This application uses state-of-the-art source separation models to remove vocals from audio files. UVR’s core developers trained all of the models provided in this package (except for the Demucs v3 and v4 4-stem models).
github.com/Anjok07/ultimatevocalremovergui
EdgeSounds’ RatHole (former GenieSys RatHole) is a free unique nondestructive universal compression utility. Its function is based on a principle of self-training neural networks. EdgeSounds RatHole was especially designed for nondestructive compression of any files containing audio data in PCM 8/16/24 bit or IEEE_FLOAT 32 bit format.
A new EdgeSounds compression algorithm makes it possible to efficiently reduce the size of packed audio data and later unpack exactly same bits, with no difference to the original data. The compression algorithm compresses audio data, considering the bit depth of the digital data contained in the audio file (8/16/24/32 bit). The algorithm is proven to be equally effective on compressing the following file types:
The compression ratio of the algorithm depends on the size of audio data, the balance between the tone and noise component, bit depth and other factors, and usually varies from 36% to 78% or even more, with an average of 48-56%. The higher is the bit depth and the fidelity of audio data, the better is the compression ratio.
The RatHole can be successfully used as a common archiving utility for any other file types as well.
www.edgesounds.com/Products/Software/RatHole
www.rarewares.org/others.php#edgesounds-rathole
We’ve created MuseNet, a deep neural network that can generate 4-minute musical compositions with 10 different instruments, and can combine styles from country to Mozart to the Beatles. MuseNet was not explicitly programmed with our understanding of music, but instead discovered patterns of harmony, rhythm, and style by learning to predict the next token in hundreds of thousands of MIDI files. MuseNet uses the same general-purpose unsupervised technology as GPT-2, a large-scale transformer model trained to predict the next token in a sequence, whether audio or text.
https://openai.com/blog/musenet/
The Audio File 