AccurateRip Error on Arctic Monkeys disc

[https://thomasvs.myopenid.com/]

$ rip cd --device /dev/cdrom1 rip --offset 48 --output-directory mori
Checking device /dev/sr0
CDDB disc id 8608d20c                         
MusicBrainz disc id 3mGdiDepgorlStCzqPseCCh9kDg-
/usr/lib/python2.6/site-packages/musicbrainz2/model.py:21: DeprecationWarning: the sets module is deprecated
  from sets import Set
Matching releases:
Artist  : Arctic Monkeys
Title   : Favourite Worst Nightmare

Ripping track 1 of 12: 01. Arctic Monkeys - Brianstorm.flac
Checksums match for track 1                        
Peak level: 99.95 %
Rip quality: 100.00 %
Ripping track 2 of 12: 02. Arctic Monkeys - Teddy Picker.flac
Checksums match for track 2                        
Peak level: 99.96 %
Rip quality: 100.00 %
Ripping track 3 of 12: 03. Arctic Monkeys - D Is for Dangerous.flac
Checksums match for track 3                        
Peak level: 99.95 %
Rip quality: 100.00 %
Ripping track 4 of 12: 04. Arctic Monkeys - Balaclava.flac
Checksums match for track 4                        
Peak level: 99.95 %
Rip quality: 100.00 %
Ripping track 5 of 12: 05. Arctic Monkeys - Fluorescent Adolescent.flac
Checksums match for track 5                        
Peak level: 99.95 %
Rip quality: 100.00 %
Ripping track 6 of 12: 06. Arctic Monkeys - Only Ones Who Know.flac
Checksums match for track 6                        
Peak level: 99.95 %
Rip quality: 100.00 %
Ripping track 7 of 12: 07. Arctic Monkeys - Do Me a Favour.flac
Checksums match for track 7                        
Peak level: 99.95 %
Rip quality: 100.00 %
Ripping track 8 of 12: 08. Arctic Monkeys - This House Is a Circus.flac
Checksums match for track 8                        
Peak level: 99.95 %
Rip quality: 100.00 %
Ripping track 9 of 12: 09. Arctic Monkeys - If You Were There, Beware.flac
Checksums match for track 9                        
Peak level: 99.95 %
Rip quality: 100.00 %
Ripping track 10 of 12: 10. Arctic Monkeys - The Bad Thing.flac
Checksums match for track 10                       
Peak level: 99.95 %
Rip quality: 100.00 %
Ripping track 11 of 12: 11. Arctic Monkeys - Old Yellow Bricks.flac
Checksums match for track 11                       
Peak level: 99.95 %
Rip quality: 100.00 %
Ripping track 12 of 12: 12. Arctic Monkeys - 505.flac
Checksums match for track 12                       
Peak level: 99.95 %
Rip quality: 100.00 %
AccurateRip URL http://www.accuraterip.com/accuraterip/d/d/3/dBAR-012-000fc3dd-00956f1a-8608d20c.bin
5 AccurateRip reponses found
Traceback (most recent call last):                   
  File "/home/thomas/svn/src/morituri/trunk/bin/rip", line 28, in <module>
    sys.exit(main.main(sys.argv[1:]))
  File "/home/thomas/svn/src/morituri/trunk/morituri/rip/main.py", line 12, in main
    ret = c.parse(argv)
  File "/home/thomas/svn/src/morituri/trunk/morituri/extern/command/command.py", line 295, in parse
    return self.subCommands[command].parse(args[1:])
  File "/home/thomas/svn/src/morituri/trunk/morituri/extern/command/command.py", line 295, in parse
    return self.subCommands[command].parse(args[1:])
  File "/home/thomas/svn/src/morituri/trunk/morituri/extern/command/command.py", line 271, in parse
    ret = self.do(args)
  File "/home/thomas/svn/src/morituri/trunk/morituri/rip/cd.py", line 295, in do
    prog.verifyImage(runner, responses)
  File "/home/thomas/svn/src/morituri/trunk/morituri/common/program.py", line 403, in verifyImage
    self._verifyImageWithChecksums(responses, cuetask.checksums)
  File "/home/thomas/svn/src/morituri/trunk/morituri/common/program.py", line 433, in _verifyImageWithChecksums
    csum, i + 1, j + 1, response.checksums[i])
AssertionError: checksum 3194766910 for 3 matches wrong response 3, checksum c8d29083

[https://www.google.com/accounts/o8/id?id=aitoawnfkt2szxtw0hkw4_yj-d9lrmlf69i_szu]

Ticket #6 and #7 were duplicate of this bug.

[http://openid-provider.appspot.com/sjordet]

Ticket #23 is also a duplicate.

[http://www.google.com/profiles/ben.konrath]

badly drawn boy disc crashing morituri

[http://www.google.com/profiles/ben.konrath]

I'm having a similar problem with a couple of discs that I have. I've attached a morituri run with Badly Drawn Boy demonstrating the problem.

[http://lool.myopenid.com/]

Trying to look into this bug, this fatal error is triggered in morituri/common/program.py, _verifyImageWithChecksums() where basically checksums computed locally for each track are compared against the accuraterip database responses.

I didn't find documentation for the format of the accuraterip database, albeit morituri/common/accurip.py is pretty readable (it does skip some data which could possibly be helpful); it doesn't seem to be a parsing problem though, but rather how morituri handles the responses or the contents of the responses themselves.

The main loop compares each track checksum with the corresponding checksum in all accuraterip responses, but it doesn't fail if a response has a checksum mismatch with the local checksum. Another constraint in the current implementation is that the first time a checksum matches, the corresponding response is used for all subsequent checksums. If the first matching response has matching checksums followed by non-matching checksums, and another response has matching checksums for this track, the program errors out, which I think is what happens here.

