Context

Our target was to predict gender, age and emotion from audio. We found audio labeled datasets on Mozilla and RAVDESS. So by using R programming language 20 statistical features were extracted and then after adding the labels these datasets were formed. Audio files were collected from "Mozilla Common Voice" and “Ryerson AudioVisual Database of Emotional Speech and Song (RAVDESS)”.

Content

Datasets contains 20 feature columns and 1 column for denoting the label. The 20 statistical features were extracted through the Frequency Spectrum Analysis using R programming Language. They are: 1) meanfreq - The mean frequency (in kHz) is a pitch measure, that assesses the center of the distribution of power across frequencies. 2) sd - The standard deviation of frequency is a statistical measure that describes a dataset’s dispersion relative to its mean and is calculated as the variance’s square root. 3) median - The median frequency (in kHz) is the middle number in the sorted, ascending, or descending list of numbers. 4) Q25 - The first quartile (in kHz), referred to as Q1, is the median of the lower half of the data set. This means that about 25 percent of the data set numbers are below Q1, and about 75 percent are above Q1. 5) Q75 - The third quartile (in kHz), referred to as Q3, is the central point between the median and the highest distributions. 6) IQR - The interquartile range (in kHz) is a measure of statistical dispersion, equal to the difference between 75th and 25th percentiles or between upper and lower quartiles. 7) skew - The skewness is the degree of distortion from the normal distribution. It measures the lack of symmetry in the data distribution. 8) kurt - The kurtosis is a statistical measure that determines how much the tails of distribution vary from the tails of a normal distribution. It is actually the measure of outliers present in the data distribution. 9) sp.ent - The spectral entropy is a measure of signal irregularity that sums up the normalized signal’s spectral power. 10) sfm - The spectral flatness or tonality coefficient, also known as Wiener entropy, is a measure used for digital signal processing to characterize an audio spectrum. Spectral flatness is usually measured in decibels, which, instead of being noise-like, offers a way to calculate how tone-like a sound is. 11) mode - The mode frequency is the most frequently observed value in a data set. 12) centroid - The spectral centroid is a metric used to describe a spectrum in digital signal processing. It means where the spectrum’s center of mass is centered. 13) meanfun - The meanfun is the average of the fundamental frequency measured across the acoustic signal. 14) minfun - The minfun is the minimum fundamental frequency measured across the acoustic signal 15) maxfun - The maxfun is the maximum fundamental frequency measured across the acoustic signal. 16) meandom - The meandom is the average of dominant frequency measured across the acoustic signal. 17) mindom - The mindom is the minimum of dominant frequency measured across the acoustic signal. 18) maxdom - The maxdom is the maximum of dominant frequency measured across the acoustic signal 19) dfrange - The dfrange is the range of dominant frequency measured across the acoustic signal. 20) modindx - the modindx is the modulation index, which calculates the degree of frequency modulation expressed numerically as the ratio of the frequency deviation to the frequency of the modulating signal for a pure tone modulation.

Acknowledgements

Gender and Age Audio Data Souce: Link: https://commonvoice.mozilla.org/en Emotion Audio Data Souce: Link : https://smartlaboratory.org/ravdess/

The following dataset is intended to be used for gender recognition using audio files in uncontrolled environments from the Mozilla Common Voice Dataset 10.0. It consists of a table of descriptive statistical characteristics of the fundamental frequency of six tonal languages Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Thai, Vietnamese, and Punjabi. In addition, the estimation of the vocal tract of each of the speakers.

