The statistic shows the average conversion rates for the travel industry in 2015, by device and region. In the United States, 0.7 percent of smartphone visits to travel sites were converted into purchases. U.S. desktop visits had a 2.4 percent conversion rate.

As of 2015, polystyrene resin accounted for a high plastic-to-fuel conversion rate, as around ** to ** percent of the material can be converted. As plastic production increases worldwide, the amount of this material's waste in landfills and the ocean has reached critical levels. Plastics-to-fuel (PTF) is a technology that aims to make use of the energy value of plastics to produce alternative fuel sources.

Market Overview:

The Data Converter market is projected to grow at a CAGR of 5.5% from 2017 to 2030. The growth of the data converter market can be attributed to the increasing demand for Data Converters in various applications, such as communications, automotive, consumer electronics, industrial, medical, and test and measurement.

Product Definition:

A data converter is a type of software used to convert data from one format to another. It is used to convert data from one type of file to another, such as from text to a spreadsheet, or from a text file to an image file. The data converter can also be used to convert data from one type of application to another, for example, from a spreadsheet to a word processor. This type of software is used to make sure data is compatible with different applications and can be used in different ways.

Analog-to-Digital Converters:

Analog-to-Digital Converters (ADCs) are devices that convert analog signals into digital signals. They are commonly used in digital systems to enable the conversion of analog data such as sound, temperature, pressure, and voltage into a format that can be used by digital systems. ADCs can be used to sample and convert analog signals into digital signals for processing in a computer or other digital system.

Digital-to-Analog Converters:

The digital-to-analog converters (DAC) are electronic devices that perform the conversion of a digital signal into an analog one. The DACs are used in data converters where they help convert the digital signals received from microprocessors or computers into an audio signal that can be played through speakers, headphones, and other audio equipment.

Application Insights:

The data converters sales market is segmented by application into communications, automotive, consumer electronics, industrial and medical. The communications segment dominated the overall industry in 2015 owing to the increasing demand for high-speed internet access and the rising number of users across the globe. The growing need for data transmission over long distances has led to an increase in demand for data converters in this sector. The automotive sector is projected to emerge as one of the fastest-growing segments over the forecast period owing to a rise in demand from emerging economies.

Regional Analysis:

The Asia Pacific regional market accounted for over 40% of the global revenue share in 2015 and is projected to continue its dominance over the forecast period. The growth can be attributed to rising demand from emerging countries such as China, India, and Japan. Increasing disposable income coupled with the growing consumer electronics industry is anticipated to drive demand further.

Growth Factors:

• Increasing demand for data converters in the industrial sector.
• The growing popularity of data converters in the telecommunications sector.
• Rising demand for miniaturized and high-performance data converters.
• The proliferation of digital signal processing (DSP) technology.
• The emergence of new applications that require data conversion.

Report Scope

Report Attributes	Report Details
Report Title	Data Converter Sales Market Research Report
By Type	Analog-to-Digital Converters, Digital-to-Analog Converters
By Application	Communications, Automotive, Consumer Electronics, Industrial, Medical, Test & Measurement
By Distribution Channel	Online, Offline
By End User	Residential, Commercial
By Price Range	Premium, Economy

Voice conversion (VC) is a technique to transform a speaker identity included in a source speech waveform into a different one while preserving linguistic information of the source speech waveform.

In 2016, we have launched the Voice Conversion Challenge (VCC) 2016 [1][2] at Interspeech 2016. The objective of the 2016 challenge was to better understand different VC techniques built on a freely-available common dataset to look at a common goal, and to share views about unsolved problems and challenges faced by the current VC techniques. The VCC 2016 focused on the most basic VC task, that is, the construction of VC models that automatically transform the voice identity of a source speaker into that of a target speaker using a parallel clean training database where source and target speakers read out the same set of utterances in a professional recording studio. 17 research groups had participated in the 2016 challenge. The challenge was successful and it established new standard evaluation methodology and protocols for bench-marking the performance of VC systems.

