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Bebe Buell Images

Feb 25, 2020 - Explore Brenda Thensted's board 'BEBE BUELL', followed by 870 people on Pinterest. See more ideas about bebe buell, bebe, groupies.

Liv's parents, her mother, Bebe Buell, a model and singer, and her then-partner, Todd Rundgren, a phenomenal musician, and Steven had made a pact that Rundgren would be Liv's father. However, Liv 'had a hunch as early as nine years old, but it wasn’t until August 1988 when she turned 11 that she put it together and confronted Buell.

Feb 25, 2020 - Explore Brenda Thensted's board 'BEBE BUELL', followed by 870 people on Pinterest. See more ideas about bebe buell, bebe, groupies.

Bebe Buell 70s

Bebe Buell Net Worth

Bebe Buell Pictures

Bebe Buell Twitter

Culture | October 17, 2020

Bebe Buell had just come to New York from the South and had met a young man who owned a recording studio. 'He must have thought I was great,' she recalls. 'He hung photographs of me all over his studio.' And everyone who passed through the studio - recording engineers, producers, musicians - saw the pictures. Jun 13, 2019 - Explore Bubbles's board 'Bebe Buell' on Pinterest. See more ideas about bebe buell, bebe, groupies.

Photo of Bebe Buell circa 1980. Photo by Michael Ochs Archives/Getty Images

Bebe Buell, mother of Liv Tyler, never liked the word 'groupie' -- she prefers to describe herself as a 'muse' when discussing her serial romantic relationships with rock stars. Mick Jagger, Rod Stewart, Todd Rundgren, Iggy Pop, Elvis Costello, David Bowie and Jimmy Page all fell under Buell's spell, with Aerosmith's Steven Tyler fathering Buell's famous actress daughter.

Rock and roll means many things to its various practitioners, but for decades now one of the key reasons a teenaged boy will pick up a guitar is to get girls. In this admittedly sexist arrangement, the girls can end up being quite influential, inspiring the love or lust that comes blaring out of speakers turned to 11 -- thus Buell's preference for 'muse.' Numerous songs were written about her by her rock star lovers, including Rundgren's 'Can We Still Be Friends?' and Costello's 'I Want You.' While charming her way into the hearts of the most prominent musicians, the 'Mother of Rock and Roll' was also tending to her own career as a model and singer. As a symbol of the 1970s, Buell led a life of success, excess and public controversy.

Bebe Buell Got Her Start With The Ford Modeling Agency

Source: LouderSound.com

All the way back to the young age of five, Beverle Lorence “Bebe” Buell was obsessed with rock ‘n’ roll. She considered Mick Jagger (with whom she would later have an affair) her biggest inspiration and would stand in front of the mirror imitating his dance moves. After graduating high school, her passion and beauty led her to agent Eileen Ford of Ford Modeling Agency who signed Buell and relocated her from Virginia Beach, Virginia to the electrifying New York City. Buell immediately became a modeling star in the Big Apple and she immersed herself into Manhattan’s music scene befriending all of the top rockers in the area. Her captivating charisma drew everyone into her presence hoping to at least become her friend.

Buell Did Racier Modeling Than Ford Would Allow

Source: Pinterest

Buell was hanging out and drinking wine with acclaimed photographer Lynn Goldsmith one night, when Goldsmith asked if they could take some “artsy” pictures. “Artsy” was photographer code for “nude” and so Buell was photographed baring it all for the first time. Impressed, Goldsmith brought the pictures to Playboy magazine, who instantly wanted to work with her after admiring her stunning photos. Buell was flown to Chicago to stay at the original Playboy mansion and thus became the first fashion model turned Playboy centerfold and was Miss November 1974. However, Ford Modeling Agency was upset by her nude pictorials, so Buell was fired, but this did not stunt her career as she quickly signed with the Wilhelmina Agency soon after. Her work grew even greater as she posed for Italian & French Vogue, Harper’s Bazaar, and Cosmopolitan just to name a few.

Todd Rundgren Was Buell's Most Significant Rock-Star Relationship

Buell with Todd Rendgren. Source: blogspot.com

Buell was notorious for her many love affairs with top rockers including David Bowie, Iggy Pop, Mick Jagger, Jimmy Page, Rod Stewart, Elvis Costello, and the list goes on. Buell was nicknamed “Friend To The Stars” as she befriended (not just dated) all the celebrities she met which even included Jack Nicholson and Andy Warhol. People flocked to her “legendary beauty” and Mick Jagger even once said she would be the woman he would bring if he were to dine with royalty. The most famous of all her relationships was her on-and-off again romance with Todd Rundgren from 1972 to 1978. The pair became known as “rock royalty” compared to the prominent couples like Bianca & Mick Jagger and Angie & David Bowie.

Buell, Rundgren And Steven Tyler Made A Pact

Buell with Steven Tyler. Source: Pinterest

Scandal ensued with Buell’s accidental pregnancy in 1976 when the father was not her long-term boyfriend Todd Rundgren. Buell was impregnated from her quick fling with Aerosmith frontman Steven Tyler, but realized he would not make the greatest father with his heavy drug abuse during the ‘70s. Buell, Tyler, and Rundgren came to an agreement that Todd would raise the child as her father to provide a more stable family environment. If they felt it was needed, the secret would be revealed when she turned 18. However, their daughter, future model/actress Liv Tyler, became suspicious as she grew older so Buell disclosed the truth about her father when she turned 11 in 1988. Rundgren and Buell’s romantic relationship broke off shortly after Liv was born.

Buell's On-Again, Off-Again Music Career

Bebe Buell on the cover of her EP 'Covers Girl.' Source: Discogs.com

Throughout Buell’s entire journey, her true goal was to become a singer from the very beginning. Buell and Patti Smith had formed a close friendship and spent wine-filled nights writing poetry and singing together which was why Buell called Smith one of her biggest inspirations. In 1981, Buell finally released her debut EP Covers Girl, produced by Ric Ocasek of The Cars and Rick Derringer, with The Cars backing each of the four songs. Two years later, Buell formed a group called The B-Sides and released A Side Of The B-Sides which was produced by her former lover Todd Rundgren. She then broke musical boundaries of the time with The Gargoyles, a hard rock band she fronted during an era where women leading edgy bands was a rare commodity. Buell established a great musical name for herself with The Gargoyles as they toured throughout the United States and The UK, eventually opening up for The Ramones for numerous shows. Nevertheless, news about Steven Tyler’s paternity with Buell broke out in 1991 and forced Buell to back off from the public to focus on her daughter. Buell spent this time managing Liv Tyler’s acting/modeling career, as well as the career of her friend Debbie Harry for a short while. She returned back to music in the mid-‘90s when she released the punk/hard rock single “Retrosexual” and formed The Bebe Buell Band.

Bebe Buell Partly Inspired Penny Lane Of 'Almost Famous'

Source: Punk Globe

Filmmaker Cameron Crowe was so intrigued by Bebe Buell that he used her as an inspiration for his 2000 masterpiece film Almost Famous. The movie revolves around a touring band in the 1970s and their free-spirited groupies (they call themselves “Band-Aids”). Queen groupie Penny Lane of the flick was based off of many of the rock ‘n’ roll women of the era including Pamela Des Barres and Bebe Buell herself. Crowe even named the lead singer Jeff Bebe after Buell. At age 67 Buell still won’t slow down as she spends her time singing and modeling. In 2001 she released an autobiography “Rebel Heart: An American Rock And Roll Journey” all about her life experiences. She can be seen performing around Nashville often where she currently lives with her husband Jim Walters.