This dataset contains 18 columns: 'client_id': id speaker from Mozilla Common Voice 'path': Name of the mp3 file 'sentence': The sentence spoken by the speaker 'age': Age in decades (teens, twenties, etc.) 'gender': Binary gender (male or female) 'duration': Duration of mp3 in seconds 'vocal_tract_length': Vocal tract length in cm. 'mean_F4': Mean of the fourth formant in Hz. 'min_pitch': Minimal pitch of the whole pitch contour in Hz. 'mean_pitch': Mean pitch of the whole pitch contour in Hz. 'q1_pitch': : First quartile of the whole pitch contour in Hz. 'median_pitch': : Median pitch of the whole pitch contour Hz. 'q3_pitch': : Third quartile of the whole pitch contour in Hz. 'max_pitch': : Max pitch of the whole pitch contour in Hz. 'stddev_pitch' : Standard deviation of the whole pitch contour in Hz. 'estimated_age': Nominal value (adult or teen) 'estimated_age_gender: Nominal value (adult-male, adult-female, teen-male and teen-female). 'language': Nominal value (Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Thai, Vietnamese, and Punjabi).

The methodology for the extraction of these characteristics was the following:

Only the audios from the valid.tsv file of the respective language were analyzed (this file is contained in the Mozilla Common Voice Dataset https://commonvoice.mozilla.org/en/datasets ) the voiced-speech was extracted using Praat's algorithm Vocal ToolKit (https://www.praatvocaltoolkit.com/extract-voiced-and-unvoiced.html)

2) The vocal tract length was calculated with the Vocal Tool Kit algorithm ( https://www.praatvocaltoolkit.com/calculate-vocal-tract-length.html ) as follows: If the audio came from a teen, then the maximum formant was established at 8000, otherwise it was adjusted to 5000 Hz for men and 5500 for women. Finally, the mean of the fourth formant was calculated for the windows with voiced speech only.

3) The fundamental frequency was calculated using the PRAAT Software in the To Pitch (ac) option and a) Time step (s) 0.0 (=auto) b) Pitch floor (Hz) 75.0 c) Max. number of candidates 15 d) Vey accurate=True e) Silence Threshold= 0.03 f) Voicing threshold= 0.45 g) Octave Cost= 0.01 h) Octave jump cost = 0.35 i) Voiced/ Unvoiced cost= 0.14 j) Pitch ceiling (Hz) = 350

4) The statistical characteristics of the fundamental frequency were calculated only in the windows that were detected as voiced speech.

A live version of the data record, which will be kept up-to-date with new estimates, can be downloaded from the Humanitarian Data Exchange: https://data.humdata.org/dataset/covid-19-mobility-italy.

If you find the data helpful or you use the data for your research, please cite our work:

Pepe, E., Bajardi, P., Gauvin, L., Privitera, F., Lake, B., Cattuto, C., & Tizzoni, M. (2020). COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Scientific Data 7, 230 (2020).

The data record is structured into 4 comma-separated value (CSV) files, as follows:

id_provinces_IT.csv. Table of the administrative codes of the 107 Italian provinces. The fields of the table are:

COD_PROV is an integer field that is used to identify a province in all other data records;

SIGLA is a two-letters code that identifies the province according to the ISO_3166-2 standard (https://en.wikipedia.org/wiki/ISO_3166-2:IT);

DEN_PCM is the full name of the province.

OD_Matrix_daily_flows_norm_full_2020_01_18_2020_04_17.csv. The file contains the daily fraction of users’ moving between Italian provinces. Each line corresponds to an entry of matrix (i, j). The fields of the table are:

p1: COD_PROV of origin,

p2: COD_PROV of destination,

day: in the format yyyy-mm-dd.

median_q1_q3_rog_2020_01_18_2020_04_17.csv. The file contains median and interquartile range (IQR) of users’ radius of gyration in a province by week. Each entry of the table fields of the table are:

COD_PROV of the province;

SIGLA of the province;

DEN_PCM of the province;

week: median value of the radius of gyration on week week, with week in the format dd/mm-DD/MM where dd/mm and DD/MM are the first and the last day of the week, respectively.

week Q1 first quartile (Q1) of the distribution of the radius of gyration on week week,

week Q3 third quartile (Q3) of the distribution of the radius of gyration on week week,

average_network_degree_2020_01_18_2020_04_17.csv. The file contains daily time-series of the average degree 〈k〉 of the proximity network. Each entry of the table is a value of 〈k〉 on a given day. The fields of the table are:

COD_PROV of the province;

SIGLA of the province;

DEN_PCM of the province;

day in the format yyyy-mm-dd.

