A test of independence based on a generalized correlation function

Rao, Murali; Seth, Sohan; Xu, Jing; Chen, Yunmei; Tagare, Hemant D.; Prı́ncipe, José C.

doi:10.1016/j.sigpro.2010.06.002

Cited by 31 publications

(10 citation statements)

References 28 publications

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“…The 1D correlation matrices for the landmarks (e.g., considering T Bx and T Ty, etc. ), given the size of our data set, was computed considering correntropy, as proposed in Rao et al [27]. Bivariate correlations (i.e, taking both coordinates of each landmark together) were computed through canonical correlation analysis [28,20].…”

Section: Computation Of Data Statisticsmentioning

confidence: 99%

Exploring Critical Articulator Identification from 50Hz RT-MRI Data of the Vocal Tract

Silva¹,

Teixeira²,

Cunha³

et al. 2019

Interspeech 2019

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The study of the static and dynamic aspects of speech production can profit from technologies such as electromagnetic midsagittal articulography (EMA) and real-time magnetic resonance (RTMRI). These can improve our knowledge on which articulators and gestures are involved in producing specific sounds and foster improved speech production models, paramount to advance, e.g., articulatory speech synthesis. Previous work, by the authors, has shown that critical articulator identification could be performed from RTMRI data of the vocal tract, with encouraging results, by extending the applicability of an unsupervised statistical identification method previously proposed for EMA data. Nevertheless, the slower time resolution of the considered RT-MRI corpus (14 Hz), when compared to EMA, potentially influencing the ability to select the most suitable representative configuration for each phone-paramount for strongly dynamic phones, e.g., nasal vowels-, and the lack of a richer set of contexts-relevant for observing coarticulation effects-, were identified as limitations. This article addresses these limitations by exploring critical articulator identification from a faster RTMRI corpus (50 Hz), for European Portuguese, providing a richer set of contexts, and testing how fusing the articulatory data of two speakers might influence critical articulator determination.

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Section: Computation Of Data Statisticsmentioning

confidence: 99%

Exploring Critical Articulator Identification from 50Hz RT-MRI Data of the Vocal Tract

Silva¹,

Teixeira²,

Cunha³

et al. 2019

Interspeech 2019

View full text Add to dashboard Cite

show abstract

“…The matlab code for computing CC [54,55] is available in the form of ITL Toolbox at www.sohanseth.com/Home/codes.…”

Section: Feature Extractionmentioning

confidence: 99%

Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals

Patidar

Pachori

Acharya

2015

Knowledge-Based Systems

157

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“…We eliminate such dependence by taking the maximum score over all permutations. By considering the maximum value, we aim at uncovering the best correlation score of the dimensions involved, which is in line with maximal correlation analysis [2,20]. Formally, letting F d be the set of bijective functions σ : {1, .…”

Section: Correlation Measures -A Brief Primermentioning

confidence: 96%

“…Quadratic measures of dependency [18,20,24] permit empirical computation in closed form. They closely follow the correlation model in Eq.…”

Section: Related Workmentioning

confidence: 99%

Universal Dependency Analysis

Nguyen¹,

Mandros²,

Vreeken³

2016

Proceedings of the 2016 SIAM International Conference on Data Mining

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Finding patterns from binary data is a classical problem in data mining, dating back to at least frequent itemset mining. More recently, approaches such as tiling and Boolean matrix factorization (BMF), have been proposed to find sets of patterns that aim to explain the full data well. These methods, however, are not robust against non-trivial destructive noise, i.e. when relatively many 1s are removed from the data: tiling can only model additive noise while BMF assumes approximately equal amounts of additive and destructive noise. Most real-world binary datasets, however, exhibit mostly destructive noise. In presence/absence data, for instance, it is much more common to fail to observe something than it is to observe a spurious presence. To address this problem, we take the recent approach of employing the Minimum Description Length (MDL) principle for BMF and introduce a new algorithm, Nassau, that directly optimizes the description length of the factorization instead of the reconstruction error. In addition, unlike the previous algorithms, it can adjust the factors it has discovered during its search. Empirical evaluation on synthetic data shows that Nassau excels at datasets with high destructive noise levels and its performance on real-world datasets confirms our hypothesis of the high numbers of missing observations in the real-world data

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A test of independence based on a generalized correlation function

Cited by 31 publications

References 28 publications

Exploring Critical Articulator Identification from 50Hz RT-MRI Data of the Vocal Tract

Exploring Critical Articulator Identification from 50Hz RT-MRI Data of the Vocal Tract

Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals

Universal Dependency Analysis

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