From the Web

Bebe Buell 70s

67 Colorized Photos Captured Way More Than Expected

Bebe Buell Net Worth

History Daily

Rarely Seen Photos From The 70s for Mature Audiences Only

History Daily

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Emily Morenz

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Despite her younger age, Emily Morenz (Emo) is a serious 1960s/1970s enthusiast who is pretty much the Austin Powers of this decade. Through her all-vintage wardrobe, obsession with old time rock 'n' roll, and her mid century bedroom and 1,200+ vinyl collection you might think she just stepped out of a time machine. Emo plays the rare gems of the ‘60s and ‘70s on her radio show on OC’s 101.5 KOCI and teaches rock ‘n’ roll history on her podcast “The Rock & Roll Sweetheart.” When there's not a pandemic, she's rockin’ out with all the middle aged-men at every single classic rock concert happening around the town, and she will battle her away to front row and dance hard. Paul McCartney even once brought her up on stage to dance...while she was in a walrus costume. You also might find Emo surfing waves, skateboarding through a neighborhood, groovin' '60s gogo style, and pretending like she can play bass. And she's obsessed with peanut butter and corgis.

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blogauthority512 · 4 years ago

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How To Get Campaigns Folder For Mac Warcraft Iii

How To Get Campaigns Folder For Mac Warcraft Iii Pc

How To Get Campaigns Folder For Mac Warcraft Iii 4

How To Get Campaigns Folder For Mac Warcraft Iii Reforged

How To Get Campaigns Folder For Mac Warcraft Iii 3

How To Get Campaigns Folder For Mac Warcraft Iii Classic

Download games and applications from Blizzard and partners. Hive Workshop: not hesitate to comment down below if you have questions. Have fun playing! A chance to replay the campaigns with more modern graphics instead of FF7 cubes. That's literally all I wanted, haha. The custom maps have been fun too, I like tower defense games, almost no bugs there. I think I've had 3-4 crashes in total out of the entire campaign + custom maps. I play on a Mac, if that means anything.

(https://wow.gamepedia.com/World_of_Warcraft_File_Locations?action=edit Please update) with ((Wowpedia:Citation|official sources)).'>

Jan 30, 2020 Warcraft 3 Reforged will automatically download custom maps regardless of which you choose. If you’re hosting your own map, you’ll be presented with a list of maps and folders containing. R/warcraft3: The subreddit for all things Warcraft III! Discuss ladder strategies, custom games, modding/map making, the campaign, artwork Press J to jump to the feed.

You can ((Project:Find or fix a stub|help expand it)) by ''(https://wow.gamepedia.com/World_of_Warcraft_File_Locations?action=edit editing it)''.'>

How To Get Campaigns Folder For Mac Warcraft Iii Pc

3World of Warcraft Installation Locations

4Important Folders

Description

This article is to try and help people find their World of Warcraftfolder on MS Windows as the standard installation folder will vary depending on the version of MS Windows installed.

Possible Uses

It is needed as there are occasions where a user may be required to find it to:

Delete/rename your 'Interface' and 'WTF' folders to resolve AddOn issues (often the first thing a GM will ask you to do if you are having in game issues).

Manually copy/install AddOns without the use of an Automatic AddOn Updater.

Find lua files from within the WTF folder to send data to AddOn authors so they can improve there AddOns or find fix bugs.

Backup config files (normally stored in your WTF folder with the extension '.lua') or move/copy config files from PC to another.

Find and view screenshots you have taken in game using the 'Print Screen' button.

World of Warcraft Installation Locations

MS Windows XP and earlier

All versions of MS Windows up to and including XP installed by default in a subfolder (called 'World of Warcraft') of the 'Program Files' folder.For 32bit versions of MS Windows this can be found by going to Start - Run and typing '%ProgramFiles%' (this is an Environment variable).For 64bit versions of MS Windows this can be found by going to Start - Run and typing '%ProgramFiles(x86)%'.If you are unsure if you have a 64bit or 32bit version of windows try the '%ProgramFiles(x86)%' folder and if it errors you must have a 32bit version of windows.

MS Window Vista and later

The location of World of Warcraft's installation folder after MS Windows Vista and later (including Windows 7) can vary. You can check check the same locations for Windows XP (mentioned above) but it should be location in the folder 'Public' (use the Environment variable '%PUBLIC%' to find this) then the subfolder 'Games' then again the subfolder 'World of Warcraft'

Alternative Methods

Another way to locate your World of Warcraft installation folder is to right click on the World of Warcraft icon you use to start World of Warcraft and goto 'Properties' then in the new window that opens click 'Open File Location' or 'Find Target..'. This will open an new Explorer window with the World of Warcraft installation folder open.

Important Folders

How To Get Campaigns Folder For Mac Warcraft Iii 4

Inside the World of Warcraft installation folder are several Important Folders. These are listed below:

Interface

The 'Interface' folder contains another folder called 'Addons' and this in turn contains all of your AddOns currently installed for this installation of WoW. If you delete or rename this folder and start WoW you will have no AddOns installed.

WTF

The 'WTF' folder contains ALL the config files for this installation of World of Warcraft including all AddOn config files for all Characters played on this installation of World of Warcraft.

Cache

World of Warcraft uses this folder to store temporary data it downloads or generates during normal game play. It is safe to completely delete this folder as it will be recreated the next time the game is started.

Fixing Game Issues

If you are having issues with World of Warcraft that you suspect is caused by an AddOn and disabling all Addons didn't solve it then rename both the 'WTF' and 'Interface' folders to something like 'WTF_Backup' and 'Interface_Backup' respectfully and delete the 'Cache' folder completely then reload WoW. If the problem goes away it was caused by an AddOn or config.You can then Rename the 'Interface_Backup' folder back to 'Interface' (you may need to delete the one WoW creates) and try again. If your problem doesn't come back it was likely a config issue and you can try re-setting up all of your AddOns again from scratch.

Retrieved from 'https://wow.gamepedia.com/World_of_Warcraft_File_Locations?oldid=2908683'

Brithny updated on Nov 17, 2020 to PC Transfer|How-to Articles

Why Do You Want to Transfer Blizzard Games to a Different Drive

A real case from Blizzard's official website - 'I am out of space on my C: drive, how do I move my Blizzard games to a new hard drive so I have enough room for them?'

Two Reasons for Moving Blizzard Games to Another Drive:

Reason 1: the drive that has Blizzard games installed is running out of free space.

By default, Blizzard will drop the game files to your C drive. So C drive is easy to run out of space due to various installation files and desktop files.

Reason 2: you want to move the games to SSD to enjoy faster speed and better performance.

How To Get Campaigns Folder For Mac Warcraft Iii Reforged

SSD can reduce the load times because the data transfer speed of SSD is faster than that of an HDD.