ESRI shapefiles of the Italian provinces updated to the most recent definition are available from the website of the Italian National Office of Statistics (ISTAT): https://www.istat.it/it/archivio/222527.

The following dataset is intended to be used for gender recognition using audio files in uncontrolled environments from the Mozilla Common Voice Dataset 10.0. It consists of a table of descriptive statistical characteristics of the fundamental frequency of Spanish language. In addition, the estimation of the vocal tract of each of the speakers.

This dataset contains 18 columns: 'client_id': id speaker from Mozilla Common Voice 'path': Name of the mp3 file 'age': Age in decades (teens, twenties, etc.) 'gender': Binary gender (male or female) 'duration': Duration of mp3 in seconds 'vocal_tract_length': Vocal tract length in cm. 'mean_F4': Mean of the fourth formant in Hz. 'min_pitch': Minimal pitch of the whole pitch contour in Hz. 'mean_pitch': Mean pitch of the whole pitch contour in Hz. 'q1_pitch': : First quartile of the whole pitch contour in Hz. 'median_pitch': : Median pitch of the whole pitch contour Hz. 'q3_pitch': : Third quartile of the whole pitch contour in Hz. 'max_pitch': : Max pitch of the whole pitch contour in Hz. 'stddev_pitch' : Standard deviation of the whole pitch contour in Hz. 'estimated_age': Nominal value (adult or teen) 'estimated_age_gender: Nominal value (adult-male, adult-female, teen-male and teen-female). 'language': Nominal value (Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Thai, Vietnamese, and Punjabi).

The methodology for the extraction of these characteristics was the following:

Only the audios from the valid.tsv file of the respective language were analyzed (this file is contained in the Mozilla Common Voice Dataset https://commonvoice.mozilla.org/en/datasets ) the voiced-speech was extracted using Praat's algorithm Vocal ToolKit (https://www.praatvocaltoolkit.com/extract-voiced-and-unvoiced.html)

2) The vocal tract length was calculated with the Vocal Tool Kit algorithm ( https://www.praatvocaltoolkit.com/calculate-vocal-tract-length.html ) as follows: If the audio came from a teen, then the maximum formant was established at 8000, otherwise it was adjusted to 5000 Hz for men and 5500 for women. Finally, the mean of the fourth formant was calculated for the windows with voiced speech only.

3) The fundamental frequency was calculated using the PRAAT Software in the To Pitch (ac) option and a) Time step (s) 0.0 (=auto) b) Pitch floor (Hz) 75.0 c) Max. number of candidates 15 d) Vey accurate=True e) Silence Threshold= 0.03 f) Voicing threshold= 0.45 g) Octave Cost= 0.01 h) Octave jump cost = 0.35 i) Voiced/ Unvoiced cost= 0.14 j) Pitch ceiling (Hz) = 350

4) The statistical characteristics of the fundamental frequency were calculated only in the windows that were detected as voiced speech.

