Study on processing of wavelet speech denoising in speech recognition system

Zhong, Xinmei; Dai, Yunzhong; Dai, Yong; Jin, Tao

doi:10.1007/s10772-018-9516-7

Cited by 15 publications

(10 citation statements)

References 8 publications

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“…The levels of planting priority were divided into five classes, including very high, high, moderate, low, and very low, for interpretability and visualization as in previous studies (e.g., Locke et al, 2010Locke et al, , 2013. The natural breaks classification (Jenks) is based on natural groupings inherent in the data searching for points where the difference is maximized and using these as the limits of each class.…”

Section: Visualization Of Resultsmentioning

confidence: 99%

“…The analysis presented here sought to maximize the benefits of street tree plantings within the City of Joliette. The development of the planting priority index included all the indicators that were determined to be relevant by the City and the team of researchers, guided by published literature (e.g., Locke et al, 2010Locke et al, , 2013 and the availability of data at the local level. This analysis also provides insight into which pre-existing trees are most important to protect in order to maintain or increase the canopy cover and associated benefits.…”

Section: Discussionmentioning

confidence: 99%

“…The selection of potential indicators and their relative weight for prioritizing street segments for tree planting was guided by published literature (e.g., Locke et al, 2010Locke et al, , 2013, expert judgment, and feedback from meetings with city managers, as well as data availability. Prioritization works by matching known tree benefits to areas lacking in those benefits; whereby areas lacking one or several benefits are classified as being of higher priority than areas already rich in benefits.…”

Section: Study Area and Indicators Analyzedmentioning

confidence: 99%

“…Other studies have described similar approaches for different cities or to prioritize specific benefits. For example, Morani et al (2011) and Locke et al (2013) have extended a priority planting methodology for Baltimore, Maryland, previously developed by Raciti et al (2006). Bodnaruk et al (2017) have explored optimal planting locations and tradeoffs for the mitigation of air pollution and excessive heat for Baltimore; Lin (2020) developed a framework to prioritize tree planting and protection locations to maximize human health benefits in a case study of New York City.…”

Section: Introductionmentioning

confidence: 99%

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Prioritizing Street Tree Planting Locations to Increase Benefits for All Citizens: Experience From Joliette, Canada

2021

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As the climate continues to warm and the world becomes more urbanized, our reliance on trees and the benefits they provide is rapidly increasing. Many cities worldwide are planting trees to offset rising temperatures, trap pollutants, and enhance environmental and human health and well-being. To maximize the benefits of planting trees and avoid further increasing social inequities, a city needs to prioritize where to establish trees by first identifying those areas of greatest need. This work aims to demonstrate a spatially explicit approach for cities to determine these priority locations to achieve the greatest returns on specific benefits. Criteria for prioritization were developed in tandem with the City of Joliette, Canada, and based on nine indicators: surface temperature, tree density, vegetation cover, resilience, tree size and age, presence of species at risk, land use type, socioeconomic deprivation, and potential for active transportation. The City’s preferences were taken into account when assigning different weights to each indicator. The resulting tree planting priority maps can be used to target street tree plantings to locations where trees are needed most. This approach can be readily applied to other cities as these criteria can be adjusted to accommodate specific tree canopy goals and planning constraints. As cities are looking to expand tree canopy, we hope this work will assist in sustaining and growing their urban forest, enabling it to be more resilient and to keep providing multiple and sustained benefits where they are needed the most.

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Section: Visualization Of Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Study Area and Indicators Analyzedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prioritizing Street Tree Planting Locations to Increase Benefits for All Citizens: Experience From Joliette, Canada

2021

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“…Thereby, the EMD algorithm is improved to EEMD, which has all the advantages of the EMD algorithm. EEMD can decompose the collected signal according to its own characteristics without setting the basis function [28,29]. Figure 8 is the algorithm flow chart.…”

Section: Scheme Design Of Ensemble Empirical Mode Decomposition (Eemd) Wavelet Threshold Denoisingmentioning

confidence: 99%

The Use of Intelligent Sensing Algorithm for Internet of Things and Hash Spatial Information Location Technology

Wang

Liu

2021

Wireless Communications and Mobile Computing

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This exploration is aimed at quickly obtaining the spatial position information of microseismic focal points and increasing the accuracy of microseismic rapid positioning, to take timely corresponding measures. A microseismic focal point location system completely different from the traditional microseismic location method is proposed. The search engine technology is introduced into the system, which can locate the microseismic focal point quickly and accurately. First, the propagation characteristics of microseismic signals in coal and rock layers are analyzed, and the focal position information is obtained. However, the collected microseismic signal of the coal mine contains noise, so it is denoised at first. Then, a waveform database is established for the denoised waveform data and focal point position. The structure and mathematical model of the location-sensitive hash (LSH) based on P stable distribution are introduced and improved, and the optimized algorithm multiprobe LSH is obtained. The microseismic location model is established according to the characteristics of microseismic data. The values of three parameters, hash table number, hash function family dimension, and interval size, are determined. The experimental data of the parameters of the search engine algorithm are analyzed. The results show that when the number of hash tables is 6, the dimension k of the hash function family is 14, and the interval size W is 8000, the retrieval time reaches a relatively small value, the recall rate reaches a large value, and the proportion of retrieved candidates is large; the parameters of the search engine algorithm of the measured coal mine microseismic data are analyzed. It is obtained that when the number of hash tables is 4, the dimension k of the hash function family is 6, and the interval size W is 500, the retrieval time reaches a relatively small value, the recall rate obtains a large value, and the proportion of retrieved candidates is large. The contents studied are of great significance to the evaluation of destructive mine earthquakes and impact risk.

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