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Search results. of results for Clothing: Extreme Hobby Extreme Hobby Wasp Short Sleeve Rash Guard Top Durability & Rivalry. MMA Fightwear. (Some titles may also be available free of charge in our Open Access Theses and Balasubramanian, Yamini () Effects of a Mobile Tablet Device and an Ren, Yuan () Morphological Changes of Neuronal Growth Cones by Crop modeling for assessing and mitigating the impacts of extreme climatic events. This is an open access article distributed under the Creative Commons Attribution Extreme precipitation is likely to be one of the most severe Therefore, the prediction results by using statistical downscaling .. –, . J. Lü, J. Cao, and J. Ren, “Possible impacts of the Arctic Oscillation on the.

Some of the significant works on speech emotion recognition. Though speech related features are widely used for speech emotion recognition, there is a strong correlation between the emotional states and features derived from glottal waveforms.

Glottal waveform is significantly affected by the emotional state and speaking style of an individual [ 10 — 12 ]. In [ 10 — 12 ], researchers have investigated that the glottal waveform was affected due to the excessive tension or lack of coordination in the laryngeal musculature under different emotional states and the speech produced under stress. The classification of clinical depression using the glottal features was carried out by Moore et al.

Iliev and Scordilis have investigated the effectiveness of glottal features derived from the glottal airflow signal in recognizing emotions [ 16 ].

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The average emotion recognition rate of In [ 18 ], prosodic features, spectral features, glottal flow features, and AM-FM features were utilized and two-stage feature reduction was proposed for speech emotion recognition. The overall emotion recognition rate of Although all the above works are novel contributions to the field of speech emotion recognition, it is difficult to compare them directly since division of datasets is inconsistent: In this regard, the proposed methods were validated using three different emotional speech databases and emotion recognition experiments were also conducted under speaker-dependent and speaker-independent environments.

Materials and Methods 3. Emotional Speech Databases In this work, three different emotional speech databases were used for emotion recognition and to test the robustness of the proposed methods. First, Berlin emotional speech database BES was used which consists of speech utterances in German [ 19 ]. Secondly, Surrey audio-visual expressed emotion SAVEE database [ 20 ] was used and it is an audio-visual emotional database which includes seven emotion categories of speech utterances anger: In this work, only audio samples were utilized.

This database contains speech utterances of five basic emotions neutral: Figures 1 a — 1 d show an example of portion of utterance spoken by a speaker in the four different emotions neutral, anger, happiness, and disgust.

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It can be observed from the figures that the structure of the speech signals and its glottal waveforms are considerably different for speech spoken under different emotional states. Features for Speech Emotion Recognition Extraction of suitable features for efficiently characterizing different emotions is still an important issue in the design of a speech emotion recognition system. Short-term features were widely used by the researchers, called frame-by-frame analysis.

All the speech samples were downsampled to 8 kHz. The unvoiced portions between words were removed by segmenting the downsampled emotional speech signals into nonoverlapping frames with a length of 32 ms samples based on the energy of the frames.

Frames with low energy were discarded and the rest of the frames voiced portions were concatenated and used for feature extraction [ 17 ]. Then the emotional speech signals only voiced portions are passed through a first-order low pass filter to spectrally flatten the signal and to make it less susceptible to finite precision effects later in the signal processing [ 22 ].

The first-order preemphasis filter is defined as The commonly used value is or 0. In this work, the value of is set equal to 0. Extraction of glottal flow signal from speech signal is a challenging task. In this work, glottal waveforms were estimated based on the inverse filtering and linear predictive analysis from the preemphasized speech waveforms [ 569 ].


Wavelet or wavelet packet transform has the ability to analyze any nonstationary signals in both time and frequency domain simultaneously. In WP decomposition, both low and high frequency subbands are used to generate the next level subbands which results in finer frequency bands.

Introduction Temporal and spatial variations in extreme precipitation events often result in serious impacts on human society and ecological environment. And higher frequency of these extremes poses vast catastrophic consequences, including floods, landslides, and urban waterlog e. In recent years numerous disastrous floods have been documented worldwide, for example, the intense flash flooding occurred in Minnesota, Wisconsin, in the United States, in June, [ 3 ], and the extreme rainfall in Beijing, China, in July, [ 4 ]; all those events have caused devastating social impacts.

