideXlab platform.
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warp time warping for Periodicity detection
International Conference on Data Mining, 2005Co-Authors: Mohamed G. Elfeky, Walid G Aref, Ahmed K. ElmagarmidAbstract:Periodicity mining is used for predicting trends in time series data. Periodicity detection is an essential process in Periodicity mining to discover potential Periodicity rates. Existing Periodicity detection algorithms do not take into account the presence of noise, which is inevitable in almost every real-world time series data. In this paper, we tackle the problem of Periodicity detection in the presence of noise. We propose a new Periodicity detection algorithm that deals efficiently with all types of noise. Based on time warping, the proposed algorithm warps (extends or shrinks) the time axis at various locations to optimally remove the noise. Experimental results show that the proposed algorithm outperforms the existing Periodicity detection algorithms in terms of noise resiliency.
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Periodicity detection in time series databases
IEEE Transactions on Knowledge and Data Engineering, 2005Co-Authors: Mohamed G. Elfeky, Walid G Aref, Ahmed K. ElmagarmidAbstract:Periodicity mining is used for predicting trends in time series data. Discovering the rate at which the time series is periodic has always been an obstacle for fully automated Periodicity mining. Existing Periodicity mining algorithms assume that the Periodicity, rate (or simply the period) is user-specified. This assumption is a considerable limitation, especially in time series data where the period is not known a priori. In this paper, we address the problem of detecting the Periodicity rate of a time series database. Two types of periodicities are defined, and a scalable, computationally efficient algorithm is proposed for each type. The algorithms perform in O(n log n) time for a time series of length n. Moreover, the proposed algorithms are extended in order to discover the periodic patterns of unknown periods at the same time without affecting the time complexity. Experimental results show that the proposed algorithms are highly accurate with respect to the discovered Periodicity rates and periodic patterns. Real-data experiments demonstrate the practicality of the discovered periodic patterns.
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Online Periodicity mining
2005Co-Authors: Mohamed G. Elfeky, Ahmed K. Elmagarmid, Walid G ArefAbstract:This dissertation addresses the online Periodicity mining problem. Periodicity mining is the process of discovering frequent periodic patterns in an attempt towards predicting the future behavior in time series data. The ubiquitousness of sensor devices that generate real-time, append-only and semi-infinite data streams has revived the need for online processing. We define Periodicity mining as a two-step process: discovering potential Periodicity rates (Periodicity Detection), and discovering the frequent periodic patterns of each Periodicity rate (Mining Periodic Patterns). We propose new algorithms for both online Periodicity detection and online mining of periodic patterns. For the latter, the proposed algorithm incrementally maintains an efficient data structure, namely the max-subpattern tree, from which the periodic patterns are discovered. For the Periodicity detection, we define two types of periodicities: segment Periodicity and symbol Periodicity. Whereas segment Periodicity concerns the Periodicity of the entire time series, symbol Periodicity concerns the periodicities of the various symbols or values of the time series. For each Periodicity type, we propose an efficient convolution-based Periodicity detection algorithm. Furthermore, we propose online Periodicity mining algorithms that integrate both Periodicity mining steps, and thus are able to discover the periodic patterns of unknown periods. All the proposed online algorithms require only one pass over the time series and no reprocessing of previously seen data. Finally, we address the inevitable problem of the presence of noise in real-world time series data. We propose a new online Periodicity detection algorithm that deals efficiently with all types of noise. Based on time warping, the proposed algorithm warps (extends or shrinks) the time axis at various locations to optimally remove the noise. Experimental studies for all the proposed algorithms are carried out using both synthetic and real-world data. Results show that the proposed algorithms outperform the existing Periodicity mining algorithms in terms of the time performance, the accuracy of the discovered Periodicity rates and periodic patterns, and the resilience to noise. Real-data experiments demonstrate the practicality of the discovered periodic patterns.
Hideki Kawahara - One of the best experts on this subject based on the ideXlab platform.
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a modulation property of time frequency derivatives of filtered phase and its application to aPeriodicity and fo estimation
Conference of the International Speech Communication Association, 2017Co-Authors: Hideki Kawahara, Kenichi Sakakibara, Masanori Morise, Hideki Banno, Tomoki TodaAbstract:We introduce a simple and linear SNR (strictly speaking, periodic to random power ratio) estimator (0dB to 80dB without additional calibration/linearization) for providing reliable descriptions of aPeriodicity in speech corpus. The main idea of this method is to estimate the background random noise level without directly extracting the background noise. The proposed method is applicable to a wide variety of time windowing functions with very low sidelobe levels. The estimate combines the frequency derivative and the time-frequency derivative of the mapping from filter center frequency to the output instantaneous frequency. This procedure can replace the Periodicity detection and aPeriodicity estimation subsystems of recently introduced open source vocoder, YANG vocoder. Source code of MATLAB implementation of this method will also be open sourced.
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Excitation source structural analysis of Japanese traditional singing voices
Journal of the Acoustical Society of America, 2012Co-Authors: Hideki KawaharaAbstract:New set of voice excitation source analysis methods are applied to study Japanese traditional singing voices, especially Noh. The first method, XSX (excitation Structure extractor) is capable of visualize detailed structure of subharmonic Periodicity, by using multiple dedicated Periodicity detectors. The second one analyzes symmetry of the fundamental component waveform, cycle by cycle. It enables to discriminate voice onset and offset details.
Guorui Ren - One of the best experts on this subject based on the ideXlab platform.