Workable SolutionsStep-by-step TroubleshootingSolution 1. Move Blizzard Games to Another Drive Without Re-installation (Handy Software)Connect the drive to your PC > Choose Games you want to...Full stepsSolution 2. Install Blizzard Games on a Different Drive (Manual Way)In the Blizzard Battle.net app, choose Uninstall Games >Open the Blizzard...Full stepsSolution 3. Changing Installation Setting/Game Folder Location (Manual Way)Copy and paste games folder to another drive > Re-start Battle...Full steps

Moving your games to another drive is easy by using these 3 solutions we talked.

Among the three methods, the EaseUS Todo PCTrans is the most convenient one. If you are looking for useful solutions to move Blizzard games to another drive, you can follow the steps below to start moving your games like Call of Duty, World of Warcraft, Hearthstone, and Heroes of the Storm.

Solution 1: Move Blizzard Games to Another Drive Without Re-installation (Automatically)

If you don’t want to re-install your games for saving your time, the first solution is suitable for you.

To migrate Blizzard games to a different drive, you can use EaseUS Todo PCTrans. It is an easy PC transfer tool that helps you transfer your data, applications, and account settings from one PC to another in one click. The APP Migration feature of this software allows you to change the game installation location and transfer installed programs to SSD easily. Download this application migration program and migrate Blizzard games to another HDD or SSD.

Step 1. Connect the destination drive to your computer

Where do you want to place the games from their original location? Use the correct way to connect the drive to your computer, where EaseUS Todo PCTrans is installed. Run the games transfer software, choose 'App Migration' and then click 'Start'.

Step 2. Choose the games you want to change the location

How To Get Campaigns Folder For Mac Warcraft Iii 3

On the left bar, choose the original drive where your game programs are installed, locate the games and tick the checkboxes. Click the triangle icon to choose the target drive as the file path. After all the selection, click 'Transfer'.

Step 3. Transfer the games to the destination drive

The software will work on moving the selected games from the original location to your customized location. Wait for the process to finish.

Solution 2: Install Blizzard Games on a Different Drive (Manually)

If you don't mind the time-consuming way, then this method is right for you. It is recommended to install Blizzard games to a drive with enough space for the very first time. If you have to move them, follow the details below:

Step 1. Uninstall Games with the Blizzard Battle.net Desktop App.

How To Get Campaigns Folder For Mac Warcraft Iii Classic

Open the Blizzard Battle.net desktop app, select the icon for the game you want to uninstall, select 'options', then select 'Uninstall Game'.

Step 2. Open the Blizzard Battle.net Desktop Application and go to the correct tab for the game you'd like to install. Hit the “Install” button.

Step 3. Under 'Install Location,' browse to the correct drive and folder you'd like to use for the installation. Click 'Start Install'. Then, repeat the steps for each game.

This method only changes the location of the game, not the Blizzard Battle.net Desktop App. Moving the Blizzard Battle.net Desktop Application to a different drive from your Operating System files can cause problems with your games.

Solution 3: Move Battle.net Games to Another Drive via Changing Installation Setting/Game Folder Location (Manually)

Besides the two methods of moving blizzard games to SSD, there is another way to help you move Call of Duty (include Warzone or Modern Warfare) to another drive. You can change the installation game folder.

How to Move Call of Duty to Another Drive

Step 1. Copy and paste the original game folder to another drive, and run the Battle.net client.

Step 2. Click Locate the game on the bottom of the interface. Choose the new folder.

Step 3. Head to interface again. The Install button will change as the Update button. Click it to finish the folder location changing.

Conclusion

With very simple steps, you can move Blizzard games to another drive easily. If you want to move Blizzard games to SSD or HDD without re-installation, solution 1 is suitable for you. If it doesn’t matter installing the games again, solution 2 and solution 3 can satisfy your needs. If you have a question about how to move your games to another drive or if you have a better solution, you can contact us via LiveChat. We will reply as soon as possible.

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blogauthority512 · 4 years ago

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Indesign To Affinity Publisher

Microsoft PublisherDeveloper(s)MicrosoftInitial release1991; 30 years agoStable release(s)

Office 3652103 (16.0.13901.20312) / March 30, 2021; 15 days ago(1)One-time purchase2019 (16.0.13328.20356) / September 24, 2018; 2 years ago(2)

Operating systemMicrosoft WindowsTypeDesktop publishing softwareLicenseTrialwareWebsiteproducts.office.com/publisher

One of the most requested features for Affinity Publisher is now available in 1.8—the ability to import InDesign Markup Language (IDML) files. This means you can now migrate your InDesign documents directly into Affinity Publisher instead of via PDF import or copy/paste operations.

How to Open an InDesign File in Affinity Publisher:. Drag & drop your files onto the IDMarkz window, in the “Drag and drop files here” section. With Affinity Publisher loaded, click on the “Publisher” icon in the top of the IDMarkz window.

Canvais a tool that helps you to create designs with your team. It provides drag and drop.

Microsoft Publisher is a desktop publishing application from Microsoft, differing from Microsoft Word in that the emphasis is placed on page layout and design rather than text composition and proofing.

Overview(edit)

Publisher is included in higher-end editions of Microsoft Office, reflecting Microsoft's emphasis on the application as an easy-to-use and less expensive alternative to the 'heavyweights' with a focus on the small-business market, where firms do not have dedicated design professionals available to make marketing materials and other documents.(3)(4) However, it has a relatively small share of the desktop publishing market, which is dominated by Adobe InDesign and formerly by QuarkXPress.(3)

While most Microsoft Office apps adopted ribbons for their user interface starting with Microsoft Office 2007, Publisher retained its toolbars and did not adopt ribbons until Microsoft Office 2010.(5)

Compatibility(edit)

LibreOffice has supported Publisher's proprietary file format (.pub) since February 2013.(6)Corel Draw X4 features read-only support.

Add page break in word. Publisher supports numerous other file formats, including the Enhanced Metafile (EMF) format, which is supported on Windows platforms. The Microsoft Publisher trial version can be used to view .pub files beyond the trial period.(7)(8)

Adobe PageMaker also saved files with a .pub extension, but the two file formats were unrelated and incompatible. Traditional post box.

Adobe Indesign Vs Affinity Publisher

Release history(edit)

NameVersion numberRelease date(9)Editions of Microsoft Office included inMicrosoft Publisher1.0Late 1991 (approx.)N/AMicrosoft Publisher2.0Jul 12, 1993N/APublisher for Windows 95 (beginning to transition to 32-bit)3.0Aug 24, 1995N/AMicrosoft Publisher 974.0Oct 21, 1996Small Business EditionMicrosoft Publisher 98 (first fully 32-bit)5.0Mar 23, 1998Small Business Edition 2.0Microsoft Publisher 20006.0Jun 7, 1999Small Business Edition, Professional, Premium, DeveloperMicrosoft Publisher 200210.0(a)May 31, 2001Professional OEM, Professional Special EditionMicrosoft Office Publisher 200311.0Oct 21, 2003Small Business, Professional, Professional Plus, EnterpriseMicrosoft Office Publisher 200712.0Jan 30, 2007Small Business, Professional, Ultimate, Professional Plus, EnterpriseMicrosoft Publisher 201014.0(b)Jun 15, 2010Standard, Professional, Professional PlusMicrosoft Publisher 201315.0Jan 29, 2013Professional, Professional Plus, Standard (volume licensing), all Office 365 editionsMicrosoft Publisher 201616.0Sep 22, 2015Microsoft Publisher 2019Sep 24, 2018

Indesign Vs Affinity Publisher Reddit

^Starting with Publisher 2002, the version number jumps to 10.0 to tally Microsoft Office versions.