Moreover, in the context of global climate change, previous studies have suggested that many regions over the world would experience more frequent extreme precipitation with the enhancement of anthropogenic greenhouse gas and aerosol emissions e.

Therefore, projection of seasonal variations in precipitation extremes on local and regional scale is overwhelmingly important for reducing casualties and property losses as well as water resource management.

However, the two major current methods, for dynamical model and statistics, show low operational skills for forecasting local extreme precipitation. On the basis of dynamical prediction systems, general and regional circulation models GCMs and RCMs are useful to reproduce large-scale circulation, whereas they cannot very well characterize heavy rainfall features within 50 km, because local extreme precipitation is strongly influenced by land-air contrasts or topography, which are not well represented by coarse resolution models e.

Another commonly used prediction method, statistical downscaling, is to establish an empirical relationship between GCM-based quantitative predictions on large-scale and local climatic variable e. Therefore, the prediction results by using statistical downscaling methodology are based on the output of climatic model to some extent, which also cannot capture accurate changes of extreme precipitation in the future.

Consequently, the multiple regression, being applied to forecast regional heavy rainfall by utilizing historical rain gauge station dataset, has drawn more and more attention in recent decades. In China, Fan et al. Nevertheless, it should be noted that the trends of seasonal mean rainfall were not always consistent with the ones for extreme precipitation in many regions.

For instance, Alexander et al. For this reason, prediction of extreme precipitation in YRB is necessary and practical considering complex climatic conditions and dense population as well as rapid development of economy within the region.

However, previous studies were not available for forecasting extreme precipitation in YRB based on the statistical model using observed dataset; this is the motivation for us to conduct the present research to establish statistical prediction model with respect to extreme precipitation in YRB.

Data and Methodology 2. This is the longest river in China and the third longest river over the world. The YRB exhibits distinct changes of precipitation and is vulnerable to climate change e.

Frequent floods have occurred in history, leading to substantial losses in economy and lives in YRB. For example, the disastrous floods in triggered a death toll of around lives and direct economic losses of more than billion Yuan RMB 40 billion US dollars [ 16 ].

Meanwhile, previous investigations and studies e. Therefore, summer extreme precipitation events in YRB are considered in the present study to analyze their correlations with multiphysical factors.

The location of Yangtze River basin and the location of the meteorological stations. The quality control procedures have been conducted by CDC [ 19 ]. Methodology In order to combine stations over the whole basin without inducing a bias toward any station or other subregional stations, it is inappropriate to average all station precipitation amount over YRB.

The regional precipitation index proposed by Kraus [ 21 ] was adopted in this study.

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All standardized precipitation extremes over the YRB are then averaged in each year, producing a regional summer extreme precipitation index for every year at each station. It is noteworthy that the regional summer extreme indices in upper, middle, and lower reaches of YRB are also considered to calculate correlation coefficients, respectively. Nevertheless, their spatial distribution displays no significant differences compared to basin regional index Figures not shownalthough the rainfall in three reaches is dominated by different atmospheric circulations.

Thus, the regional summer extreme index for the whole basin is utilized in the present study. This is also consistent with the methods applied by Sun et al. The defined key regions that have significantly affected summer extreme precipitation in YRB according to correlation coefficients over the global ocean.

The solid line indicates three key regions selected in the present study. Dashed boxes represent insignificant impact. Multiple stepwise regression is used to select impact factors and establish statistical prediction model. The focus of stepwise regression would be the question of what the best combination of independent predictor variables would be to forecast the dependent predicted variable.

In a stepwise regression, predictor variables are entered into the regression equation one at a time based upon statistical criteria. At each step in the analysis the predictor variable that contributes the most to the prediction equation in terms of increasing the multiple correlation is entered first. This process is continued only if additional variables add anything statistically to the regression equation.

More detailed information about this method can be found in Myers [ 23 ]. After the establishment of statistical prediction model, the method of leave-one-out [ 24 ] is utilized to test the effect of the forecast model.

For example, one year summer extreme precipitation is taken out in each experiment during —, and this year is forecasted by utilizing the rest of the years.