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Messages posted by: hazay19  XML
Profile for hazay19 -> Messages posted by hazay19 [4]
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Thank you for the response.

I re-read my post and I guess I wasn't as clear in my writing as I was in my head. My apologies.

What I was trying to say is that I would definately prefer to delete the lower quality ones, if I had to make a choice. But, my question was more in regards to your thoughts about the following...

Say you have a complete album with 10 tracks (1-10) all in 128. Then you have two favorite songs on that album (say tracks 1 and 2) that you have a duplicate copy in FLAC.

It is my understanding that if you run the duplicate program in Jaikoz, as it stands now, it would delete tracks 1 and 2 at 128 in this scenario, which makes sense for most users. But, for me, someone not woried about disdk space, I may be needlessly deleting tracks 1 and 2 at 128 or at least seperating those two tracks from the rest of the album. So, if I ever play the album in order from 1 to 10, then will be a noticeable difference in sound quality between tracks 1 and 2, compared to the tracks on the rest of the album.

The type of duplicate removal that I would prefer and hope for, and maybe I am the only one, would be to only delete the exact same song at the same bit rate, same file size, etc. that is only from the result of having moved the same files to different hard drives and duplicates being created.

So, in other words, is there a way keep a lossless copy and 128 version of the same song, and only delete a duplicate if it is an exact copy of either one of those lossless or 128 song?

Best Regards,


Thank you for your thoughts and for taking the time to reply.

I am far from a programmer and can't tell you the first thing about an API, but this was the kind of out of the box thinking that I was hoping for when I posted this.

Wonder what Paul's thoughts are on this and what are the % chance of implementation in the long term, once other more prominent issues are taken care of.

Thank you,

This may be completely out of left field, but I wanted to get your thoughts about a new possible way to tag music collection. FYI.. I have purchased Jaikoz, am going to purchase Song Kong and will support Paul in any way I can, I just wanted to get some thoughts on this.

With so much good music tag data on Spotify and the itunes store, I thought trying to find some other way to harness it. Here is what I came up with...

Spotify: Getting a spotify premium account, which streams in 320kbps. Search for each one of the albums and songs in my collection and then using a program like Audials Tunebyte (which can automatically rip the song, look for cover art, tag the song, break up the tracks, etc.). This happens in real time, so it may take a long time. But, once it will done, it should be in excellent shape and then can use Song Kong or Jaikoz to fix all the incorrect tags...

Itunes: Sign up for itunes match... Dump as many songs as possible into itunes match, even with poor metadata, no cover art, etc. Let itunes match "match" as many songs as it can to it's own server. Then, download all of those songs off the apple server all at a consistent 256kbps, normalized, etc. (as if you purchaed it from the itunes store). Then, since itunes keeps users tags and does not fix tags, use the open source program "itunes-match-tagger", which corrects the tags right from the apple server. Finally, use jaikoz or song kong at that point and fix any incorrect tags...

Look forward to any thoughts and comments,

I haven't thought this through completely, but I would appreciate some feedback.

I read somewhere on here the steps that Jaikoz uses when identifying duplicates. I really like the thought and logic that went into it and need to test it a bit more.

However, one thing that I deal with is having a whole album in 128 or 192 and then having a few of my favorite songs on that album in 256 or 320 or lossless. I even thought my favorite songs are in higher quality, I would feel weird deleting the lower quality songs, as I woulld want to keep the album together at the same quality.

How have other worked through this situation and what are some best practices.

Best Regards,

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