In 2018, we have launched the second edition of VCC, the VCC 2018 [3]. In the second edition, we revised three aspects of the challenge. First, we educed the amount of speech data used for the construction of participant's VC systems to half. This is based on feedback from participants in the previous challenge and this is also essential for practical applications. Second, we introduced a more challenging task refereed to a Spoke task in addition to a similar task to the 1st edition, which we call a Hub task. In the Spoke task, participants need to build their VC systems using a non-parallel database in which source and target speakers read out different sets of utterances. We then evaluate both parallel and non-parallel voice conversion systems via the same large-scale crowdsourcing listening test. Third, we also attempted to bridge the gap between the ASV and VC communities. Since new VC systems developed for the VCC 2018 may be strong candidates for enhancing the ASVspoof 2015 database, we also asses spoofing performance of the VC systems based on anti-spoofing scores.

In 2020, we launched the third edition of VCC, the VCC 2020 [4][5]. In this third edition, we constructed and distributed a new database for two tasks, intra-lingual semi-parallel and cross-lingual VC. The dataset for intra-lingual VC consists of a smaller parallel corpus and a larger nonparallel corpus, where both of them are of the same language. The dataset for cross-lingual VC consists of a corpus of the source speakers speaking in the source language and another corpus of the target speakers speaking in the target language. As a more challenging task than the previous ones, we focused on cross-lingual VC, in which the speaker identity is transformed between two speakers uttering different languages, which requires handling completely nonparallel training over different languages.

This repository contains the training and evaluation data released to participants, target speaker’s speech data in English for reference purpose, and the transcriptions for evaluation data. For more details about the challenge and the listening test results please refer to [4] and README file.

[1] Tomoki Toda, Ling-Hui Chen, Daisuke Saito, Fernando Villavicencio, Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "The Voice Conversion Challenge 2016" in Proc. of Interspeech, San Francisco.

[2] Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "Analysis of the Voice Conversion Challenge 2016 Evaluation Results" in Proc. of Interspeech 2016.

[3] Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda, Daisuke Saito, Fernando Villavicencio, Tomi Kinnunen, Zhenhua Ling, "The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods", Proc Speaker Odyssey 2018, June 2018.

[4] Yi Zhao, Wen-Chin Huang, Xiaohai Tian, Junichi Yamagishi, Rohan Kumar Das, Tomi Kinnunen, Zhenhua Ling, and Tomoki Toda. "Voice conversion challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion" Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 80-98, DOI: 10.21437/VCC_BC.2020-14.

Analysis of ‘Apartment balance after conversion and conversion category since 2015 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/815dd151-446d-47da-9153-73b9c845a466-stadt-zurich on 17 January 2022.

--- Dataset description provided by original source is as follows ---

The data set contains the housing balance by conversion category, year and size since 2015.

--- Original source retains full ownership of the source dataset ---

This statistic illustrates the average website conversion rate in Europe in 2015, by country or region and device, In 2015, the average conversion rate of websites accessed via smartphone in the United Kingdom (UK) was 1.2 percent.

All 311 Service Requests from 2010 to present. This information is automatically updated daily.

Click here to download data from 2011 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2011/fpz8-jqf4

Click here to download data from 2012 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2012/as38-8eb5

Click here to download data from 2013 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2013/hybb-af8n

Click here to download data from 2014 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2014/vtzg-7562

Click here to download data from 2015 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2015/57g5-etyj

This dataset tracks annual graduation rate from 2015 to 2017 for Norwood Conversion Community School vs. Ohio and Norwood Conversion Community School District

This dataset tracks annual reading and language arts proficiency from 2015 to 2017 for Norwood Conversion Community School vs. Ohio and Norwood Conversion Community School District

This dataset tracks annual overall school rank from 2015 to 2017 for Norwood Conversion Community School

Shipbuilding and Conversion, Navy account, Iteration 1, Fiscal year 2024

Gross domestic product per capita at current prices (converted to purchasing power parities) 1995-2015 by region and year Tables Fact, Gross Domestic Product Per Capita At Current Prices Converted To Purchasing Power Parities 1995 2015 By Region And YearTSV Gross domestic product per capita at current prices (converted to purchasing power parities) by region and year

This dataset tracks annual math proficiency from 2015 to 2017 for Norwood Conversion Community School vs. Ohio and Norwood Conversion Community School District

This dataset tracks annual two or more races student percentage from 2015 to 2023 for Kualapuu School - Public Conversion Charter vs. Hawaii and Hawaii Department Of Education School District

[250 Pages Report] The data conversion services market is likely to register a CAGR of 30.1% during the forecast period and is anticipated to reach a data conversion services market share of US$ 540795.08 Million in 2032, from US$ 38927.79 Million in 2022.

