Version 2.8, 2019-02-07 (maintenance and minor feature release)
* When running in a 32-bit process within 64-bit Windows (WoW64),
use the VAMP_PATH_32 environment variable instead of VAMP_PATH
(hosts running in this context already use "Program Files (x86)"
instead of Program Files)
* Fix off-by-one rounding errors in frame-to-ns conversions. Unlike
the other changes here which are invisible to plugin code, this
change can lead to different results in the lowest significant
figures from existing plugins if relinked against the newer code
* Fix path environment variable character encoding handling on
Windows
* Fix theoretical possibility of integer overflow in RealTime
constructor
* Fix use of undefined behaviour in PluginRateExtractor
Version 2.7.1, 2017-03-06 (maintenance release)
* Fix inclusion mechanism for FFTs which could cause a host
application to crash in certain circumstances due to
conflicting versions of C-linkage symbols
Version 2.7, 2017-02-24 (maintenance and minor feature release)
* Add ability to PluginLoader to list plugins only in (or not in)
certain library files
* Fix fixed-sample-rate output timestamps in printout from the
simple host, and add regression test script using test plugin
* Switch the convenience FFT interface for plugin usage from the
very slow reference implementation previously provided, to the
somewhat faster KissFFT
* Add a top-secret compiler flag to switch internal FFTs to single-
precision only
* Fix some small but long-standing memory leaks and minor bugs
Vamp is an audio processing plugin system for plugins that extract descriptive
information from audio typically referred to as audio analysis plugins or
audio feature extraction plugins.
Just like an audio effects plugin (such as a VST), a Vamp plugin is a binary
module that can be loaded up by a host application and fed audio data. However,
unlike an effects plugin, a Vamp plugin generates not more audio, but some sort
of symbolic information. Typical things that a Vamp plugin might calculate
include the locations of moments such as note onset times, visualisable
representations of the audio such as spectrograms, or curve data such as power
or fundamental frequency.
