With the rapid development of remote sensing technology, our ability to obtain remote sensing data has been improved to an unprecedented level. We have entered an era of big data. Remote sensing data clear showing the characteristics of Big Data such as hyper spectral, high spatial resolution, and high time resolution, thus, resulting in a significant increase in the volume, variety, velocity and veracity of data. This paper proposes a feature supporting, salable, and efficient data cube for time-series analysis application, and used the spatial feature data and remote sensing data for comparative study of the water cover and vegetation change.The spatial-feature remote sensing data cube (SRSDC) is described in this paper. It is a data cube whose goal is to provide a spatial-feature-supported, efficient, and scalable multidimensional data analysis system to handle largescale RS data. It provides a high-level architectural overview of the SRSDC.The SRSDC offers spatial feature repositories for storing and managing vector feature data, as well as feature translation for converting spatial feature information to query operations.The paper describes the design and implementation of a feature data cube and distributed execution engine in the SRSDC. It uses the long time-series remote sensing production process and analysis as examples to evaluate the performance of a feature data cube and distributed execution engine. Big data has become a strategic highland in the knowledge economy as a new strategic resource for humans. The core knowledge discovery methods include supervised learning methods data analysis supervised learning, unsupervised learning methods data analysis unsupervised learning, and their combinations and variants.
Esophageal speech is one of the pathological voices, which is known to be weak in intelligibility and hard to understand. Our approach's main idea is to reduce the esophageal speech noises using two-hybrid methods. This paper aims to merge the advantages of wavelet-based methods such as DWT and DTCWT, along with the standard methods such as the Wiener filter and the time dilated Fourier. The first hybrid method applies the filters on the vocal tract cepstrum, while the second one applies them at the synthesis stage. Two experiments were conducted as well to evaluate the results by objective analysis. The results obtained by the proposed hybrid methods gave good performances.
A new wavelet-based method is presented in this work for estimating and tracking the pitch period. The main idea of the proposed new approach consists in extracting the cepstrum excitation signal and applying on it a wavelet transform whose resulting approximation coefficients are smoothed, for a better pitch determination. Although the principle of the algorithms proposed has already been considered previously, the novelty of our methods relies in the use of powerful wavelet transforms well adapted to pitch determination. The wavelet transforms considered in this article are the discrete wavelet transform and the dual tree complex wavelet transform. This article, by all the provided experimental results, corroborates the idea of decomposing the cepstrum excitation by using wavelet transforms for improving pitch detection. Another interesting point of this article relies in using a simple but efficient voicing decision (which actually improves a similar voicing criterion we proposed in a preceding published study) which on one hand respects the real-time process with low latency and on the other hand allows obtaining low classifications errors. The accuracy of the proposed pitch tracking algorithms has been evaluated using the international Bagshaw and the Keele databases which include male and female speakers. Our various experimental results demonstrate that the proposed methods provide important performance improvements when compared with previously published pitch determination algorithms.
In the current paper, we propose a new pitch tracking technique based on a wavelet transform in the temporal domain. Our algorithm is designed to determine the pitch frequency of the speech signal using a simple voicing decision algorithm. The pitch period is extracted from the cepstrum excitation signal processed by a wavelet transform; then the pitch contour is refined by thresholding and correction algorithms without any postprocessing. The results obtained show that the proposed algorithm provides very good pitch contours compared to those furnished by the Bagshaw database.
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