On top of this, there are confidence stats; morituri looks at the highest confidence for each track, independently of the checksum.

Now I'm not sure what kind of bias we can expect from responses:

• is it possible that two unrelated CDs end up in the list of responses so that some responses have zero matching checksums? apparently yes, read below
• are there partial responses with missing checksums for some tracks? (a comment in the code suggests that some responses have a confidence of zero, which suggests they should be ignored, but perhaps only some tracks should be ignored) apparently yes, read below

I'm not sure how confidence is computed either; is it globally reliable? If it is from the rip, is it likely that some drives report higher confidences than they really provide? Apparently, the database as weird for confidences for some CD ids, apparently for all responses for this id.

So I extended morituri/common/accurip.py to output some data for various URLs; I'll attach it here.

I tried running the program again various types of failing CDs; first, for the failure in this ticket; CD is "The Virgin Suicides" with AccurateRip? URL  http://www.accuraterip.com/accuraterip/f/8/4/dBAR-012-0017c48f-00dd628e-a60ca90c.bin:
PYTHONPATH=. python morituri/common/accurip.py  http://www.accuraterip.com/accuraterip/f/8/4/dBAR-012-0017c48f-00dd628e-a60ca90c.bin

Found 5 responses

Found 5 responses with 12 tracks [1, 2, 3, 4, 5]

For track count 12

Track 1

1 result(s) for checksum 00000000: [{'confidence': 0, 'response': 5}]
1 result(s) for checksum cee804a5: [{'confidence': 3, 'response': 2}]
1 result(s) for checksum d85f5e9b: [{'confidence': 2, 'response': 3}]
1 result(s) for checksum 7a5eca8d: [{'confidence': 3, 'response': 1}]
1 result(s) for checksum 10540eb6: [{'confidence': 2, 'response': 4}]

Track 2

1 result(s) for checksum 00000000: [{'confidence': 0, 'response': 5}]
1 result(s) for checksum 27dd7a7b: [{'confidence': 3, 'response': 2}]
1 result(s) for checksum eb22b1cd: [{'confidence': 2, 'response': 4}]
1 result(s) for checksum 5b6221cb: [{'confidence': 3, 'response': 1}]
1 result(s) for checksum 0b848c87: [{'confidence': 2, 'response': 3}]

Track 3

1 result(s) for checksum 00000000: [{'confidence': 0, 'response': 5}]
1 result(s) for checksum 14e295a2: [{'confidence': 3, 'response': 2}]
1 result(s) for checksum 9f642dc2: [{'confidence': 3, 'response': 1}]
1 result(s) for checksum c685e05a: [{'confidence': 2, 'response': 3}]
1 result(s) for checksum ec1eac36: [{'confidence': 2, 'response': 4}]

Track 4

1 result(s) for checksum 723aafee: [{'confidence': 3, 'response': 2}]
1 result(s) for checksum 00000000: [{'confidence': 0, 'response': 5}]
1 result(s) for checksum 95088aaf: [{'confidence': 2, 'response': 4}]
1 result(s) for checksum 01d95844: [{'confidence': 2, 'response': 3}]
1 result(s) for checksum 5c3d5e78: [{'confidence': 3, 'response': 1}]

Track 5

1 result(s) for checksum 00000000: [{'confidence': 0, 'response': 5}]
1 result(s) for checksum 2d4861b6: [{'confidence': 2, 'response': 4}]
1 result(s) for checksum 6516761a: [{'confidence': 3, 'response': 1}]
1 result(s) for checksum bf7a0d2c: [{'confidence': 3, 'response': 2}]
1 result(s) for checksum 09034748: [{'confidence': 2, 'response': 3}]

Track 6

1 result(s) for checksum 00000000: [{'confidence': 0, 'response': 5}]
1 result(s) for checksum 5c1f32a4: [{'confidence': 2, 'response': 3}]
1 result(s) for checksum ca381420: [{'confidence': 2, 'response': 4}]
1 result(s) for checksum d58e0203: [{'confidence': 3, 'response': 2}]
1 result(s) for checksum 062ee181: [{'confidence': 3, 'response': 1}]

Track 7

1 result(s) for checksum 738680e4: [{'confidence': 2, 'response': 5}]
1 result(s) for checksum 422143f7: [{'confidence': 2, 'response': 3}]
1 result(s) for checksum 36d22e24: [{'confidence': 2, 'response': 4}]
1 result(s) for checksum 9e81a5c5: [{'confidence': 3, 'response': 2}]
1 result(s) for checksum 7404aa97: [{'confidence': 3, 'response': 1}]

Track 8

1 result(s) for checksum cba3f5f4: [{'confidence': 2, 'response': 4}]
1 result(s) for checksum ab0efe22: [{'confidence': 2, 'response': 5}]
1 result(s) for checksum ddb98fc4: [{'confidence': 2, 'response': 3}]
1 result(s) for checksum 8e1c8628: [{'confidence': 3, 'response': 1}]
1 result(s) for checksum 0f783c18: [{'confidence': 3, 'response': 2}]

Track 9

1 result(s) for checksum cf7737fa: [{'confidence': 2, 'response': 5}]
1 result(s) for checksum 126b24fd: [{'confidence': 3, 'response': 1}]
1 result(s) for checksum cfc6e7f5: [{'confidence': 3, 'response': 2}]
1 result(s) for checksum cc8ccee3: [{'confidence': 2, 'response': 4}]
1 result(s) for checksum c5d553cb: [{'confidence': 2, 'response': 3}]

Track 10

1 result(s) for checksum bb908176: [{'confidence': 2, 'response': 3}]
1 result(s) for checksum ed41516d: [{'confidence': 3, 'response': 2}]
1 result(s) for checksum eaa68d04: [{'confidence': 2, 'response': 4}]
1 result(s) for checksum a3ed1ea4: [{'confidence': 2, 'response': 5}]
1 result(s) for checksum 9bf60b39: [{'confidence': 3, 'response': 1}]