Attributes	Details
Data conversion services Market (CAGR)	30.1%
Data conversion services Market (2022)	US$ 38927.79 Mn
Data conversion services Market (2032)	US$ 540795.08 Mn

Scope of Report

Report Attribute	Details
Growth rate	CAGR of 30.1% from 2022 to 2032
Base year for estimation	2021
Historical data	2015 – 2020
Forecast period	2022 – 2032
Quantitative units	Revenue in USD Million and CAGR from 2022 to 2032
Report coverage	Revenue forecast, volume forecast, company ranking, competitive landscape, growth factors, and trends, Pricing Analysis
Segments covered	Service type, Enterprise Type, Industry, region
Regional scope	North America; Western Europe, Eastern Europe, Middle East, Africa, ASEAN, South Asia, Rest of Asia, Australia and New Zealand
Country scope	U.S.; Canada; Mexico; Germany; U.K.; France; Italy; Spain; Russia; Belgium; Poland; Czech Republic; China; India; Japan; Australia; Brazil; Argentina; Colombia; Saudi Arabia; UAE; Iran; South Africa
Key companies profiled	IBM Corporation; Oracle; Amazon Web Services; Microsoft; SAS Institute; SAP SE; Informatica; Talend; Attunity; TIBCO Software; Invensis Technologies Pvt Ltd; Syncsort, Damco Solutions and others
Customization scope	Free report customization (equivalent to up to 8 analysts’ working days) with purchase. Addition or alteration to country, regional & segment scope.
Pricing and purchase options	Avail customized purchase options to meet your exact research needs.

South Korea ImPI: USD: MI: EE: ECDC: Electricity Conversion Apparatus data was reported at 95.290 2015=100 in Apr 2019. This stayed constant from the previous number of 95.290 2015=100 for Mar 2019. South Korea ImPI: USD: MI: EE: ECDC: Electricity Conversion Apparatus data is updated monthly, averaging 95.750 2015=100 from Jan 2018 (Median) to Apr 2019, with 16 observations. The data reached an all-time high of 96.250 2015=100 in May 2018 and a record low of 95.290 2015=100 in Apr 2019. South Korea ImPI: USD: MI: EE: ECDC: Electricity Conversion Apparatus data remains active status in CEIC and is reported by The Bank of Korea. The data is categorized under Global Database’s South Korea – Table KR.I080: Import Price Index (USD Basis): 2015=100.

Shipbuilding and Conversion, Navy account, Iteration 2, Fiscal year 2023

Voice conversion (VC) is a technique to transform a speaker identity included in a source speech waveform into a different one while preserving linguistic information of the source speech waveform.

In 2016, we have launched the Voice Conversion Challenge (VCC) 2016 [1][2] at Interspeech 2016. The objective of the 2016 challenge was to better understand different VC techniques built on a freely-available common dataset to look at a common goal, and to share views about unsolved problems and challenges faced by the current VC techniques. The VCC 2016 focused on the most basic VC task, that is, the construction of VC models that automatically transform the voice identity of a source speaker into that of a target speaker using a parallel clean training database where source and target speakers read out the same set of utterances in a professional recording studio. 17 research groups had participated in the 2016 challenge. The challenge was successful and it established new standard evaluation methodology and protocols for bench-marking the performance of VC systems.

