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  • Kyrie from "Missa de Notre Dame" of Guillaume de Machaut (1300--1377). Four parts - tenor, contratenor, triplum and motetus - performed in an anechoic environment.
    Data Types:
    • Dataset
    • Audio
  • Anechoic recordings of the Gregorian Chants: - Antiphona "Vidi Aquam" from Ezekiel 47:1 - Canticum "Sicut Cervus" from Psalm 41:2-4 Tracks recorded by omnidirectional (Audio Technica 4050), Cardioid (Audio Technica 4050) and Sphere (Schoeps KFM6) microphones. Performed by Gregorian Choir Mediae Aetatis Sodalicium: Anna Pia Capurso, choir; Bruna Caruso, choir and conductor; Carla Cesari, choir and soloist in canticum Sicut cervus; Dina Cucchiaro; Francesca Provezza, choir and soloist in antiphona Vidi aquam. De-noising by Matteo Cingolani. Edited in Reaper v.5.9.
    Data Types:
    • Dataset
    • Audio
  • The attached file is a sample video of 10 volunteers who recorded 10 static gestures from American Sign Language. The dataset actually contains RGB and registered depth images in png and bin formats respectively. The letters/numbers taken from American Sign Language are A, F, D, L, 7, 5, 2, W, Y, None. Please contact osamazhar[at]yahoo[dot[.com to get complete data.
    Data Types:
    • Dataset
    • Audio
  • RDM - File Type Support 21May2019 ElsCustomer Apart from .u3d all files preview [ .obj / .ply / .vtk / .stl / .ent / .brk / .pdb / .pse / .mol / .mol2 / .cif / .u3d / .dcm / .nii] - .pse is not supported
    Data Types:
    • Other
    • Software/Code
    • Image
    • Sequencing Data
    • Video
    • Tabular Data
    • Dataset
    • Document
    • Text
    • Audio
  • This dataset contains processed and raw data necessary to reproduce the reported research investigation. The steps to reproduce this research are detailed described in the Materials and Method section in our paper.
    Data Types:
    • Other
    • Software/Code
    • Image
    • Video
    • Dataset
    • Audio
  • These files are example stimuli, shown in Figure 1 in the article. They all contain target sounds with 45 milliseconds VOT, corresponding to a "pa" response. The name of the file contains the intonation condition (flat, LH) and the duration condition (short, long), as discussed in the article.
    Data Types:
    • Dataset
    • Audio
  • this source code proposes a dynamic parameterization approach to the ant colony optimization algorithm configuration applied to multi-objective optimization problems. Indeed, the inertia of the static vision of the pheromone or visibility preferences values makes our dynamic approach a desired approach. We propose a model based on a collective knowledge center shared by the colony members, storing the best configurations based on the old experiments of the colony during the learning phase on random problems. The construction of this center is based on a statistical and qualitative study of the evaluation criteria that will be explained over the paper. Our model gives results that show a rise in quality of the outputs, as well as a proof of concept of the artificial learning approach.
    Data Types:
    • Software/Code
    • Dataset
    • Text
    • Audio
  • The videos depict the whole analysis. Every result was extracted from the simulations as attached. The simulations when played in slower frames will explain the time series results vividly. In case of any confusion the author would be glad to help.
    Data Types:
    • Dataset
    • Audio
  • These are the raw data files (in WAV format) for English and Spanish speakers as recorded and analyzed for the article "Sound level protrusions as physical correlates of sonority," published in Journal of Phonetics 36:55-90 (2008).
    Data Types:
    • Dataset
    • Document
    • Audio
  • The S-transform (ST) is a popular linear time-frequency (TF) transform with hybrid characteristics from the short-time Fourier transform (STFT) and the wavelet transform. It enables a multi-resolution TF analysis and returns globally referenced local phase information, but its expensive computational requirements often overshadow its other desirable features. In this paper, we develop a fully discrete ST (DST) with a controllable TF sampling scheme based on a filter-bank interpretation. The presented DST splits the analyzed signal into subband channels whose bandwidths increase progressively in a fully controllable manner, providing a frequency resolution that can be varied and made as high as required, which is a desirable property for processing oscillatory signals lacked by previously presented DSTs. Thanks to its flexible sampling scheme, the behavior of the developed transform in the TF domain can be adjusted easily; with specific parameter settings, for example, it samples the TF domain dyadically, while by choosing different settings, it may act as a STFT. The spectral partitioning is performed through asymmetric raised-cosine windows whose collective amplitude is unitary over the signal spectrum to ensure that the transform is easily and exactly invertible. The proposed DST retains all the appealing properties of the original ST, representing a local image of the Fourier transform; it requires low computational complexity and returns a modest number of TF coefficients. To confirm its effectiveness, the developed transform is utilized for different applications using real-world and synthetic signals.
    Data Types:
    • Software/Code
    • Dataset
    • Text
    • Audio
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