Track 11

1 result(s) for checksum 5a7b5e20: [{'confidence': 2, 'response': 3}]
1 result(s) for checksum 80c0d2e3: [{'confidence': 2, 'response': 4}]
1 result(s) for checksum 118eaeb5: [{'confidence': 2, 'response': 5}]
1 result(s) for checksum 9ca2fb1d: [{'confidence': 3, 'response': 1}]
1 result(s) for checksum 29801e5e: [{'confidence': 2, 'response': 2}]

Track 12

1 result(s) for checksum 7fedc08a: [{'confidence': 3, 'response': 1}]
1 result(s) for checksum 00000000: [{'confidence': 0, 'response': 5}]
1 result(s) for checksum 5936930c: [{'confidence': 2, 'response': 3}]
1 result(s) for checksum 220e6f99: [{'confidence': 2, 'response': 4}]
1 result(s) for checksum f607c1b2: [{'confidence': 3, 'response': 2}]

Many interesting things here:

• always the same number of tracks; this might not be true for all CD ids, but I suspect it is due to the way the CD is constructed
• never more than one result for a track's checksum; it seems that only one response was ever submitted per real CD, but that multiple real CDs might have the same CD id in the accuraterip database
• response 5 seems completely bogus (checksum 0 and confidence 0 for 7 out of 12 tracks)

Since my failure with morituri was:
AssertionError?: checksum 294563509 for 11 matches wrong response 5, checksum 29801e5e
it seems that this means that I matched at least one checksum from response 2 before track 11 (response 2 has checksum 29801e5e for track 11), and for track 11 I matched another response (response 5).

Even ignoring zero confidence / zero checksum tracks, since the checksums differ between response 2 and response 5 for track 11, there is definitely no response which corresponds to my local rip :-/

I'm not quite sure how to interpret the results; did the rip indeed fail due to some improper offset handling or read errors or ...? Is response 5 some kind of overlay response?

I also used the debug output to look into another class of failures I was getting; "NOT accurate" reports for the last track on some CDs:
Track 12: rip accurate (confidence 147 of 156) [b948c50d], DB [b948c50d]
Track 13: rip accurate (confidence 141 of 143) [45f36bd0], DB [45f36bd0]
Track 14: rip NOT accurate (max confidence 133) [a41938d7], DB [42dfd237]

Track 14: rip accurate (confidence 22 of 75) [735e447b], DB [735e447b]
Track 15: rip accurate (confidence 22 of 76) [1e1d82e5], DB [1e1d82e5]
Track 16: rip NOT accurate (max confidence 78) [5d46d5f1], DB [d103d939]

Track 13: rip accurate (max confidence 45) [85c7d744], DB [85c7d744]
Track 14: rip accurate (max confidence 45) [f4fb8279], DB [f4fb8279]
Track 15: rip NOT accurate (max confidence 44) [84ef7e08], DB [e367517e]

For instance, Trainspotting, AccurateRip? URL  http://www.accuraterip.com/accuraterip/e/f/2/dBAR-014-0028b2fe-01acb8ad-cd11b80e.bin:
11 AccurateRip? reponses found
Track 0: unknown (not tracked)
Track 1: rip accurate (confidence 148 of 158) [073bc6d5], DB [073bc6d5]
Track 2: rip accurate (confidence 149 of 159) [118fb072], DB [118fb072]
Track 3: rip accurate (confidence 149 of 152) [c19cd97c], DB [c19cd97c]
Track 4: rip accurate (confidence 146 of 159) [08070666], DB [08070666]
Track 5: rip accurate (confidence 142 of 157) [8d27b69c], DB [8d27b69c]
Track 6: rip accurate (confidence 148 of 155) [a54b5c85], DB [a54b5c85]
Track 7: rip accurate (confidence 146 of 151) [27c6fff2], DB [27c6fff2]
Track 8: rip accurate (confidence 150 of 159) [adafbf40], DB [adafbf40]
Track 9: rip accurate (confidence 146 of 161) [fec5950c], DB [fec5950c]
Track 10: rip accurate (confidence 143 of 155) [221eaaf4], DB [221eaaf4]
Track 11: rip accurate (confidence 143 of 160) [27a636bc], DB [27a636bc]
Track 12: rip accurate (confidence 147 of 156) [b948c50d], DB [b948c50d]
Track 13: rip accurate (confidence 141 of 143) [45f36bd0], DB [45f36bd0]
Track 14: rip NOT accurate (max confidence 133) [a41938d7], DB [42dfd237]

PYTHONPATH=. python morituri/common/accurip.py  http://www.accuraterip.com/accuraterip/e/f/2/dBAR-014-0028b2fe-01acb8ad-cd11b80e.bin
Found 11 responses

Found 11 responses with 14 tracks [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]

For track count 14

Track 1

1 result(s) for checksum 073bc6d5: [{'confidence': 148, 'response': 2}]
1 result(s) for checksum 29a8d84d: [{'confidence': 2, 'response': 6}]
1 result(s) for checksum 7133f9a3: [{'confidence': 158, 'response': 1}]
5 result(s) for checksum 00000000: [{'confidence': 0, 'response': 7}, {'confidence': 0, 'response': 8}, {'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}]
1 result(s) for checksum 510fcada: [{'confidence': 79, 'response': 3}]
1 result(s) for checksum 057e841a: [{'confidence': 3, 'response': 4}]
1 result(s) for checksum f007bf86: [{'confidence': 2, 'response': 5}]