^Version 13 was skipped due to the superstition attached to the number 13.(10)

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blogauthority512 · 4 years ago

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Add Page Break In Word

How To Add Page Break In Word 2016 Youtube

Here is an example of writing some text to the word doc then adding a page break. Then writing the rest to the text to the document. In order to get a better answer you may need to rewrite your question as it is unclear when you want to insert a break and how you are writing the information to the document.

Put your cursor where you want one page to end and the next to begin. Go to Insert Page Break.

First, make sure you position the cursor in the document, in the place where you want to start a new section. Click or tap the Layout tab on the ribbon. In the Page Setup section, locate the Breaks button and click or tap on it. A drop-down menu opens up, and the section breaks options are at the bottom. To insert a page or section break, place the cursor where you want one section/page to end and the next section/page to begin. Go to Insert Break, then choose the specific type of page break or section break you want to insert. The image above shows how to insert section break in.

Page breaks are incredibly useful when it comes to formatting your Microsoft Word documents so that they look exactly the way you want them to. The page breaks tool allows you to determine where a page begins and ends. Here is some information about how to insert and remove page breaks in Microsoft Word 2016 as well as the types of page breaks that exist.

How to Add a Page Break

When a page can no longer fit additional content, Word automatically adds a page break. However, in some cases, you may want to add page breaks elsewhere. For example, if you’re working on a lengthy document, you may want to create to add a page break to create a new section. Page breaks are also useful when it comes to adding space around images and other graphics.

Fortunately, the process of creating a page break is incredibly simple.

1. Move your cursor where you want to begin a new page. For example, if you want to add a page break between two paragraphs, you should place the cursor before the first character of the second paragraph.

2. Navigate to Insert > Pages > Page Break. Microsoft Word will then create a new page where you placed your cursor.

You can also the keyboard shortcut CTRL + Enter to add a page break quickly.

Other Kinds of Page Breaks

Add Page Break In Wordpad

In Microsoft Word, there are many different types of breaks that you can add.

The LAME MP3 encoding library allows Audacity to export audio in the popular MP3 format. LAME is now bundled in with the latest Audacity version. The FFmpeg import/export library allows Audacity to import and export many additional audio formats such as AC3, AMR(NB), M4A and WMA, and to import audio from video files. Audacity 2.0.6 and later requires FFmpeg 1.2 to 2.3.x (or libav 0. Audacity Tour Guide - quick tour of selected features of Audacity; Installing and updating Audacity on Windows, Mac or Linux. Updating from Audacity 2.x to Audacity 3.x (changed project structure) Installing plug-ins for Audacity on Windows, Mac or Linux; Changing language; Connecting audio equipment - Microphones, instruments, USB devices. Soundgoodizer Soundgoodizer is a stereo 'maximizer-enhancer' plugin based on the Maximus soundprocess engine. Soundgoodizer has been fully endorsed by famous people with really big egos ears - It's shiny, it's good! Soundgoodizer audacity. Download the free Audacity audio editor for Windows, Mac or Linux from our download partner, FossHub: Download Audacity for Windows, Mac or Linux; Audacity is free of charge. No registration or hardware purchase required. Audacity on Windows is only available as a 32-bit application that may be used on 32-bit or 64-bit systems.

Column Break: If you have multiple columns in your document, you can use a column break to start adding content to a new column. Using a column break is far better than simply pressing the Enter key to move to the next column. Even if you make changes to the text or change the font size, you won’t have to worry about formatting as long as you use a column break instead of pressing the Enter key.

Text Wrapping: Another type of page break is text wrapping. If you have an image or in your document, you can use a text wrapping break to ensure the rest of the content flows around the image and the caption.

Next Page: The Next Page feature works similarly to the Page Break feature. The main difference is that it creates a new section and gives you the option to use formatting that is different from the prior sections. For example, you can use the Next Page feature to rotate a section to portrait or landscape mode. You can also use different footers or headers or add columns to the next section without impacting the formatting of the rest of the document.

Continuous Break: The Continuous Break feature creates a new section but doesn’t have you begin on a new page.

Even and Odd Page Breaks: This feature allows you to add a section break. You will begin on the next odd or even page, depending on the type of break you pick. You can also format the even and odd pages of a document differently.

Insert Page Break In Word Shortcut

How to Remove Page Breaks

It is just as easy to remove page breaks from a document in Microsoft Word 2016. https://blogauthority512.tumblr.com/post/657187144837857280/firefox-send-github.

1. Click on the symbol for Show/Hide.

2. Double-click on the page break you want to get rid of.

3. Click on “Delete”.

Keep in mind that you can’t remove page breaks automatically added by Microsoft Word, However, you can change where these automatic page breaks land so that you can keep certain lines or paragraphs together.

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blogauthority512 · 4 years ago

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Firefox Send Github

What do you do when you need to send a file to someone you don’t interact with a lot?

Many of us use email attachments for small files, because it’s quick and easy to share modest amounts of data that way.

Found a problem with this page? Source on GitHub; Report a problem with this content on GitHub; Want to fix the problem yourself? See our Contribution guide. Firefox Send was a free and open-source end-to-end encrypted file sharing web service developed by Mozilla. It was launched on March 12, 2019 3 and was taken offline on July 7, 2020 after the discovery that it was used to spread malware and spear phishing attacks. I use Send a lot, would love to have this as well. As @mehdi said, the use cases are a bit different. I haven't had any practical use for FilePizza but it's great for some p2p data transfer testing Whereas with send, there is a firefox extension using which you can upload screenshots, clips automatically.

Sure, the attachment will probably lie around in the recipient’s mailbox for days, or months, or even years, which might not be quite what you had in mind…

…but when you send someone else a file, you can’t control what they do with it anyway, or how long they keep it, or how widely visible it is on their corporate network after they save it.

But email is no good for large files such as audio data or videos, because most email servers quite reasonably have a low limit on message sizes to stop the system getting clogged up by attachments.

So the usual fallback for sending files that you can’t or don’t want to transmit via email is to use a file sharing service instead, which is rather like using webmail, only without the messaging part.

You upload the file to a file sharing site, optionally setting various options that describe which other users can see it, and for how long, and then send the recipient an email that contains a download link where they can fetch the file at their leisure.

That worrying feeling

If you routinely use file sharing services, however, we’re sure you’ve experienced that worrying feeling that comes whenever you browse through the list of files you’ve shared in the past, especially if you were sending a file to someone you don’t deal with often.

What on earth was that file test-footage-march-unedited.mov you once sent, and who on earth are the four users on the access control list?

Can you delete the file temporary-backup-of-event-pics.zip that you shared two years ago, and did you ever make a permanent backup, and if so, where did the file backup-to-keep-forever.zip end up anyway?

