Audio Noise Dataset

Noise is an unwanted behavior in audio datasets. Noise plays an important part in the machine learning field of audio data type.

The dataset can be used for noise filtering, noise generation & noise recognition in audio classification, audio recognition, audio generation, and audio-related machine learning. I, Min Si Thu, used this dataset on open-source projects.

I collected ten types of noise in this dataset.

Location - Myanmar, Mandalay, Amarapura Township

Ten types of noise

• the noise of a crowded place (Myanmar, Mandalay, Amarapura Township)
• the noise of urban areas with people talking (Myanmar, Mandalay, Amarapura Township)
• the noise of the restaurant (Myanmar, Mandalay, Amarapura Township, at a random restaurant)
• the noise of a working place, people's discussion (Myanmar, Mandalay, Amarapura Township, a private company)
• the noise of mosquitos (Myanmar, Mandalay, Amarapura Township, Dataset creator's home)
• the noise of car traffic (Myanmar, Mandalay, Amarapura Township, Asia Bank Road, nighttime)
• the noise of painful sounds (Myanmar, Mandalay, Amarapura Township)
• the noise of the rainy day (Myanmar, Mandalay, Amarapura Township, Dataset creator's home)
• the noise of motorbike and people talking (Myanmar, Mandalay, Amarapura Township, NanTawYar Quarter, Cherry Street)
• the noise of a festival (Myanmar, Mandalay, Chinese Festival)

DESCRIPTION This table contains data on the percent of residents aged 16 years and older mode of transportation to work for ...

SUMMARY This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

ind_id - Indicator ID ind_definition - Definition of indicator in plain language reportyear - Year that the indicator was reported race_eth_code - numeric code for a race/ethnicity group race_eth_name - Name of race/ethnic group geotype - Type of geographic unit geotypevalue - Value of geographic unit geoname - Name of a geographic unit county_name - Name of county that geotype is in county_fips - FIPS code of the county that geotype is in region_name - MPO-based region name; see MPO_County list tab region_code - MPO-based region code; see MPO_County list tab mode - Mode of transportation short name mode_name - Mode of transportation long name pop_total - denominator pop_mode - numerator percent - Percent of Residents Mode of Transportation to Work,
Population Aged 16 Years and Older LL_95CI_percent - The lower limit of 95% confidence interval UL_95CI_percent - The lower limit of 95% confidence interval percent_se - Standard error of the percent mode of transportation percent_rse - Relative standard error (se/value) expressed as a percent CA_decile - California decile CA_RR - Rate ratio to California rate version - Date/time stamp of a version of data

Static dataset describing the trajectories linked to multimodal counting sites (Bike, Scooter, 2WD, VL, HGV, Bus-car).

The City of Paris collects vehicle count data by:

• Travel modes (Scooters, Scooters + Bicycles (when the distinction between these 2 travel modes is not implemented on the sensor ), Bicycles, 2 motorized wheels, Light vehicles < 3.5 tonnes, Heavy vehicles > 3.5 tonnes, Buses & coaches),
• Traffic Lane Types (Corona Lanes, Bike Lanes, General Lanes),
• Direction of flow.

This data is built using an artificial intelligence algorithm which analyzes images from thermal cameras installed in public spaces.

The images from thermal cameras do not allow the identification of faces or license plates. The data collected in this way does not contain any personal or individual data.

No image is transferred or stored on computer servers, the analysis being carried out as close as possible to the thermal camera. Only counting data is transmitted.

This dataset feeds the counting dataset Multimodal count - Counts

Clarification on the content of the "Trajectory" field:

This is a character string designating the detection start zone (input) and the detection exit zone (Input > Output)

A trajectory is characterized by a direction of traffic and by the line of traffic taken by the vehicle on entry and exit.

Data set of hourly multimodal counts by travel mode from thermal sensors.

The City of Paris collects vehicle count data by:< /p>

• Movement modes (Scooters, Scooters + Bikes (when the distinction between these 2 travel modes is not implemented on the sensor ), Bicycles, 2 motorized wheels, Light vehicles < 3.5 tonnes, Heavy vehicles > 3.5 tonnes, Buses & coaches),
• Traffic lane types (Corona-paths, Cycle paths, General traffic lanes),
• Direction of traffic.

This data is constructed by exploiting an artificial intelligence algorithm which analyzes images from thermal cameras installed in public spaces.

Images from thermal cameras do not allow faces or license plates to be identified. The data collected in this way does not present personal or individual data.

No image is transferred or stored on computer servers, the analysis being carried out as close as possible to the thermal camera. Only counting data is transmitted.

This dataset is powered by the counting carried out by the sensors and the dataset describing the trajectories of the counting sites Multimodal counting - Counting sites and trajectories

The number of sensors and their ability to distinguish the type of vehicles (e.g. scooters and bicycles) may change over time.

Precision on the content of the field « Trajectory »:

It is a character string designating the detection start zone (input) and the detection output zone (Input > Output )

A trajectory is characterized by a direction of traffic and by the lane of traffic taken by the vehicle on entry and exit.

Example: A bicycle that can, in the detection zone of the thermal camera, enter the cycle path and exit on the general traffic lane.

You will find more details in the attached notice of the dataset.

Birds Population & Climate Data (Asia)

Explore the impact of climate change on bird populations across Asia from 1980 to 2010.

About This Dataset

Context

This dataset provides a unique look into the relationship between bird populations and key environmental factors over three decades. It contains records of bird species occurrences across several Asian countries, paired with crucial climate data like temperature and precipitation for the year of observation.

The data also includes novel features such as Shift_km, representing the potential geographic shift in a species' habitat, and Traffic, a proxy for human activity or urbanization. This makes the dataset ideal for analyzing the multifaceted impacts of climate change and human influence on avian life.

Content

The dataset contains 1000 records and 10 columns:

• Bird_Species: The scientific or common name of the bird species observed.
• Year: The year of the observation (ranging from 1980 to 2010).
• Country: The Asian country where the observation was recorded.
• Latitude: The geographic latitude of the observation point.
• Longitude: The geographic longitude of the observation point.
• Temperature: The average temperature (in Celsius) for the region during the observation period.
• Precipitation: The total precipitation (in mm) for the region during the observation period.
• Shift_km: A calculated value representing the potential geographic shift in the species' core habitat range in kilometers, possibly due to climate change.
• Population: The observed population count for that species at that location and time.
• Traffic: An index or count representing local traffic, likely serving as a proxy for human activity and urbanization levels.

Inspiration

This dataset is perfect for anyone interested in conservation, ecology, and climate science. Here are a few questions you could explore:

• Can you build a model to predict bird population numbers based on climate and human activity data?
• Which bird species are showing the steepest decline over the 30-year period?
• How strongly do temperature, precipitation, and habitat shift correlate with population changes?
• Can you create a map visualization to show population hotspots and how they've changed over time?