Track 2

1 result(s) for checksum 65d8449a: [{'confidence': 77, 'response': 3}]
6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 6}, {'confidence': 0, 'response': 7}, {'confidence': 0, 'response': 8}, {'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}]
1 result(s) for checksum f48209fb: [{'confidence': 2, 'response': 5}]
1 result(s) for checksum 0b4b0e9a: [{'confidence': 3, 'response': 4}]
1 result(s) for checksum 118fb072: [{'confidence': 149, 'response': 2}]
1 result(s) for checksum e5c8bee2: [{'confidence': 159, 'response': 1}]

Track 3

1 result(s) for checksum c19cd97c: [{'confidence': 149, 'response': 2}]
6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 6}, {'confidence': 0, 'response': 7}, {'confidence': 0, 'response': 8}, {'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}]
1 result(s) for checksum 9b831c1c: [{'confidence': 3, 'response': 4}]
1 result(s) for checksum 06ee9f29: [{'confidence': 2, 'response': 5}]
1 result(s) for checksum 46b5741c: [{'confidence': 75, 'response': 3}]
1 result(s) for checksum 15582f3c: [{'confidence': 152, 'response': 1}]

Track 4

1 result(s) for checksum 08070666: [{'confidence': 146, 'response': 2}]
1 result(s) for checksum df6eb300: [{'confidence': 159, 'response': 1}]
6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 6}, {'confidence': 0, 'response': 7}, {'confidence': 0, 'response': 8}, {'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}]
1 result(s) for checksum 1bc8c93d: [{'confidence': 77, 'response': 3}]
1 result(s) for checksum db3766fd: [{'confidence': 3, 'response': 4}]
1 result(s) for checksum 13b12ee3: [{'confidence': 2, 'response': 5}]

Track 5

1 result(s) for checksum 8d27b69c: [{'confidence': 142, 'response': 2}]
5 result(s) for checksum 00000000: [{'confidence': 0, 'response': 7}, {'confidence': 0, 'response': 8}, {'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}]
1 result(s) for checksum 19ce1b81: [{'confidence': 3, 'response': 4}]
1 result(s) for checksum a8d07a41: [{'confidence': 76, 'response': 3}]
1 result(s) for checksum e30228e4: [{'confidence': 2, 'response': 5}]
1 result(s) for checksum efbccd2a: [{'confidence': 157, 'response': 1}]
1 result(s) for checksum 14f6ab18: [{'confidence': 2, 'response': 6}]

Track 6

6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 6}, {'confidence': 0, 'response': 7}, {'confidence': 0, 'response': 8}, {'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}]
1 result(s) for checksum 3ecfd7b2: [{'confidence': 3, 'response': 4}]
1 result(s) for checksum ccb07a0f: [{'confidence': 2, 'response': 5}]
1 result(s) for checksum e5686472: [{'confidence': 77, 'response': 3}]
1 result(s) for checksum a54b5c85: [{'confidence': 148, 'response': 2}]
1 result(s) for checksum 80d08043: [{'confidence': 155, 'response': 1}]

Track 7

1 result(s) for checksum 51229cf0: [{'confidence': 151, 'response': 1}]
1 result(s) for checksum 73bf36ff: [{'confidence': 2, 'response': 4}]
6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 6}, {'confidence': 0, 'response': 7}, {'confidence': 0, 'response': 8}, {'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}]
1 result(s) for checksum 27c6fff2: [{'confidence': 146, 'response': 2}]
1 result(s) for checksum 1711cbbf: [{'confidence': 77, 'response': 3}]
1 result(s) for checksum 750c7dfc: [{'confidence': 2, 'response': 5}]

Track 8

1 result(s) for checksum 41cb0451: [{'confidence': 77, 'response': 3}]
1 result(s) for checksum 4788db96: [{'confidence': 159, 'response': 1}]
1 result(s) for checksum adafbf40: [{'confidence': 150, 'response': 2}]
1 result(s) for checksum ddb62091: [{'confidence': 2, 'response': 4}]
6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 6}, {'confidence': 0, 'response': 7}, {'confidence': 0, 'response': 8}, {'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}]
1 result(s) for checksum 3377659a: [{'confidence': 2, 'response': 5}]

Track 9

1 result(s) for checksum 99d7d7ea: [{'confidence': 3, 'response': 4}]
1 result(s) for checksum fec5950c: [{'confidence': 146, 'response': 2}]
1 result(s) for checksum 5135a8cd: [{'confidence': 2, 'response': 5}]
6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 6}, {'confidence': 0, 'response': 7}, {'confidence': 0, 'response': 8}, {'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}]
1 result(s) for checksum f40d6560: [{'confidence': 161, 'response': 1}]
1 result(s) for checksum 7260b06a: [{'confidence': 76, 'response': 3}]

Track 10

1 result(s) for checksum 221eaaf4: [{'confidence': 143, 'response': 2}]
1 result(s) for checksum d42c52ba: [{'confidence': 2, 'response': 4}]
6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 6}, {'confidence': 0, 'response': 7}, {'confidence': 0, 'response': 8}, {'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}]
1 result(s) for checksum 33bd6638: [{'confidence': 155, 'response': 1}]
1 result(s) for checksum 765ab13a: [{'confidence': 71, 'response': 3}]
1 result(s) for checksum 2052828a: [{'confidence': 2, 'response': 5}]

Track 11

1 result(s) for checksum b42f500a: [{'confidence': 160, 'response': 1}]
1 result(s) for checksum 9fe81541: [{'confidence': 2, 'response': 5}]
1 result(s) for checksum 75bbb9c1: [{'confidence': 2, 'response': 4}]
6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 6}, {'confidence': 0, 'response': 7}, {'confidence': 0, 'response': 8}, {'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}]
1 result(s) for checksum f9fd9081: [{'confidence': 75, 'response': 3}]
1 result(s) for checksum 27a636bc: [{'confidence': 143, 'response': 2}]