The annoyance – and the small but ever-present cybersecurity risk – in leaving a litter of old files behind in various third-party websites is one that affects many of us…

…which is why we are occasional but enthusiastic users of Firefox Send, a free service from Mozilla that aims to let you share large files easily, but without the worry of what gets left behind and forgotten about.

When you upload a file to send DOT firefox DOT com, it gets encrypted in your browser before any data is send into the cloud; the decryption key is encoded into the URL for downloading the file; and the link thus generated is (by default, at least) valid for one download or 24 hours, whichever comes first.

If the recipient downloads the file using the link you send them, the data gets decrypted in their browser only after it has been downloaded, and then it vanishes from Mozilla’s servers forever.

If both you and the recipient forget about the uploaded file altogether, then it vanishes anyway and you don’t have to wonder if it’s still sitting around somewhere for someone else to download.

While the file is still on Mozilla’s servers, the pre-upload encryption means that even Mozilla can’t decrypt the file anyway, because only the encrypted data was uploaded and not the key.

(In fact, given that the temporarily stored files are no more use than shredded cabbage to Mozilla – or to any network hackers, law enforcment agents, data protection regulators and so on – there is every incentive for Mozilla delete the files as promised, in order to recover disk space that would be completely wasted otherwise.)

What’s good for the goose

Unfortunately, as with so many simple, free and effective online services, what’s good for the goose is good for the gander, too.

In other words, crooks love Firefox Send just as much as we do, because it lets them generate short-term links based on trusted URLs for sharing arbitrary files without leaving any leftover data in the cloud.

The problem is that in the case of the crooks, they’re typically using Firefox Send for what you might call “data infiltration” – a way of importing malware files or attack tools onto a network they’ve already broken into without drawing undue attention to themselves.

That sort of operational tactic goes by the name of living off the land – a slightly misplaced metaphor, to be sure, but one that is now widely used in the cybersecurity industry to mean “fitting right in with everyday behaviour on the network”.

By using Firefox Send, the crooks don’t need to set up a file sharing server of their own at a legitimate-looking URL, and they don’t have to worry about making sure their URLs expire automatically after use.

Links that work only once are a thorn in the side of security researchers, because even if you manage to acquire a full URL as an indicator of compromise, you can’t go back to the URL to investigate what malevolent baggage it might have served up when it was used.

The crooks also make themselves harder to track because their malicious content is effectively hiding in plain sight at an IP number operated by Mozilla.

What now?

Hats off to Mozilla and the Firefox team: following recent suggestions from cybersecurity researchers that some tweaks to the service might be a good idea, such as a (Report Abuse) button to make it quick and easy to get dodgy links blocked…

…the company has suspended its service temporarily to address the issues, rather than simply handing out vague promises to look at changes in the future:

Firefox Send is temporarily unavailable while we work on product improvements.

We appreciate your patience while we make the Firefox Send experience better.

The holding page doesn’t actually say that the outage relates to the problem of abuse by cybercriminals, but Mozilla has issued a statement to say:

Before relaunching, we will be adding an abuse reporting mechanism to augment the existing Feedback form, and we will require all users wishing to share content using Firefox Send to sign in with a Firefox Account.

Firefox Send Github Files

Until now, you could use Firefox Send without creating a Firefox account, although that limited your links to at most 24 hours, but it looks as though the days of free-in-all-senses use of Firefox Send are over.

We’re not sure quite how much of a dent Mozilla will make in the abuse of the service by requiring even occasional users to stump up email addresses and create yet another cloud account, but the organisation seems determined to keep the service alive while addressing the community’s geniune concerns.

What do you think?

Does your organisation block sites like Firefox Send anyway?

If so, do you think these changes will make you rethink your policy, given that Send’s auto-encrypt-before-upload and auto-purge-after-download features gives you two less things to worry when sending files via the cloud?

To improve the Firefox URL bar, we used Federated Learning, a new privacy-preserving machine learning technique.Roughly 360,000 users helped to train and evaluate a model in a distributed way, and our results show that this optimization process worked well.This posts explains the decisions we made and shows the study results.Since Federated Learning is still a young technique, this is one of the very first implementations in a major software project.

Most of machine learning nowadays is based on collecting a lot of data which is then put on a powerful server where the training is performed.If the data is considered private by people, using machine learning this way is not possible or a bad idea.The entirely opposite approach, a completely decentralized system where individual models are trained for each user locally, generally works badly.Individuals by themselves often do not have enough data to fit good models.

Federated Learning is a new subarea of machine learning where the training process is distributed.The parts of the algorithm that touch the users’ data are executed locally on their machines.Instead of sharing data, clients then only send back abstract model improvements, for example weight updates, to the server.This way, a high-quality model can be trained collaboratively without needing to collect data.The Federated Learning blog post on this site provides a much broader overview of this area.

Over the past three months, we implemented a Federated Learning system for Firefox.The aim of this project was to improve part of the suggestions displayed in the Firefox URL bar, though in theory the system is flexible enough to be used in many other situations.Collecting local search queries and browsing histories is extremely bad for privacy, so using Federated Learning to still train a model on this data makes a lot of sense.Initially, we experimented with language models but then reduced the scope of the project to make it possible to implement everything in three months.

Search in the Firefox URL bar

The Firefox URL bar shows suggestions when users type a search query.A part of these suggestions is provided directly by a search engine.The others are generated by Firefox itself, for example based on the user’s history, bookmarks or open tabs.We tried to optimize the history and bookmark suggestions using our project.

The Firefox URL bar with some suggestions based on the browsing history

Searching for history and bookmark entries in the Firefox URL bar is a two-step process:

The search query is matched against the browser history and bookmarks. Matching is a binary decision. Pages either match the query or do not

The set of matched links is ranked based on the user’s history

Our project purely tries to optimize the ranking part of this process.Future work could tackle the problem directly from the query matching.

Learning to Rank

Before diving into the current implementation, it is worth taking a step back to understand how ranking in machine learning works.This makes it easier to see how the current algorithm fits into a machine learning system.Fundamentally, there are three different approaches to learning a ranking algorithm:

Pointwise ranking: Each item is given separately to the model, which assigns a score to the item. The ranking is then determined by sorting all items using their respective scores. Essentially, this is a special type of a regression model since we are assigning a real number to every input

Pairwise ranking: The model learns to compare pairs of items. Its task is to decide which of the two items should be ranked higher. Intuitively, this method can be motivated by the fact that the learned comparison function could then be used by various sorting algorithms. In this approach, we treat the problem as a classification task since the model can only have two possible outputs

Listwise ranking: Instead of only working with individual items in each step, these methods try to operate on the entire list. The motivation behind this idea is that the evaluation metric can be optimized directly. In practice, this turns out to be fairly difficult because many evaluation metrics are not differentiable and the models need to work with many more inputs. Another difficulty is that the list could have an arbitrary length

All these approaches have different advantages and disadvantages.The existing ranking algorithm in Firefox is very similar to a pointwise ranking approach.Since this algorithm should be optimized using machine learning techniques, this gives us a clear set of techniques that could be useful in this project.

