Hi, new Jaikoz customer here.
I have a large collection of MP3s - 14,412 files at last count. I just finished retrieving MusicIP Acoustic IDs for all files (about 12 hours!), MusicBrainz IDs and MusicBrainz data.
Most of my MP3 files started out with accurate tags, but I was missing some fields for many files (ie… genre, album cover art, year of recording) so I thought Jaikoz would be a great way to automatically fix up those problems.
What I found: MusicIP / MusicBrainz is about 95% accurate identifying my music. That’s good, but in a colleciton this size it means that there are about 720 incorrectly tagged files, randomly distributed throughout my collection. Simply applying the changes as-is will mess up those files’ tags. On the other hand, reviewing each file one by one is proving to be tedious and error prone. Plus on a collection this size, simply navigating from folder to folder in the view takes about 5sec. (on an Athlon x2 6000+ / Raptor HD system)
What I’d like to see is Jaikoz to apply some level of fuzzy matching between MusicBrainz retrieved data and the existing MP3 tags and/or existing folder hierarchy where the file is located and file name. I’d like it to use the following update logic:
If existing ID3 tags “fuzzy match” OK to the musicbrainz data
Update missing ID3 tags with musicbrainz data, leave existing tags alone
Or, if the filename/folder structure ‘fuzzy matches’ to the musicbrainz data, but the ID3 tags do not, update all ID3 tags to match what MusicBrainz has.
If the musicbrainz data does not fuzzy match to the ID3 tags, yet their audio fingerprints are the same, then flag the file in such a way the file’s id3 tags and directory location can be displayed for quick bulk reviews and a decision to apply or discard the musicbrainz id3 tags.