Wu Collection: Vehicles Vol. 1

Comprehensive vehicle workups for everyday modern cars.

590 files30GB24-bit/96kHz View Sound List

$ 179.00

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Wu Collection: Vehicles Vol. 1 is a high-quality vehicle sound effects library of everyday and luxury cars recorded from a wide variety of perspectives. Be covered for any project with detailed “workups” for 8 different vehicles – providing thorough mic angles including onboard, engine, exhaust, and static external recordings, as well as different maneuvers, isolated mechanical sounds, and driving speeds.

Captured by award-winning recordist Watson Wu (Baby Driver, Need for Speed), these car sound effects are from frequently on-screen, everyday cars, including the Ford F-150, Subaru Crosstrek, Honda Civic, BMW 750 and more. With many recordings captured while Wu was driving the vehicle himself for maximum precision (don’t try this at home!), the comprehensive collection will prepare you for any driving sequence. Drag and drop directly into your projects with realistic and characterful car sounds that will fit naturally in your mix.

“There are multiple mic perspectives of the same take — You can exclusively use engine sounds or a mix of engine and exhaust at the same time for the mix, having full control of what you need.” — Watson Wu

Each sound file is embedded with rich metadata to help you find the exact sound effect you need with fast, pinpoint search. Advanced metadata fields ensure compatibility across any database search platform such as SoundQ by PSE, Soundminer, BaseHead, Netmix, Workspace (Pro Tools), Find Tool (Media Composer), Media Bay (Nuendo), Reaper, Adobe Premiere, and beyond.

Key Features:

  • 590 files (30GB)
  • View Sound List
  • 24-bit/96kHz WAV files
  • Descriptive embedded metadata
  • Universal Category System (UCS) ?
  • 100% Royalty-Free

Vehicles Included:

  • 2013 Ford F-150
  • 2015 BMW X1
  • 2017 Mercedes GLE 350
  • 2017 Subaru Crosstrek SUV
  • 2005 Honda Civic
  • 2007 Infiniti G35
  • 2018 Mercedes-AMG C63 S
  • 2019 BMW 750 Sedan

Credits:

Watson Wu