21–30 of 68 posts
Now we can create all-star bands that never existed. For example: Neil schon from journey. Lead guitar Heart sisters doing lead vocals and lead/rthyum guitar Flea -- bass guitar from Chili Peppers Neal Peart -- drummer from rush Tony kay --- keys from genesis The only difficulty is they must all be playing the same song. Then we can extract, transpose if needed, and remix together.
It says it can be 100 times faster than in real-time. So can it be run in real-time? I am thinking about extracting features for music visualization but it could make a DJ happy also.
The first one refers to the speed of the processing in relation to the length of the recording - so, say, you can process a 10 minute recording in 1 minute then you're 10x real-time. However, your analysis might require the full track to be available for best outcomes, and so you cannot really start with the processing until the full source is available.
The latter is what "online" processing refers-to, the ability to process on-the-fly in parallel to the recording. Obviously, this cannot be faster than real-time ;-) but hopefully it is not slower, either. Often times, though, you get a (somewhat constant and) hopefully slow offset, i.e., you can process a 10 minute recording online in the same time but you need another 10 seconds on top of that.
This is, by the way, not restricted to source separation, it applies to other disciplines as well, say, automatic speech recognition.
Historically, blending between mixed stereo tracks has limited to mixing EQ bands, but now DJs will be able to layer and mix the underlying stems themselves -- like putting the vocal from one track onto an instrumental section on another (even if there were never a capella / instrumental versions released.)
It also opens up a previously unreachable world for amateur remixing in general; for instance, creating surround sound mixes from stereo or even mono recordings for playback in 3D audio environments like Envelop (https://envelop.us) [disclaimer: I am one of the co-founders of Envelop]
https://github.com/introlab/odas https://github.com/introlab/manyears https://github.com/introlab/16SoundsUSB
Website of the team behind these:
I was even able to run it on their notebook https://colab.research.google.com/github/deezer/spleeter/blo... without setting anything up locally.
The results of vocal separation were quite impressive.
(1) Open the file in Audacity and switch to Spectrogram view, (2) set a high-pass filter with ~150 Hz, i.e. filter out frequencies lower than that (which tend to be loud anyway), (3) don’t remove the higher frequencies (which aren’t loud), because they are what make the consonants understandable (apparently), (4) look for specific noises, select the rectangle, and use “Spectral Edit Multi Tool”.
But if machine learning can help that would be really interesting! This Spleeter page does mention “active listening, educational purposes, […] transcription” so I'm excited.
Though then you'll run into compatibility errors like "No module named 'tensorflow.contrib'" which you'll have to fix.
It says it can be 100 times faster than in real-time. So can it be run in real-time? I am thinking about extracting features for music visualization but it could make a DJ happy also.
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