Frecency

The ranking of possible suggestions in the Firefox URL bar is determined using frecency, an algorithm that weights how frequently and recently a site was visited.To do this, a frecency score is assigned to each history entry and bookmark entry.After computing the score, it is cached.When searching, the matched results are then sorted using this score.This section introduces the existing frecency algorithm, while the next one explains how we planned to improve it.

Frecency does not only take frequency and recency into account but also other information, such as how the page was visited and whether it is bookmarked.It does this by looking at the latest visits to the respective site.The value (operatorname(visit)(v)) of one single visit (v) is then defined by how recent that visit was, scaled by the type of visit:

(operatorname(visit)(v) = operatorname(recency)(v) * operatorname(type)(v))

Firefox Source Github

Frecency scores have to be cached in order to allow an efficient ranking while the user is typing.This means that the recency aspect has to be modeled using time buckets.Otherwise, the score would change all the time and caching would not work.In the current Firefox implementation, there are five time buckets.With this approach, the recency score only changes when a visit changes time buckets:

(operatorname(recency)(v) = begin(cases) 100 & text(if visited in the past 4 days) 70 & text(if visited in the past 14 days) 50 & text(if visited in the past 31 days) 30 & text(if visited in the past 90 days) 10 & text(otherwise)end(cases))

Sites can be visited in many different ways.If the user typed the entire link themselves or if it was a bookmarked link, we want to weight that differently to visiting a page by clicking a link.Other visit types, like some types of redirects, should not be worth any score at all.We implement this by scaling the recency score with a type weight:

(operatorname(type)(v) = begin(cases) 1.2 & text(if normal visit) 2 & text(if link was typed out) 1.4 & text(if link is bookmarked) 0 & text(otherwise)end(cases))

Now that we can assign a score to every visit, we could determine the full points of a page by summing up the scores of each visit to that page.This approach has several disadvantages.For one, it would scale badly because some pages are visited a lot.Additionally, user preferences change over time and we might want to decrease the points in some situations.

Instead, we compute the average score of the last 10 visits.This score is then scaled by the total number of visits.The full frecency score can now be computed efficiently and changes in user behavior are reflected fairly quickly.Let (S_x) be the set of all visits to page (x), and let (T_x) be the set of the last up to 10 of these.The full frecency score is then given by:

(operatorname(frecency)(x) = frac(|S_x|)(|T_x|) * sumlimits_(v in T_x) operatorname(visit)(v))

Note that this is a simplified version of the algorithm.There is some additional logic for special cases, like typing out bookmarks or different kinds of redirects.The description here only shows the essence of the algorithm in a mathematical form.

Optimizing Frecency

While frecency has been working pretty well in Firefox, the weights in the algorithm were not decided on in a>A visualization of the SVM loss when adapted for ranking

The image above shows a visualization of this loss function.Each bar represents a possible suggestion, with the selected one being shown in black.The y-axis displays how many points the model assigned to the respective suggestion.The hatched areas show the SVM loss.Everything above the selected suggestion and below it by a margin of (Delta) adds to the full loss.Even though the selected suggestion had the second highest score, four suggestions contribute to the penalty.

Computing Gradients

Every time a user performs a history or bookmark search in the URL bar, we compute the SVM loss on that search.To compute an update, we then try to move the weights a little bit into a direction where this loss is minimized.The update corresponds to the gradient of the SVM loss with respect to the weights in the frecency algorithm that we optimize for.

Gradients can be computed elegantly using computational graphs.By using machine learning libraries, we first construct the function we want to compute.Afterwards, we can make use of automatic differentiation techniques to obtain the gradient.Our initial prototyping was based on this idea.The major advantage is that it is very easy to change the model architecture.

The current frecency implementation in Firefox, however, is written in C++, while the client-side part of this experiment works using JavaScript.To launch the experiment, the Firefox SHIELD mechanism was used.SHIELD allows us to directly send new code to Firefox clients, without having to go through major version releases, which only happen every few weeks.To be able to do this, SHIELD experiments have to be written in JavaScript and can only make very limited use of C++ components.

This made it hard to add a computational graph to the existing C++ frecency module.Reimplementing the full algorithm in JavaScript seemed like a bad idea.Performance-wise there would be a huge penalty and it is hard to reconstruct the way the current implementation handles all the edge cases.

Instead, we decided to use a simple finite-difference technique.If we want to compute the gradient of a function (g) at the point (x), we check the difference of values close to that point:

(g'(x) approx frac(g(x + epsilon) - g(x - epsilon))(2 * epsilon))

This formula is very close to the definition of derivatives.To compute the gradient of a multivariate function, this process is then performed by iterating through all dimensions independently.In each dimension, the value is changed by (epsilon) in the two directions, while all other values stay constant.The resulting vector is our gradient estimate.

This method is both easy to understand and implement.It is simple to change the frecency weights in our experiment without changing the actual algorithm.For large models there is a performance penalty since we need to evaluate (g) two times for every dimension.In (n) dimensions, there are (mathcal(O)(n)) function evaluations as opposed to (mathcal(O)(1)) for computational graphs.But since we only work in 22 dimensions here, this is not a major problem.

The finite-difference method also allows us to essentially treat frecency as a black box.Our model does not need to know about all edge cases.It is sufficient for the model to see how different decisions affect the output and the loss.

Data Pipeline

The addon we built observes how users are interacting with the URL bar and retrieves all necessary information to compute the gradient.That update and some statistics about how well the model is doing are then sent to a Mozilla server.This works by using the Telemetry system, which has several advantages.It is a well-designed system with clear rules about what can be collected.There is a lot of infrastructure around using it and dealing with the data on the server.

All messages sent by clients are stored in a Parquet data store. A Spark MapReduce job reads the new updates and averages them in real-time.Every 30 minutes, the average update is then given to an optimizer and applied to the model.The resulting model is published to S3 and fetched by clients.

Updating the Model

One central problem with applying the update to the model is choosing the hyperparameters of the optimizer.Since we did not collect any data, it is hard to tune the optimizer beforehand.Even values like the learning rate are hard to set since we have no information about the gradient magnitude.Trying out many different learning rates in production would take time and could lead to a bad user experience.Directly collecting some data conflicts with the goal of doing machine learning in a privacy-respecting way.

We tackled this problem in two ways.First of all, we created simulations which use a made-up dataset that should be similar to the one we expect to see in production.This allowed experimenting with different optimizers and helped with making early design decisions.It also made it possible to quickly iterate on ideas to see if they could work.

The second way of dealing with the fact that it is hard to set hyperparameters was using the RProp optimizer.This optimizer has major advantages in our case:

It completely ignores the gradient magnitude and only takes into account the signs of the gradient. This means it will work with any sort of gradient we could see in production. We do not have to worry about properly scaling it

It automatically adapts internal learning rates based on how well they work. So even if the initial values are off, they will move to decent ones in a few iterations

The updates produced by RProp are very interpretable. In our case, we make sure they are 3 at most, so that frecency scores only change slowly

After RProp produces an update, we still apply several constraints to it.

Weights have to be nonnegative. This means visiting a site cannot directly have a negative effect

The time buckets have to be sorted by the last day they take into account. In other words, the ((i + 1))-th time bucket needs to contain older visits than the (i)-th time bucket. This is to ensure that the client-side frecency implementation continues to work

These essentially act as safeguards to make sure that user experience does not degrade too much if the optimization process fails.