Track 12

1 result(s) for checksum 224d4d8e: [{'confidence': 2, 'response': 4}]
1 result(s) for checksum 513c2183: [{'confidence': 156, 'response': 1}]
6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 6}, {'confidence': 0, 'response': 7}, {'confidence': 0, 'response': 8}, {'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}]
1 result(s) for checksum de12e54e: [{'confidence': 75, 'response': 3}]
1 result(s) for checksum 912c3066: [{'confidence': 2, 'response': 5}]
1 result(s) for checksum b948c50d: [{'confidence': 147, 'response': 2}]

Track 13

1 result(s) for checksum 2a4a3bdc: [{'confidence': 143, 'response': 1}]
1 result(s) for checksum 8d61b202: [{'confidence': 71, 'response': 3}]
6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 6}, {'confidence': 0, 'response': 7}, {'confidence': 0, 'response': 8}, {'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}]
1 result(s) for checksum f64391a2: [{'confidence': 2, 'response': 5}]
1 result(s) for checksum 458c6e82: [{'confidence': 2, 'response': 4}]
1 result(s) for checksum 45f36bd0: [{'confidence': 141, 'response': 2}]

Track 14

1 result(s) for checksum a7f838d7: [{'confidence': 7, 'response': 6}]
1 result(s) for checksum 57b8a7e7: [{'confidence': 2, 'response': 8}]
1 result(s) for checksum a41f38d7: [{'confidence': 133, 'response': 1}]
1 result(s) for checksum 32f8a7e7: [{'confidence': 16, 'response': 5}]
1 result(s) for checksum 0e38a7e7: [{'confidence': 55, 'response': 3}]
1 result(s) for checksum 8b0b3f5b: [{'confidence': 2, 'response': 11}]
1 result(s) for checksum f2c1d237: [{'confidence': 3, 'response': 7}]
1 result(s) for checksum 42dfd237: [{'confidence': 118, 'response': 2}]
1 result(s) for checksum 679fd237: [{'confidence': 24, 'response': 4}]
1 result(s) for checksum f081ebe7: [{'confidence': 2, 'response': 9}]
1 result(s) for checksum 8c5fd237: [{'confidence': 2, 'response': 10}]

What's interesting here is that there are multiple results for the zero checksum for almost every track except the last one.

Indeed, there is on result with my checksum and the checksums of response 2 match mine except for last track.

Responses which show zero checksum / zero confidence also consistently show low confidence in other tracks; perhaps these are encoded in another format than the other responses?

[http://lool.myopenid.com/]

Modified accurip.py with main debug helper

[http://lool.myopenid.com/]

I found some references on the accuraterip db format:

• perl implementation checking a .cue with .wav files against the DB:  http://www.srcf.ucam.org/~cjk32/ARCue/ARCue.pl
• blog post explaining the checksum algorithm:  http://jonls.dk/2009/10/calculating-accuraterip-checksums/
• C++ rewrite of ARCue.pl:  http://www.hydrogenaudio.org/forums/index.php?showtopic=59423
• author of the CRC discuss improvements:  http://forum.dbpoweramp.com/showthread.php?t=16463
• apparently a windows cuetools software (not the Berlios/Linux? one?) on SourceForge? supports AccurateRip? as well:  http://www.cuetools.net/doku.php
• rubyripper implementation and their research:  http://code.google.com/p/rubyripper/wiki/AccurateRip_Research

[http://lool.myopenid.com/]

I'm using this patch for now; ideally, I'm not sure exactly what's going on, either a bogus checksum logic, or a problematic drive, or perhaps simply the response on the server is incorrect? It seems the resolution of the checksums is relatively low in any case.

Perhaps a better way to approach this would be to look at all responses and all checksums, and keep responses which have at least 3 matching checksums?

For the record, the checksum mismatch on this particular CD happens only on the track before the last one; the last track has the correct checksum.

[http://thomasvs.myopenid.com/]

FYI, when I mailed the accuraterip maintainer about this, I got a (vague) reply:

Hello,

In my Linux CD ripper I've come to have a doubt about the interpretation
of AccurateRip? results.

Let's assume a disc has two sets of AccurateRip? results, set A and B.

Let's assume that my 11 track CD matches checksums with AR set A for the
first ten tracks. However, track 11 matches the checksum from set B.

Should this be treated as correct or invalid ? If I understand the
purpose of the algorithm correctly, all tracks should match with only
one set of AccurateRip? results.

For an example of such a problem I ran into, see
 https://thomas.apestaart.org/morituri/trac/ticket/5

Thanks in advance,
Thomas

Sets in Accuraterip can be intermingled, so for a 4 track disc you could
have:

A
A
B
A

B
B
A
B

[http://thomasvs.myopenid.com/]

I'm adding a rip accuraterip show command that takes your debug output.

You seem to handle the possibility of the number of tracks being different across responses; that sounds unlikely to me, as the accuraterip id is calculated from the toc, and different number of tracks would generate a different accuraterip URL.

So I'll remove that bit of complexity.

[thomas]

In [462]:

based on code by: Loïc Minier <lool@…>

• morituri/rip/Makefile.am:
• morituri/rip/main.py:
• morituri/rip/accurip.py (added): Add a rip accurip show command to show the accuraterip information for a given URL. See #5.

[http://thomasvs.myopenid.com/]

I added that code, and I changed it to order it by confidence.

Which brings out an interesting fact, and a hunch. It looks like the response sets are in order of confidence; ie. response set 1 always has the highest confidence levels.

This leads me to believe that the response sets are just that - the first one has the highest confidence for each track, the second one the second-highest, ...

so the response sets do not directly match pressings.

