Audio Modeling's Solo Woodwinds Bundle includes SWAM Flutes, SWAM Double Reeds, SWAM Clarinets and SWAM Saxophones, all developed by Audio Modeling using a combination of innovative performance techniques and concepts of Physical and Behavioral Modeling with the Multi-Vector/Phase-Synchronous Sample-Morphing technique.
What you get are the most expressive, realistic virtual solo woodwinds on the market. These real-time controllable virtual instruments offer, via MIDI controllers, the same natural reactions as real woodwinds.
No multiple-gigabyte pre-recorded libraries are necessary: the smallest footprint guarantees the perfect organic consistency resulting from the endless expressive parameters that are unique to every live performance. The SWAM Solo Woodwinds Bundle is not a simple recording of notes via sample libraries but rather a set of real virtual instruments based on their traditional counterparts.
You can use the SWAM Solo Woodwinds Bundle for Classical, Country, Pop music, and any other musical genre by layering it in sections, with no artifacts, and simply selecting different timbres for each instance.
All SWAM Engine digitally handcrafted acoustic instruments allow you to control the expression of a virtual acoustic instrument. While a sample library repeats a pre-recorded sound, SWAM instruments play for real. Are you a composer or a producer? SWAM is perfect for your workflow. You can adjust any sound or behavioral parameter to get exactly the response you want. Are you a live performer? SWAM is the only tool available that allows you to play a realistic virtual acoustic instrument in real-time.
AU, VST, VST3, AAX 64bit
10.7 – 10.15 (Catalina)
Windows 7, Windows 8, Windows 10
Required space after installation: 874 MB
RAM occupancy: about 80 MB for each instrument instance
The realism and expressiveness of the SWAM instrument requires a computer with at least a 1.6 GHz Core 2 Duo CPU for running a single plugin instance. Less powerful systems may also prove satisfactory, but may require larger buffer sizes, involving higher latencies.