Study

Users in the experiment were split into three groups:

treatment: The full study was shipped to these users. They compute updates, send them to the server, and start using a new model every 30 minutes

control: This group is solely for observational purposes. No behavior in the URL bar actually changes. We are just collecting statistics for comparison to treatment

control-no-decay: Firefox decays frecency scores over time. Our treatment group loses this effect because we are recomputing scores every 30 minutes. To check if the decay is actually useful, this group has no decay effect but uses the same original algorithm otherwise

The study was shipped to 25% of Firefox Beta users who had Telemetry enabled.As with all other SHIELD studies, only users with Telemetry enabled were eligible for enrollment.60% of the users in our study were assigned to the treatment group, while the other 40% were split among the two control groups.

Metrics

To evaluate how well our trained model works, we had three success criteria:

Firefox Github Mirror

Do not significantly decrease the quality of the existing Firefox URL bar

Successfully train a model using Federated Learning

Stretch goal: Improve the Firefox URL bar

Actually improving the quality of the ranking for users was only a stretch goal.The primary goal of the study was to see if it is possible to make the distributed optimization process work.Essentially this meant consistently decreasing the loss of the model.At the same time, the quality of the URL bar should not decrease.The reason for distinguishing between these is that our optimization goal could have been misaligned.It is possible to minimize some loss function without actually improving the experience for the user.

To measure the quality of history and bookmark suggestions in the URL bar, we used two metrics:

Number of characters typed before selecting a result: Users should have to type few characters to find what they are looking for

The rank of the suggestion that was selected: The item that is selected should be as far on top as possible

If the quality of any of these two metrics increases, we consider the stretch goal to be reached.We were not entirely sure if both metrics could be improved.One theory for this was that maybe users always type a similar number of characters before choosing one of the suggestions.The alternative could also be possible, users always type until the first suggestion displayed is the one they were looking for.For this reason, we decided that for the third goal only one of the metrics would need to be improved, while the other should not get much worse.The first goal meant that both metrics should not get significantly worse.

Power Analysis

Power Analysis is an important part of designing studies.It tries to answer the question of how many people are required to get statistically significant results in an experiment.If too few people are enrolled, the results will contain too much random noise to rely on them.If a lot of people are enrolled, we can be confident in the results but the cost of the study will be much higher.

In the case of Firefox, this cost consists of two factors.For one, if our study enrolls most Firefox users, we would block other studies that want to experiment with changes in the URL bar.Another reason is that the experiment might break parts of Firefox.If this happens, it should not affect unnecessarily many people.

For this reason, we performed a power analysis to decide on our sample sizes for treatment and control.Concretely, this analysis consisted of two parts:

How many users do we need to have enough data to train a model? (relevant for treatment)

How many users do we need to show certain effects confidently? (relevant for treatment and control)

The first part was answered using simulations.By using an adapted form of the simulation we used to decide on optimization hyperparameters, we could get some idea on how many users we would need.Existing Telemetry data was helpful for this, as it allowed us to get some idea of how many history searches people perform every day.

The second part of the power analysis was tackled using classical hypothesis testing.There was no prior data on the number of typed characters, so no power analysis was possible for this metric.To analyze the rank of the selected item, the Mann-Whitney-U test was used since the data was not following a distribution that allows for parametric tests.The Mann-Whitney-U test is non-parametric, which means that it does not make any assumptions about the underlying distribution of the data.This analysis turned out to be pretty helpful since we realized that our control groups could be smaller than the treatment group.

Analyzing the Results

Over the course of the experiment, 723,581 users were enrolled in the study.The model was fetched 58,399,063 times from the server.360,518 users participated in sending updates and evaluation data to the server, accounting for a total of 5,748,814 messages.The optimization phase of the experiment consisted of 137 iterations of 30 minutes each, or just under three days.In this phase, 186,315 users sent pings to help in the training process.

A separate phase of purely evaluating the model was started afterwards and took a total of 10 days.In this phase, 306,200 users send 3,674,063 pings, which included statistics detailing how well the model worked for them.Since all these users were assigned to treatment or control groups, the new model can be compared well to the old one that was used by the control groups.Some users were enrolled but did not help with optimization or evaluation because they performed no history and bookmark searches.

During the optimization process, the loss of the model was supervised to check how well the training was going.The plot below shows how the loss changed over time, across all three study variations.There is some noise in this plot, since each iteration only had a very limited number of users.However, it can still be seen that the loss of the treatment group goes down continuously.This shows that the optimization process generally worked.After 40 iterations, less than one day of optimization, the loss of the treatment group is significantly below the loss of the control groups.The second goal of the study was thus reached.

Rolling average of reported loss over the last 5 iterations

After the optimization process ended, an evaluation phase began to determine how well the new model works.This is equivalent to the testing phase in machine learning.The model is evaluated on new data that was not used for training or validation.The table below shows the results.On average, users in the treatment group type about half a character less to find what they are looking for.This is a strong improvement over both control groups.However, users in the treatment group also choose suggestions that were ranked slightly worse.Hypothesis testing determined that the changes in the treatment group were highly significant, with p-values being below 1e-75.

study variationmean characters typedmean selected ranktreatment3.67470.37435control4.262390.3535control-no-decay4.241250.35771

From a user perspective, it is not clear if these changes improve the user experience.While users now have to type a good amount less, they also selected suggestions that were not on top of the list more often.One potential explanation for this could be that the items they were looking for are displayed earlier in the suggestion list.Since they spent less time typing, they might be willing to take the time to select an item that is not the top ranked one.

It is hard to determine purely based on these two metrics if this change is good, since it is not clear how their importance should be weighted.Instead, surveying users would be required to decide if goal 3 was met.But even if users are not satisfied with the new model, the Federated Learning system is still highly useful.Since the optimization process works well, one would only need to find a loss function that correlates more closely with what users want.We consider goal 1 to be reached since at least one of the metrics improved.

To learn from this experiment for further Federated Learning studies, we additionally analyzed all the update data later on.In retrospect, the Federated Learning protocol we used was too simple.The plot below shows how Firefox beta activity in our study varies over time.The protocol could be improved by dynamically determining the iteration length depending on how many updates were sent to the server so far.This way, there would be no iterations with very few updates.Furthermore, there could be more iterations during periods with many active users, allowing for a faster optimization process.

The number of pings sent by clients over time

Firefox Send Github Password

A more sophisticated protocol could adapt the iteration length depending on how stable the current update estimate is.We noticed that the later iterations of the optimization process require much fewer reports to compute a good estimate.The plot below compares the update we actually used to updates we would get by randomly sampling 2,000 of these update reports.The (L_1)-distance is used to perform this comparison.Because of the randomness, the mean and standard deviation after 50 such simulations per iteration are reported.

Mean and std deviation of difference in update quality when using 2,000 updates

It can be observed that the estimates become much more stable after iteration 100.While the (L_1)-distance of two updates can be large without affecting the RProp optimizer much, this is still an interesting result.We observed similar results for the loss estimates.