Notice: Due to funding limitations, this data set was recently changed to a “Basic” Level of Service. Learn more about what this means for users and how you can share your story here: Level of Service Update for Data Products.NOTE: The data product titled U.S. National Ice Center Arctic and Antarctic Sea Ice Concentration and Climatologies in Gridded Format supersedes this product. It begins with charts from January 2003 and is updated weekly. The U.S. National Ice Center (NIC) is an inter-agency sea ice analysis and forecasting center comprised of the Department of Commerce/NOAA, the Department of Defense/U.S. Navy, and the Department of Homeland Security/U.S. Coast Guard components. Since 1972, NIC has produced Arctic and Antarctic sea ice charts. This data set is comprised of Arctic sea ice concentration climatology derived from the NIC weekly or biweekly operational ice-chart time series. The charts used in the climatology are from 1972 through 2007; and the monthly climatology products are median, maximum, minimum, first quartile, and third quartile concentrations, as well as frequency of occurrence of ice at any concentration for the entire period of record as well as for 10-year and 5-year periods. These climatologies and the charts from which they are derived are provided in the 25-km Equal-Area Scalable Earth Grid (EASE-Grid) binary (.bin) format. The climatologies are also available in ArcGIS geodatabases (.mdb), and GIF format browse files (.gif) are also provided.To view the browse files and compare climatological periods visually, choose Search Database under the Download Data tab.

Notice: Due to funding limitations, this data set was recently changed to a “Basic” Level of Service. Learn more about what this means for users and how you can share your story here: Level of Service Update for Data Products.NOTE: The data product titled U.S. National Ice Center Arctic and Antarctic Sea Ice Concentration and Climatologies in Gridded Format supersedes this product. It begins with charts from January 2003 and is updated weekly. The U.S. National Ice Center (NIC) is an inter-agency sea ice analysis and forecasting center comprised of the Department of Commerce/NOAA, the Department of Defense/U.S. Navy, and the Department of Homeland Security/U.S. Coast Guard components. Since 1972, NIC has produced Arctic and Antarctic sea ice charts. This data set is comprised of Arctic sea ice concentration climatology derived from the NIC weekly or biweekly operational ice-chart time series. The charts used in the climatology are from 1972 through 2007; and the monthly climatology products are median, maximum, minimum, first quartile, and third quartile concentrations, as well as frequency of occurrence of ice at any concentration for the entire period of record as well as for 10-year and 5-year periods. These climatologies and the charts from which they are derived are provided in the 25-km Equal-Area Scalable Earth Grid (EASE-Grid) binary (.bin) format. The climatologies are also available in ArcGIS geodatabases (.mdb), and GIF format browse files (.gif) are also provided.To view the browse files and compare climatological periods visually, choose Search Database under the Download Data tab.

Main land use classesThis file contains GIS information on the main land use classes over the study area (French Alps) at a resolution of 1*1km. Classes come from aggregated Corine Land Cover 2006 classes.land_use.zipRichness in ecosystem servicesThis file contains GIS information on the aggregated distribution of ecosystem services (ES) over the study area (French Alps). Sixteen ES were included as binary datasets (presence/absence - threshold at third quartile) to calculate the number of present ES at a resolution of 1*1km.ES_richness.zipSelf-Organising Map on ecosystem service distributionsThis file contains GIS information on the self-organising map obtained from ecosystem service (ES) distributions over the study area (French Alps) at a resolution of 1*1km. Sixteen ES were included to cluster the area into five groups presenting similar ecological profiles. The Self-Organising Map was obtained using Kohonen's algorithms.SOM.zip

This dataset contains three summaries. These were generated through continuous optimization of the CEC2020 and SOCO2011 benchmarks, as well as for a real-world application involving portfolio optimization with the SP500 dataset. The objective of these experiments is to analyze the performances and behaviors of a family of optimization algorithms, we named: 'fractal-based decomposition algorithms'. They hierarchically decompose a continuous search space using a self-similar and self-recurrent geometrical object. These algorithms can be described by five elementary building blocks: fractal, tree search, scoring, exploration and exploitation search components. To obtain these experimental results, we used 11 different algorithms instantiated with our Python package named 'Zellij'. Each of these algorithms has a unique combination of building blocks. Thus, one can analyze the sensitivity to the five components, dimensionality, and problem definition. The summaries are provided as CSV files. Each file contains 10 features (8 for summary_sp500.tab): the dimensionality of the benchmark function, the optimization algorithm, the benchmark function and basic statistics regarding the errors computed using the raw data (minimum, maximum, mean, standard error, median, first, and third quartile). Summaries are named according to the three experiments: summary_cec2020.tab, summary_socco2011.tab, summary_sp500.tab. Raw data is provided as a compressed ZIP file, named raw_data.zip, it contains three folders for the three experiments. Each experiment folder contains subfolders for each algorithm, dimension, and benchmark function. These are organized as follows: /experiment_{name}/{algorithm}/D{dimension}_{function}_save/outputs/all_evaluations.csv. A README.md file is provided for further technical details.