In 2018, we have launched the second edition of VCC, the VCC 2018 [3]. In the second edition, we revised three aspects of the challenge. First, we educed the amount of speech data used for the construction of participant's VC systems to half. This is based on feedback from participants in the previous challenge and this is also essential for practical applications. Second, we introduced a more challenging task refereed to a Spoke task in addition to a similar task to the 1st edition, which we call a Hub task. In the Spoke task, participants need to build their VC systems using a non-parallel database in which source and target speakers read out different sets of utterances. We then evaluate both parallel and non-parallel voice conversion systems via the same large-scale crowdsourcing listening test. Third, we also attempted to bridge the gap between the ASV and VC communities. Since new VC systems developed for the VCC 2018 may be strong candidates for enhancing the ASVspoof 2015 database, we also asses spoofing performance of the VC systems based on anti-spoofing scores.

In 2020, we launched the third edition of VCC, the VCC 2020 [4][5]. In this third edition, we constructed and distributed a new database for two tasks, intra-lingual semi-parallel and cross-lingual VC. The dataset for intra-lingual VC consists of a smaller parallel corpus and a larger nonparallel corpus, where both of them are of the same language. The dataset for cross-lingual VC consists of a corpus of the source speakers speaking in the source language and another corpus of the target speakers speaking in the target language. As a more challenging task than the previous ones, we focused on cross-lingual VC, in which the speaker identity is transformed between two speakers uttering different languages, which requires handling completely nonparallel training over different languages.

As for listening test, we subcontracted the crowd-sourced perceptual evaluation with English and Japanese listeners to Lionbridge TechnologiesInc. and Koto Ltd., respectively. Given the extremely large costs required for the perceptual evaluation, we selected 5 utterances (E30001, E30002, E30003,E30004, E30005) only from each speaker of each team. To evaluate the speaker similarity of the cross-lingual task, we used audio in both the English language and in the target speaker’s L2language as reference. For each source-target speaker pair, we selected three English recordings and two L2 language recordings as the natural reference for the converted five utterances.

This data repository includes the audio files used for the crowd-sourced perceptual evaluation and raw listening test scores.

[1] Tomoki Toda, Ling-Hui Chen, Daisuke Saito, Fernando Villavicencio, Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "The Voice Conversion Challenge 2016" in Proc. of Interspeech, San Francisco.

[2] Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "Analysis of the Voice Conversion Challenge 2016 Evaluation Results" in Proc. of Interspeech 2016.

[3] Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda, Daisuke Saito, Fernando Villavicencio, Tomi Kinnunen, Zhenhua Ling, "The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods", Proc Speaker Odyssey 2018, June 2018.

[4] Yi Zhao, Wen-Chin Huang, Xiaohai Tian, Junichi Yamagishi, Rohan Kumar Das, Tomi Kinnunen, Zhenhua Ling, and Tomoki Toda. "Voice conversion challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion" Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 80-98, DOI: 10.21437/VCC_BC.2020-14.

[5] Rohan Kumar Das, Tomi Kinnunen, Wen-Chin Huang, Zhenhua Ling, Junichi Yamagishi, Yi Zhao, Xiaohai Tian, and Tomoki Toda. "Predictions of subjective ratings and spoofing assessments of voice conversion challenge 2020 submissions." Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 99-120, DOI: 10.21437/VCC_BC.2020-15.

Historical Dataset of Norwood Conversion Community School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2016-2023),Total Classroom Teachers Trends Over Years (2016-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2016-2023),Hispanic Student Percentage Comparison Over Years (2016-2023),Black Student Percentage Comparison Over Years (2016-2023),White Student Percentage Comparison Over Years (2016-2023),Two or More Races Student Percentage Comparison Over Years (2016-2023),Diversity Score Comparison Over Years (2016-2023),Reading and Language Arts Proficiency Comparison Over Years (2015-2017),Math Proficiency Comparison Over Years (2015-2017),Overall School Rank Trends Over Years (2015-2017),Graduation Rate Comparison Over Years (2015-2017)

In 2023, the value of plastics converted to raw materials exported from the European Union amounted to 41.1 billion euros, higher than the value of imported converted plastic of 33.8 billion euros. There was an export surplus of 7.3 billion euros that year.