IMO that's a mistake in the AccurateRip? database - it would not make sense IMO for a track on pressing A to match a result for pressing B, while all other tracks match for pressing A. The checksums should have been kept together for a CD.

But given that that doesn't seem to be the case, there's no point in sticking to it.

[http://thomasvs.myopenid.com/]

For your 14 track disc I get this:

rip accurip show http://www.accuraterip.com/accuraterip/e/f/2/dBAR-014-0028b2fe-01acb8ad-cd11b80e.bin
Found 14 responses for 14 tracks

Track 1:
  1 result(s) for checksum 7133f9a3: [{'confidence': 171, 'response': 1}]
  1 result(s) for checksum 073bc6d5: [{'confidence': 164, 'response': 2}]
  1 result(s) for checksum 510fcada: [{'confidence': 89, 'response': 3}]
  1 result(s) for checksum 29a8d84d: [{'confidence': 13, 'response': 4}]
  1 result(s) for checksum 36d74f5c: [{'confidence': 11, 'response': 5}]
  1 result(s) for checksum 99ee1a65: [{'confidence': 5, 'response': 6}]
  1 result(s) for checksum a7a4b81d: [{'confidence': 3, 'response': 8}]
  1 result(s) for checksum 057e841a: [{'confidence': 3, 'response': 7}]
  1 result(s) for checksum f007bf86: [{'confidence': 2, 'response': 9}]
  5 result(s) for checksum 00000000: [{'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}, {'confidence': 0, 'response': 12}, {'confidence': 0, 'response': 13}, {'confidence': 0, 'response': 14}]
Track 2:
  1 result(s) for checksum e5c8bee2: [{'confidence': 172, 'response': 1}]
  1 result(s) for checksum 118fb072: [{'confidence': 165, 'response': 2}]
  1 result(s) for checksum 65d8449a: [{'confidence': 87, 'response': 3}]
  1 result(s) for checksum f48209fb: [{'confidence': 13, 'response': 4}]
  1 result(s) for checksum 322a6912: [{'confidence': 11, 'response': 5}]
  1 result(s) for checksum 52304fdb: [{'confidence': 5, 'response': 6}]
  1 result(s) for checksum a0db5ab2: [{'confidence': 3, 'response': 8}]
  1 result(s) for checksum 0b4b0e9a: [{'confidence': 3, 'response': 7}]
  6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}, {'confidence': 0, 'response': 12}, {'confidence': 0, 'response': 13}, {'confidence': 0, 'response': 14}]
Track 3:
  1 result(s) for checksum c19cd97c: [{'confidence': 165, 'response': 1}]
  1 result(s) for checksum 15582f3c: [{'confidence': 165, 'response': 2}]
  1 result(s) for checksum 46b5741c: [{'confidence': 85, 'response': 3}]
  1 result(s) for checksum 06ee9f29: [{'confidence': 13, 'response': 4}]
  1 result(s) for checksum 5d52c374: [{'confidence': 10, 'response': 5}]
  1 result(s) for checksum 8b5383f9: [{'confidence': 5, 'response': 6}]
  1 result(s) for checksum 9b831c1c: [{'confidence': 3, 'response': 7}]
  1 result(s) for checksum 7688127c: [{'confidence': 3, 'response': 8}]
  6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}, {'confidence': 0, 'response': 12}, {'confidence': 0, 'response': 13}, {'confidence': 0, 'response': 14}]
Track 4:
  1 result(s) for checksum df6eb300: [{'confidence': 172, 'response': 1}]
  1 result(s) for checksum 08070666: [{'confidence': 161, 'response': 2}]
  1 result(s) for checksum 1bc8c93d: [{'confidence': 86, 'response': 3}]
  1 result(s) for checksum 13b12ee3: [{'confidence': 13, 'response': 4}]
  1 result(s) for checksum 9886ed9b: [{'confidence': 11, 'response': 5}]
  1 result(s) for checksum 59972245: [{'confidence': 5, 'response': 6}]
  1 result(s) for checksum db3766fd: [{'confidence': 3, 'response': 7}]
  1 result(s) for checksum 5a21fd7e: [{'confidence': 3, 'response': 8}]
  6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}, {'confidence': 0, 'response': 12}, {'confidence': 0, 'response': 13}, {'confidence': 0, 'response': 14}]
Track 5:
  1 result(s) for checksum efbccd2a: [{'confidence': 170, 'response': 1}]
  1 result(s) for checksum 8d27b69c: [{'confidence': 157, 'response': 2}]
  1 result(s) for checksum a8d07a41: [{'confidence': 85, 'response': 3}]
  1 result(s) for checksum 14f6ab18: [{'confidence': 13, 'response': 4}]
  1 result(s) for checksum e0da966c: [{'confidence': 10, 'response': 5}]
  1 result(s) for checksum efa94b4b: [{'confidence': 5, 'response': 6}]
  1 result(s) for checksum e30228e4: [{'confidence': 4, 'response': 7}]
  1 result(s) for checksum 19ce1b81: [{'confidence': 3, 'response': 8}]
  6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}, {'confidence': 0, 'response': 12}, {'confidence': 0, 'response': 13}, {'confidence': 0, 'response': 14}]
Track 6:
  1 result(s) for checksum 80d08043: [{'confidence': 167, 'response': 1}]
  1 result(s) for checksum a54b5c85: [{'confidence': 164, 'response': 2}]
  1 result(s) for checksum e5686472: [{'confidence': 87, 'response': 3}]
  1 result(s) for checksum ccb07a0f: [{'confidence': 13, 'response': 4}]
  1 result(s) for checksum eaf0a935: [{'confidence': 10, 'response': 5}]
  1 result(s) for checksum e9e2c332: [{'confidence': 5, 'response': 6}]
  1 result(s) for checksum 3ecfd7b2: [{'confidence': 3, 'response': 7}]
  1 result(s) for checksum 39cbce0d: [{'confidence': 3, 'response': 8}]
  6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}, {'confidence': 0, 'response': 12}, {'confidence': 0, 'response': 13}, {'confidence': 0, 'response': 14}]
Track 7:
  1 result(s) for checksum 51229cf0: [{'confidence': 163, 'response': 1}]
  1 result(s) for checksum 27c6fff2: [{'confidence': 161, 'response': 2}]
  1 result(s) for checksum 1711cbbf: [{'confidence': 87, 'response': 3}]
  1 result(s) for checksum 750c7dfc: [{'confidence': 12, 'response': 4}]
  1 result(s) for checksum 996f58fc: [{'confidence': 11, 'response': 5}]
  1 result(s) for checksum ca38d199: [{'confidence': 5, 'response': 6}]
  1 result(s) for checksum 0b193c7a: [{'confidence': 3, 'response': 7}]
  1 result(s) for checksum 73bf36ff: [{'confidence': 2, 'response': 8}]