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the analysis of turbulence intensity based on wind speed data in onshore wind farms
Renewable Energy, 2018Co-Authors: Guorui Ren, Jinfu Liu, Jie Wan, Yufeng GuoAbstract:Abstract Wind speed turbulence intensity is crucial for wind turbine structure design and aerodynamic loads calculation. In the study, the actual turbulence intensity observations are compared with the Normal Turbulence Model defined by IEC standard. The results show that the Normal Turbulence Model overestimates the turbulence intensity. A new turbulence intensity model is proposed based on the actual observations, which shows better performance than the Normal Turbulence Model. Then the variation pattern of turbulence intensity during a day is analyzed. The turbulence intensity exhibits obvious daily Periodicity in two wind farms. Furthermore, the causes of daily Periodicity are discussed and verified by the wind speed dataset 3. Finally, an improved time-varying turbulence intensity model is developed according to the daily Periodicity.
Mohamed G. Elfeky - One of the best experts on this subject based on the ideXlab platform.
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warp time warping for Periodicity detection
International Conference on Data Mining, 2005Co-Authors: Mohamed G. Elfeky, Walid G Aref, Ahmed K. ElmagarmidAbstract:Periodicity mining is used for predicting trends in time series data. Periodicity detection is an essential process in Periodicity mining to discover potential Periodicity rates. Existing Periodicity detection algorithms do not take into account the presence of noise, which is inevitable in almost every real-world time series data. In this paper, we tackle the problem of Periodicity detection in the presence of noise. We propose a new Periodicity detection algorithm that deals efficiently with all types of noise. Based on time warping, the proposed algorithm warps (extends or shrinks) the time axis at various locations to optimally remove the noise. Experimental results show that the proposed algorithm outperforms the existing Periodicity detection algorithms in terms of noise resiliency.
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Periodicity detection in time series databases
IEEE Transactions on Knowledge and Data Engineering, 2005Co-Authors: Mohamed G. Elfeky, Walid G Aref, Ahmed K. ElmagarmidAbstract:Periodicity mining is used for predicting trends in time series data. Discovering the rate at which the time series is periodic has always been an obstacle for fully automated Periodicity mining. Existing Periodicity mining algorithms assume that the Periodicity, rate (or simply the period) is user-specified. This assumption is a considerable limitation, especially in time series data where the period is not known a priori. In this paper, we address the problem of detecting the Periodicity rate of a time series database. Two types of periodicities are defined, and a scalable, computationally efficient algorithm is proposed for each type. The algorithms perform in O(n log n) time for a time series of length n. Moreover, the proposed algorithms are extended in order to discover the periodic patterns of unknown periods at the same time without affecting the time complexity. Experimental results show that the proposed algorithms are highly accurate with respect to the discovered Periodicity rates and periodic patterns. Real-data experiments demonstrate the practicality of the discovered periodic patterns.
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Online Periodicity mining
2005Co-Authors: Mohamed G. Elfeky, Ahmed K. Elmagarmid, Walid G ArefAbstract:This dissertation addresses the online Periodicity mining problem. Periodicity mining is the process of discovering frequent periodic patterns in an attempt towards predicting the future behavior in time series data. The ubiquitousness of sensor devices that generate real-time, append-only and semi-infinite data streams has revived the need for online processing. We define Periodicity mining as a two-step process: discovering potential Periodicity rates (Periodicity Detection), and discovering the frequent periodic patterns of each Periodicity rate (Mining Periodic Patterns). We propose new algorithms for both online Periodicity detection and online mining of periodic patterns. For the latter, the proposed algorithm incrementally maintains an efficient data structure, namely the max-subpattern tree, from which the periodic patterns are discovered. For the Periodicity detection, we define two types of periodicities: segment Periodicity and symbol Periodicity. Whereas segment Periodicity concerns the Periodicity of the entire time series, symbol Periodicity concerns the periodicities of the various symbols or values of the time series. For each Periodicity type, we propose an efficient convolution-based Periodicity detection algorithm. Furthermore, we propose online Periodicity mining algorithms that integrate both Periodicity mining steps, and thus are able to discover the periodic patterns of unknown periods. All the proposed online algorithms require only one pass over the time series and no reprocessing of previously seen data. Finally, we address the inevitable problem of the presence of noise in real-world time series data. We propose a new online Periodicity detection algorithm that deals efficiently with all types of noise. Based on time warping, the proposed algorithm warps (extends or shrinks) the time axis at various locations to optimally remove the noise. Experimental studies for all the proposed algorithms are carried out using both synthetic and real-world data. Results show that the proposed algorithms outperform the existing Periodicity mining algorithms in terms of the time performance, the accuracy of the discovered Periodicity rates and periodic patterns, and the resilience to noise. Real-data experiments demonstrate the practicality of the discovered periodic patterns.
Peter J. Murphy - One of the best experts on this subject based on the ideXlab platform.
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Periodicity estimation in synthesized phonation signals using cepstral rahmonic peaks
Speech Communication, 2006Co-Authors: Peter J. MurphyAbstract:APeriodicity in sustained phonation can result from temporal, amplitude and waveshape perturbations, turbulent noise, nonlinear phenomena and non-stationarity of the vocal tract. General measures of the Periodicity of the voice signal are of interest in, for example, quantifying voice quality and in the assessment of pathological voice. High and low quefrency cepstral techniques are employed to supply an index of the degree of voice signal Periodicity. In the high quefrency region, the first rahmonic is used to provide an indication of the Periodicity of the signal. A new measure, SRA (sum of rahmonic amplitudes) - utilising all rahmonics in the cepstrum, is tested against synthesis data (six levels of random jitter, cyclic jitter, shimmer and random noise). In addition, an existing popular technique using the first rahmonic (cepstral peak prominence, CPP) is assessed with synthesis data for the first time. Both measures decrease with increasing aPeriodicity levels of the glottal source, decreasing more noticeably for noise and random jitter than for shimmer and cyclic jitter. CPP is shown to be relatively f"0-independent; however, the index appears to be less sensitive when compared against SRA.
