We are proud to announce the release of PyTuneSounds 0.0.5.
PyTuneSounds was extended by an exciting feature, making the app even more useful. Besides estimating hearing-thresholds using the methods already implemented, it is now possible to use the very fast and accurate Maximum-Likelihood-Procedure that estimates the best psychometric function. This method was first described by Green (1993) and was studied and refined by many labs thereafter.
Besides adding this exciting feature, further bugs have been found and corrected for.
For more information, go to http://pytunesounds.sourceforge.net
The latest release of PyTuneSounds brings new features as well as bug-fixes:
1. Length of Fadein/out can now be adjusted by the user.
2. Added functions to process multiple files using the same settings.
3. Added progress dialog when doing frequency correction.
1. Appearance on Mac OSX improved.
2. Loading and saving of AIFF files finally works.
3. Fixed some typos.
4. Tune File to Tone now always has the correct maxima for adding the volume to the tone.... read more
PyTuneSounds 0.0.3 was just released.
This version does not provide new features but closes two bugs that came up shortly after the release of 0.0.2.
PyTuneSounds is a tool for scientists working with auditory stimuli. It can be used to tune the volume of sounds in a standardized way as needed for proper experiments.
PyTuneSounds, a great tool for all scientists dealing with acoustic stimulation, has just released version 0.0.2, enhancing support for Mac OSX using the self-contained version.
PyTuneSounds enables scientists to tune the volume of sounds using a variety of methods commonly used. It also features correction of the frequency-spectrum due to frequency-disturbing equipment.