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Artificial intelligence-driven/automated tagging


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Having recently spent several days on a tagging project (and I haven't even started with the genre tagging) I was thinking that this job could easily be automated to save a lot of time and to reduce errors.

 

I'm not talking about using an online tagging database because these online libraries contain problems of their own which still have to be corrected but at least they offer a starting point and even tags on commercial platforms such as Spotify or Qobuz are riddled with errors. A program could be written to scan a whole library and look for likely mistakes, e.g. artist tags which differ slightly but are likely to be the same could be flagged -> "I found separate tags for 'Beatles' and 'The Beatles'." Then you could select which tag to apply in all cases or choose to do nothing at all. Potential spelling errors could be flagged, as well as track numbers which accidentally found their way into track titles, etc. Other tags such as disk number and total disks are so time consuming to do manually but would be a snap with a smart tagging app.

 

Now for the really interesting part: there are already AI apps which can analyze a music track and identify the genre accurately (90-99% depending on the genre), not by looking up a database but by analyzing the actual music. THIS got my attention :) You can test this for yourself for free at https://tag.musiio.com/home  (unfortunately the demo only works with .mp3, .wav and .m4a file types – I have no idea whether this limitation applies to the paid plans). This service is not really aimed at the individual but surely intelligent tagging like this must be just around the corner for the music enthusiast and personally I can't wait!

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I think there are already such applications, that can for example identify the "mood" of tracks, or similar attributes, and are used for automatic suggestions (radio). I don't personally find that of any interest, but others may.

 

Here's an example that popped up with a simple Google search - there must be many others: https://towardsdatascience.com/predicting-the-music-mood-of-a-song-with-deep-learning-c3ac2b45229e

 

There is also research being done on music analysis to identify the specificty of musicians (ex: what makes a Charlie Parker so identifiable and different from, say, a Johnny Hodges ?).   Not sure this is of general interest.

 

In terms of "tagging" (I prefer to talk about metadata), I spend a lot of time adding metadata to my albums - recording dates, venues, and credits. Those are not things that can be guessed, and they need to be accurate.

 

Provided there were an accurate repository for metadata, which does not exist today, and provided this repository were linked to the digital tracks (ex: AcurrateRip database), then one interesting application would be automatic recognition based on tracks, and not based on albums (as is the case for example with Roon). But that's a lot of ifs...

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There are different _types_ of metadata. My own name for them are:

 

- Identification tags - identifies a release in some way (not necessarily uniquely, that could be a subset), e.g. ISRC, MB ID, a URI, album title, year, cat #

- Structural tags - defines the ordering and structuring of a release, e.g. disc number, track number, grouping title

- Classification tags - more subjective, e.g. genre, mood etc

 

In terms of identifying inconsistent data, this is already done by several apps.

 

The thing with auto suggesting tag values for classification tags, like detecting genre, is that while you might get "a" value you still need bounds within which values are acceptable. For example, within genre you don't want to end up with 200 genres - it just makes a music library unusable. These constraints could, of course, be automated.

 

bliss - fully automated music organizer. Read the music library management blog.

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2 hours ago, Dan Gravell said:

In terms of identifying inconsistent data, this is already done by several apps.

 

I would be interested to know which ones you would recommend for this purpose.

 

 

2 hours ago, Dan Gravell said:

The thing with auto suggesting tag values for classification tags, like detecting genre, is that while you might get "a" value you still need bounds within which values are acceptable. For example, within genre you don't want to end up with 200 genres - it just makes a music library unusable. These constraints could, of course, be automated.

 

That's a very good point. Some tags are insanely specific. The app which I linked to above seemed to identify several genres, if applicable, which would than allow you to automate the removal of overly specific tags, e.g. Jazz and Vocal Jazz could be later reduced to Jazz, if you so wished.

 

Still very messy though. Sigh...

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  • 5 weeks later...

@lamode

 

Thoughts on Bliss?  I have an embarrassing large, sloppily-named and duplicate-saturated music collection I dream of having automagically fixed.  I'm looking for a reasonably accurate, stable, multiformat, multi-genre capable fixer for someone too impatient or stupid to master the other commercial organizers.  I suspect my problems relate to size, but dividing my collection from entire to artist in size hasn't helped.  

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I have played around with Jaikoz and Songkong, haven't bought either though. They make a good starting point. I normally used it on a per album or per artist basis rather than on an entire library because there are always a few manual interventions needed and they are easier to find and fix when working on a smaller scale.

