We want to test and compare the splitting results of the leading open-source stem separation programs on the market with the LALAL.AI Rocknet and Cassiopeia editions. ![]() The rise of AI technologies is having a significant impact on progress in the audio source separation(1) area. Because of that, it’s possible to get rid of a significant portion of audio artifacts. This can lead to various unpleasant effects that give separated stems a certain dry, plastic sound.Ĭassiopeia, on the other hand, contains an advanced accounting mechanism for the phase component of the input signal and generates the phase for the output signal. Comparable to Rocknet in complexity, Cassiopeia’s breakthrough capabilities of tracking the input and output signal phase components are unparalleled.īoth solutions work in the frequency domain but Rocknet only considers the amplitude component while ignoring the phase component. One might assume that Cassiopeia is a successor to Rocknet but it’s actually a completely new neural network architecture. It may take longer for the new network to produce results, however, they can be more precise and clean than those of Rocknet.īelow you can learn more about Cassiopeia, what makes it different from Rocknet, and how it compares to other popular music source separation solutions. All of that makes for improved splitting results with significantly fewer audio artifacts and unnatural sounds.īoth Rocknet and Cassiopeia are available for trial and use on the LALAL.AI site. It’s a next-generation solution relative to Rocknet, the original LALAL.AI neural network, but with new architecture and more advanced capabilities. We are happy to present Cassiopeia, a new source separation neural network we’ve recently trained and implemented. LALAL.AI starts the new year of 2021 with a leap into the future of stem splitting.
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