  6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}, {'confidence': 0, 'response': 12}, {'confidence': 0, 'response': 13}, {'confidence': 0, 'response': 14}]
Track 8:
  1 result(s) for checksum 4788db96: [{'confidence': 172, 'response': 1}]
  1 result(s) for checksum adafbf40: [{'confidence': 165, 'response': 2}]
  1 result(s) for checksum 41cb0451: [{'confidence': 86, 'response': 3}]
  1 result(s) for checksum 3377659a: [{'confidence': 13, 'response': 4}]
  1 result(s) for checksum 5cc58197: [{'confidence': 10, 'response': 5}]
  1 result(s) for checksum fc80e9b2: [{'confidence': 5, 'response': 6}]
  1 result(s) for checksum 02a6e868: [{'confidence': 3, 'response': 7}]
  1 result(s) for checksum ddb62091: [{'confidence': 2, 'response': 8}]
  6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}, {'confidence': 0, 'response': 12}, {'confidence': 0, 'response': 13}, {'confidence': 0, 'response': 14}]
Track 9:
  1 result(s) for checksum f40d6560: [{'confidence': 174, 'response': 1}]
  1 result(s) for checksum fec5950c: [{'confidence': 162, 'response': 2}]
  1 result(s) for checksum 7260b06a: [{'confidence': 86, 'response': 3}]
  1 result(s) for checksum 5135a8cd: [{'confidence': 13, 'response': 4}]
  1 result(s) for checksum 751e14ff: [{'confidence': 10, 'response': 5}]
  1 result(s) for checksum 8ae19162: [{'confidence': 5, 'response': 6}]
  1 result(s) for checksum 99d7d7ea: [{'confidence': 3, 'response': 7}]
  1 result(s) for checksum e2b4debc: [{'confidence': 2, 'response': 8}]
  6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}, {'confidence': 0, 'response': 12}, {'confidence': 0, 'response': 13}, {'confidence': 0, 'response': 14}]
Track 10:
  1 result(s) for checksum 33bd6638: [{'confidence': 168, 'response': 1}]
  1 result(s) for checksum 221eaaf4: [{'confidence': 159, 'response': 2}]
  1 result(s) for checksum 765ab13a: [{'confidence': 80, 'response': 3}]
  1 result(s) for checksum 2052828a: [{'confidence': 13, 'response': 4}]
  1 result(s) for checksum ecb4029c: [{'confidence': 12, 'response': 5}]
  1 result(s) for checksum 90dad7aa: [{'confidence': 5, 'response': 6}]
  1 result(s) for checksum 2c4f0ce4: [{'confidence': 3, 'response': 7}]
  1 result(s) for checksum d42c52ba: [{'confidence': 2, 'response': 8}]
  6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}, {'confidence': 0, 'response': 12}, {'confidence': 0, 'response': 13}, {'confidence': 0, 'response': 14}]
Track 11:
  1 result(s) for checksum b42f500a: [{'confidence': 172, 'response': 1}]
  1 result(s) for checksum 27a636bc: [{'confidence': 159, 'response': 2}]
  1 result(s) for checksum f9fd9081: [{'confidence': 84, 'response': 3}]
  1 result(s) for checksum 9fe81541: [{'confidence': 13, 'response': 4}]
  1 result(s) for checksum 87a34c2e: [{'confidence': 12, 'response': 5}]
  1 result(s) for checksum 9072e479: [{'confidence': 5, 'response': 6}]
  1 result(s) for checksum 30c5ee04: [{'confidence': 3, 'response': 7}]
  1 result(s) for checksum 75bbb9c1: [{'confidence': 2, 'response': 8}]
  6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}, {'confidence': 0, 'response': 12}, {'confidence': 0, 'response': 13}, {'confidence': 0, 'response': 14}]
Track 12:
  1 result(s) for checksum 513c2183: [{'confidence': 167, 'response': 1}]
  1 result(s) for checksum b948c50d: [{'confidence': 163, 'response': 2}]
  1 result(s) for checksum de12e54e: [{'confidence': 83, 'response': 3}]
  1 result(s) for checksum 912c3066: [{'confidence': 12, 'response': 4}]
  1 result(s) for checksum 0648b84d: [{'confidence': 11, 'response': 5}]
  1 result(s) for checksum 43dc0fc1: [{'confidence': 5, 'response': 6}]
  1 result(s) for checksum 3547e5b5: [{'confidence': 3, 'response': 7}]
  1 result(s) for checksum 224d4d8e: [{'confidence': 2, 'response': 8}]
  6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}, {'confidence': 0, 'response': 12}, {'confidence': 0, 'response': 13}, {'confidence': 0, 'response': 14}]
Track 13:
  1 result(s) for checksum 45f36bd0: [{'confidence': 156, 'response': 1}]
  1 result(s) for checksum 2a4a3bdc: [{'confidence': 155, 'response': 2}]
  1 result(s) for checksum 8d61b202: [{'confidence': 79, 'response': 3}]
  1 result(s) for checksum f64391a2: [{'confidence': 12, 'response': 4}]
  1 result(s) for checksum 75c6d31f: [{'confidence': 10, 'response': 5}]
  1 result(s) for checksum eb9d076a: [{'confidence': 5, 'response': 6}]
  1 result(s) for checksum 04e488a0: [{'confidence': 3, 'response': 7}]
  1 result(s) for checksum 458c6e82: [{'confidence': 2, 'response': 8}]
  6 result(s) for checksum 00000000: [{'confidence': 0, 'response': 9}, {'confidence': 0, 'response': 10}, {'confidence': 0, 'response': 11}, {'confidence': 0, 'response': 12}, {'confidence': 0, 'response': 13}, {'confidence': 0, 'response': 14}]
Track 14:
  1 result(s) for checksum a41f38d7: [{'confidence': 146, 'response': 1}]
  1 result(s) for checksum 42dfd237: [{'confidence': 131, 'response': 2}]
  1 result(s) for checksum 0e38a7e7: [{'confidence': 63, 'response': 3}]
  1 result(s) for checksum 679fd237: [{'confidence': 24, 'response': 4}]
  1 result(s) for checksum 32f8a7e7: [{'confidence': 16, 'response': 5}]
  1 result(s) for checksum 8b0b3f5b: [{'confidence': 12, 'response': 6}]
  1 result(s) for checksum 2710f8e4: [{'confidence': 11, 'response': 7}]
  1 result(s) for checksum a7f838d7: [{'confidence': 9, 'response': 8}]
  1 result(s) for checksum a7cddc52: [{'confidence': 5, 'response': 9}]
  1 result(s) for checksum f2c1d237: [{'confidence': 3, 'response': 10}]
  1 result(s) for checksum 99c17b57: [{'confidence': 3, 'response': 11}]
  1 result(s) for checksum f081ebe7: [{'confidence': 2, 'response': 13}]
  1 result(s) for checksum 8c5fd237: [{'confidence': 2, 'response': 14}]
  1 result(s) for checksum 57b8a7e7: [{'confidence': 2, 'response': 12}]

