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AcMus is a project on musical acoustics devoted to the development of models and computer tools for the study of room acoustics and focus on helping the investigation of issues related to the design, control and acoustic treatment of environments. AcMus has not been maintained for a few years now, but the code may still be useful. We consider the current repository to be at https://gitlab.com/ccsl-usp/acmus .
Python Script to process a wave file and transform to movement (key frames) on Blender 2.5. Input: sound file (.wav) Output: Key frames direct into the selected object Work to meshes and bones (location, scale or rotation in any axis)
NOTE: Project is looking for a new maintainer
MPEG Maaate is a platform to do compressed-domain analysis of mpeg audio files (mpeg-1 layers 1,2,3), which includes mp3 files. The library has a bunch of filters which are implemented as plugins, such as histogram, pauseRate, signalMagnitude etc.
(last successful compile was probably round 2005)
Sonic Visualiser is an application for inspecting and analysing the contents of music audio files. It combines powerful waveform and spectral visualisation tools with automated feature extraction plugins and annotation capabilities.
LAoE is a rich featured graphical audiosample-editor, based on multi-layers, floating-point samples, volume-masks, variable selection-intensity, and many plugins suitable to manipulate sound, such as filtering, retouching, resampling...
Connect your computer to your music/sound system and use your netbook, laptop, Apple, IPhone or smartphone to control the server from wherever you are. Perfect for parties, pubs... Access your audio, mp3 library remotely or dj from the dance floor.
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Easy-to-use platform-independent plugin API for the extraction of low-level features from audio data in PCM format, as required in the context of music information retrieval software.
TSSBank is written in c#(.Net 2.0).The main aimed group is the disabled persons.This component outputs voice & textual outputs (with value/words)plus experimental Voice Recognition (VR) system that identifies more then 80% accurately with out training.