Future Work

There is still a lot of low-hanging fruit in this Federated Learning system.For one, the protocol could be made much more sophisticated, as explained above.Furthermore, when trying to interpret the new weights, it seems like it would be a good idea to introduce additional weight constraints to make better use of the time buckets.

To be able to implement the entire system in three months, a lot of simplifications had to be done.This is why our resulting model has few weights and is easy to interpret.If more time could go into this project, a language model would be interesting because it would allow tackling the problem directly from a query matching angle.Instead of making suggestions based on the current query, we could base them on what the model thinks the user is going to type next.

Finally, there are other applications for Federated Learning in Firefox.Based on user interactions with Firefox, our system could really optimize anything that currently relies on hardcoded constants.

Open Source Code

Nearly the entire code of the Federated Learning system is open sourced:

Firefox addon that implements the client-side part of the system

Server-side implementation which computes the next model

Simulations for experimenting with Federated Learning

Only a few analyses based on Firefox interaction data are not publicly available.

Thanks!

This project would not have been possible without the help of many people:

Sunah Suh planned out the project with me and helped to resolve many blockers along the way

Arkadiusz Komarzewski ported the Python optimizer to Scala and implemented the remaining server-side logic

Drew Willcoxon and Rob Helmer helped a lot with Firefox specific parts of the project

Tim Smith provided most of the code for using the Mann-Whitney-U test and along with Ilana Segall answered many questions related to power analyses

Jeff Klukas always had an open ear when I ran into problems and helped out when Sunah was on vacation

Katie Parlante made it possible for me to return to Mozilla to work on this and was very open to my ideas for projects

Thanks a lot for all your help and support!

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blogauthority512 · 4 years ago

Text

Soundgoodizer Audacity

Reaper audio recording software is awesome, and is what I recommend to everyone. I use it every day. And I also seem to learn something new about it every day. It has an incredible noise reduction tool built right in that I just discovered recently.

Free 5-part series

Download the free Audacity audio editor for Windows, Mac or Linux from our download partner, FossHub: Download Audacity for Windows, Mac or Linux; Audacity is free of charge. No registration or hardware purchase required. Audacity on Windows is only available as a 32-bit application that may be used on 32-bit or 64-bit systems. Is a free exciter and enhancer VST plugin developed by Red Shine Sound. Compatible OS(s): Windows.

This is a 5-post series, each one on a separate topic for setting up your home recording studio.

How to Set Up a Home Recording Studio

Soundgoodizer Soundgoodizer is a stereo 'maximizer-enhancer' plugin based on the Maximus soundprocess engine. Soundgoodizer has been fully endorsed by famous people with really big egos ears - It's shiny, it's good!

Here is a link to Soundgoodizer Equivalent for other DAWs, discovered in a thread on here earlier. Plus bonus points for the site being strangely translated and quite funny to read!

The reason I didn't know about it sooner is primarily due to the fact that reducing recorded noise is not its only or primary function. It just happens to do it REALLY well, and non-destructively (if that's not a word already, I hereby create it). The tool is called ReaFIR. I know! Who'd have thought to look for noise reduction editing chops under a moniker like that?

Review of What Noise Reduction Does

First though, let's do a quick review of what noise reduction does for us in the world of recording. There is almost always a bit of background noise in recorded audio. It is especially noticeable in recordings of just one thing, like a single voice.

The noise usually comes from a combination of stuff happening in the space/room where the recording takes place, and the electronics of the microphone and other gear involved. Recordings sound much better if you can reduce the noise, and that is what noise reduction tools are designed to do.

The typical method is to use an effect in an audio editor called 'Noise Reduction.' See my article How To Get Rid Of Background Noise In Audacity for how that normally works.

But since the noise and the voice are both together in the same recording, it isn't a perfect process. What noise reduction tools try to do is filter and removing as much noise as it can without also removing too much of the voice.

In order to do this, the software has to know what noise looks like so it can separate it from the signal (voice). So you have to highlight a section of the recording where there is ONLY noise, and no voice, and feed that sample to the noise reduction tool. Once it has the noise profile, it can do its thing.

My favorite recording program, Reaper, is a digital audio workstation (DAW), which is a fancy way of saying full-featured multi-track recording program. For more information on why I love Reaper so much, see my article, Why Reaper Rocks As A DAW.

But for some reason, Reaper doesn't have an effect called 'Noise Reduction.' So how do you reduce noise in Reaper?

Enter ReaFIR

Reaper names its effect plugins by using 'Rea' as a prefix (for Reaper), and then the name of the function after it. So ReaFIR means Reaper FIR. So what does FIR mean? It stands for Finite Impulse Response. If you would really like to geek out on what it really means, including all the scary math involved, you can read about it at the Finite Impulse Response Wikipedia page. But the simpler definition for ReaFIR itself from the Reaper manual is:

ReaFIR is a EQ and dynamics plug-in that includes an FFT spectrum analysis window. Amongst other things it can be used as a precision EQ, a gate, a fast attack/release precision compressor, a noise reduction tool.

Obviously it is that last thing we're interested in. In the video below, I walk you through how to use ReaFIR to reduce the hiss noise that happened when I recorded a short voice over. BTW, this video was recorded several years ago (why it says from the 'upcoming' course). That course, The Newbies Guide To Audio Recording Awesomeness - Part 2, has now been around since 2012 and had been updated may times since.

Soundgoodizer Plugin Audacity

How Does It Work?

So see it in action in the video above. But here is is a written summary of how it works. Once you have recorded some audio onto a track, click the FX button in the track control panel. Then select VST: ReaFIR from the Cockos collection of FX plug-ins.

Next, go ahead and click the “OK” button and then close the ReaFIR window by clicking on the red X in the upper right corner.

Now just highlight/select a section of the voice-over audio that has no voice actually saying anything. In other words, pick a small area that was only noise.

Then click on the Toggle Repeat button (down by the Play and Stop buttons). This is important because it will prevent any of the actual voice signal being played when sampling the noise for ReaFIR. With that area still highlighted, click on the FX button again to open the ReaFIR window.

In the Mode window of the ReaFIR tool, select Subtract, and put a tick into the Automatically build noise profile box. Now hit the Space bar on your keyboard (which will play the audio, which is just the noise at this point). You'll hear it for a second and then it will be gone. That's ReaFIR getting rid of the noise. Now hit the Space bar again to stop playback.

The next part is very important! Uncheck the box that says Automatically build noise profile. Otherwise ReaFIR will try to grab your voice and anything else on the track, and treat it as noise. Once you've done that, go ahead and close the ReaFIR window. Now all you have to do is click on the Toggle Repeat button again to turn it off, and listen to the entire track.

Voila! The noise is gone and only the voice remains. Pretty darned cool huh? And I think the result is better than a lot of tools dedicated to removing noise. I didn't hear much, if any, of the weirdness that sometimes happens after noise reduction.

The audio takes on a kind of swirly, under-water-y artifact sometimes. But I didn't hear that in this example. However, there noise we got rid of was a low-level and consistent hiss type of noise.

Soundgoodizer Cost

The louder the noise and the more varied the noise (if it contains lots of frequencies and intermittent clicks, pops, etc.), the more likely you are to have that swirly artifact left over after noise reduction.

So now you know about the secret noise reduction tool at your disposal if you use Reaper. Use it wisely.

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