 

http://www.jthink.net/jaikoz/jsp/overview/startup.jsp

No electron left behind...

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10 hours ago, WMW said:

@lamode

 

Thoughts on Bliss?

 

I tried it a month ago so unfortunately my memory of it isn't as fresh any more.

 

My impression was that I liked the overall concept and design of the app but it was let down by the quality of the data source upon which it relied. For example, one of my pet peeves is tags which combine multiple artists into one tag, e.g. a tag which creates a new artist tag of "Nancy Sinatra & Frank Sinatra" rather than creating two individual artist tags. Or, even worse, tags of the same pairing using a mixture of "and" and "&" for different albums.

 

In some cases, the database was just wrong. For example, this album cover indicates an artist name of "Buddy Bregman and his Orchestra":

 

a.jpg.c71d2cb1f13b7245d3810154b853901e.jpg

 

 

However, according to Bliss, this artist name is incorrect and should be "The Buddy Bregman Orchestra"

 

370916546_Screenshot2021-04-09at11_27_16.thumb.jpg.3154db387ef241c428cfa718dbe2317b.jpg

 

 

Apart from the quality of the database, I liked it a lot and appreciated that it was possible to review suggestions before committing to them. For people with large collections which need extensive fixing, it could be a great starting point.

 

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Thanks for this feedback @lamode!

 

The artists one is a common request. There are two ways of looking at it:

 

- Making sure incoming artist data splits into separate tags

- Analysing existing tags and split those

 

In general, this would be best implemented as a rule I think, because different players will have different demands - I've learnt how primitive some are, and support for multiple artist fields is not an option on some (my own car stereo only supports ID3v1 for example).

 

The artist name is an interesting one - the data is coming from https://www.discogs.com/Buddy-Bregman-And-His-Orchestra-Swinging-Kicks/master/450155 which lists the one from the cover as the canonical artist name for the release, but is also linked to https://www.discogs.com/artist/897724-The-Buddy-Bregman-Orchestra which is the canonical name for the artist - we use both as suggestions and that's why you have the two choices you showed in the screenshot. Maybe the latter should be a lower score, and so won't get the "recommended" tag. What do you think?

 

bliss - fully automated music organizer. Read the music library management blog.

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Hi @Dan Gravell, I wish now that I had posted feedback when my memory was fresh. The Buddy Bregman example was just the first that I found today and that is a particularly tricky example as back in those days the artist name could change a little with every release as they formed slightly difference groups and so there is an argument to be made for unifying the artist name (and this probably explains why Discogs links to the alternative name).

 

However, there are some better examples such as this mystifying suggestion to change the album artist to "Various Artists" :)

 

1971166845_Screenshot2021-04-09at18_28_00.thumb.jpg.ec5a2fcb512d2ed052dfcc99594d7f3d.jpg

 

Even the alternative suggestion is wrong as it tries to correct "and" to "&" despite "and"  being used on the album itself.

 

480628229_Screenshot2021-04-09at18_30_36.thumb.jpg.96298afb85894b95562a76e7a39c53f4.jpg

 

 

The album cover for reference:

 

R-2725992-1588319886-7690.jpeg.jpg.a1ae53ac017e957416c8d06770568d8c.jpg

 

 

These are fairly small niggles and I would still rate Bliss above any other solution I've seen. I would recommend to users to take the time to check the recommendations.

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Just a small example of Bliss being misled by the Discogs database. The track name is correct but Bliss would like to change the word "For" to "Fro".

 

283664851_Screenshot2021-04-09at19_08_25.thumb.jpg.30e3582f4d90a2b2b5df061a6f331051.jpg

 

This seems to be an error in the Discogs database.

 

1996097994_Screenshot2021-04-09at19_11_47.thumb.jpg.ef8394bcafbb1aff7ea4e2085c63253c.jpg

 

To remove any doubt, here is the track listing on the CD itself:

 

550x504.jpg.08ac797f69850766bd3b5718669236dc.jpg

 

I will try to submit a correction to Discogs for this example.

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Something which is closer to an actual programme bug is the overloaded pop-up window which appears when hovering over a suggestion on a classical album. It becomes impossible to review the suggestion as the suggestion doesn't fit into the pop-up.

 

993018929_Screenshot2021-04-09at19_16_46.thumb.jpg.a35d10a11dd1c06f46dde7f4fbf7e523.jpg

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