Comments (2)
Hi,
Well as seen in the picture, the more genres you add to the clustering, the less Bliss is able to perform clustering successfully:
Because I used very dissimilar genres, I'd say roughly half of the tracks are distributed into right clusters, and half are just scattered around genres that are similar, but not exactly the same (some blues songs in jazz, etc).
So, in short, I think it would still be possible to make « genre » clouds based on Bliss' output values; it would just need a huge amount of data. I can clean and upload the Octave file I used to make the graph if you want to fiddle with it.
from bliss.
Cool! :)
from bliss.
Related Issues (20)
- cosine_similarity producing similar result values? HOT 15
- libswresample dependency only available for very latest distros HOT 9
- trying to decode more than 7 files produces crash of the process HOT 10
- Fails to build with ffmpeg-6.0 HOT 3
- ReplayGain HOT 3
- ffmpeg garbage output HOT 2
- Online database HOT 4
- Segfault HOT 3
- Freeing struct upon BL_UNEXPECTED HOT 3
- Wrong usage example HOT 2
- Ranges for each analysis value HOT 4
- Probleme de compilation HOT 11
- use aubio for analysis? HOT 2
- build error with ffmpeg 2.8.8 HOT 2
- ArchLinux PKGBUILD fix please HOT 1
- Status of this project HOT 2
- Failed test building on Debian/buster HOT 1
- Please tag the repository with a version number HOT 2
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from bliss.