CARDS Monthly Statistics is digital data set DSI-6307, archived at the National Climatic Data Center (NCDC). This data set uses data from Comprehensive Aerological Data Set (CARDS) (DSI-6305), also archived at NCDC. DSI-6307 is similar in concept and format to Monthly Aerological Data Set (MONADS) (DSI-6220), another digital data set archived at NCDC. DSI-6305 and DSI-6220 are monthly upper air statistics. DSI-6307 data are for surface, tropopause, and mandatory pressure levels. At each level, monthly statistical parameters are provided for geopotential height or pressure, temperature, relative humidity, specific humidity, dew point temperature, wind speed, zonal wind speed, and meridional wind speed. Those statistical parameters are: mean value; standard deviation; minimum value; maximum value; first, second, and third quartile values, and number of non-missing observations used in the calculations. Data are global, from 1948 through 2001. NCDC maintains this data set in archive but no longer updates nor actively distributes it. It has been superseded by the Integrated Global Radiosonde Archive (IGRA) (C00616).

Supplementary figure 1Rank abundance distributions for habitats at three taxonomic levelsSuppl_fig_1.pdfSupplementary figure 2Evenness and species richness of the four habitats at three taxonomic levels.Suppl_fig_2.pdfSupplementary figure 3Distribution of p-values from Mantel test for Spearman correlation between dissimilarity matrices representing different taxonomic and numerical levels. A-C, Correlation between taxonomic levels at different numerical resolutions. D-F, Correlation between proportional abundance data and higher levels of numerical transformation. Filled points represent median p-values across 1000 subsampling iterations, empty points are outliers that lie beyond 1.5 times the interquartile range from the upper quartile.Suppl_fig_3.pdfSupplementary figure 4NMDS ordination of a double-standardized subsample of the total dataset comparing individual habitats along the depth- and salinity gradient for species and families using proportional abundances and presence/absence ...

This CD-ROM atlas contains statistics of 45 years of summer (July through September) temperature and salinity data. All temperatures in this atlas are shown as potential temperatures. Salinities are reported in standard salinity units. Densities are reported as potential density. Minimum, maximum, mean, standard deviation, skewness, kurtosis, 1st quartile, median, and 3rd quartile of the temperature and salinity data were calculated for each decade in the data set and over each 200 km by 200 km grid cell within the central Arctic region. In the Nordic and Siberian Seas, the statistics were calculated over 50 km by 50 km cells, as well as 100 km by 100 km cells, due to the smaller spatial scales in this region. These statistics were compiled for depths (in meters below the ocean surface) of 5, 10, 25, 50, 75, 100, 150, 200, 250, 300, 400, 500, 750, 1000, 1500, 2000, 2500, 3000, 3500, and 4000. The ocean bottom bathymetry was defined using the National Geophysical Data Center ETOPO5 digital data set. The decadal periods are: 1950-1959, 1960-1969, 1970-1979, 1980-1989. The number of original data points used to calculate the statistics are recorded for each location and a planar fit to the data, at each depth, was also generated to indicate linear trends within each cell. The three coefficients of the planar fit and the root-mean-square error of the fit are also recorded in the atlas. It was agreed to publish these statistics because most of the Russian original data could not be released. The CD-ROM atlas also contains a complete set of statistics for the periods, June, October and November. Statistics for December through May are available in the atlas for the winter period.