[http://thomasvs.myopenid.com/]

And for my original Arctic Monkeys disc:

 rip accurip show http://www.accuraterip.com/accuraterip/d/d/3/dBAR-012-000fc3dd-00956f1a-8608d20c.bin
Found 5 responses for 12 tracks

Track 1:
  1 result(s) for checksum 5c0c1079: [{'confidence': 200, 'response': 1}]
  1 result(s) for checksum 121328b9: [{'confidence': 45, 'response': 2}]
  1 result(s) for checksum a923d12d: [{'confidence': 11, 'response': 3}]
  1 result(s) for checksum 1ea20a0e: [{'confidence': 2, 'response': 4}]
  1 result(s) for checksum 00000000: [{'confidence': 0, 'response': 5}]
Track 2:
  1 result(s) for checksum 08585f2f: [{'confidence': 200, 'response': 1}]
  1 result(s) for checksum 3ab71e73: [{'confidence': 45, 'response': 2}]
  1 result(s) for checksum 5c9050a3: [{'confidence': 12, 'response': 3}]
  1 result(s) for checksum a15a7374: [{'confidence': 2, 'response': 4}]
  1 result(s) for checksum 00000000: [{'confidence': 0, 'response': 5}]
Track 3:
  1 result(s) for checksum c3dd341d: [{'confidence': 133, 'response': 1}]
  1 result(s) for checksum c8d29083: [{'confidence': 98, 'response': 2}]
  1 result(s) for checksum be6c463e: [{'confidence': 45, 'response': 3}]
  1 result(s) for checksum d1a34d65: [{'confidence': 12, 'response': 4}]
  1 result(s) for checksum b50a5703: [{'confidence': 2, 'response': 5}]
Track 4:
  1 result(s) for checksum 4a1489d3: [{'confidence': 200, 'response': 1}]
  1 result(s) for checksum 62f5569f: [{'confidence': 45, 'response': 2}]
  1 result(s) for checksum fcccba0b: [{'confidence': 12, 'response': 3}]
  1 result(s) for checksum e6a3c30d: [{'confidence': 2, 'response': 4}]
  1 result(s) for checksum 00000000: [{'confidence': 0, 'response': 5}]
Track 5:
  1 result(s) for checksum 861de9f3: [{'confidence': 133, 'response': 1}]
  1 result(s) for checksum 8b8faee0: [{'confidence': 98, 'response': 2}]
  1 result(s) for checksum 03342e39: [{'confidence': 46, 'response': 3}]
  1 result(s) for checksum b500763b: [{'confidence': 12, 'response': 4}]
  1 result(s) for checksum cea1ed19: [{'confidence': 2, 'response': 5}]
Track 6:
  1 result(s) for checksum 7af1e3bb: [{'confidence': 133, 'response': 1}]
  1 result(s) for checksum 4d962431: [{'confidence': 97, 'response': 2}]
  1 result(s) for checksum 2f01ac50: [{'confidence': 45, 'response': 3}]
  1 result(s) for checksum 3c85e74b: [{'confidence': 12, 'response': 4}]
  1 result(s) for checksum d8fb3447: [{'confidence': 2, 'response': 5}]
Track 7:
  1 result(s) for checksum 9e826702: [{'confidence': 133, 'response': 1}]
  1 result(s) for checksum 986518e4: [{'confidence': 96, 'response': 2}]
  1 result(s) for checksum 95b6e388: [{'confidence': 45, 'response': 3}]
  1 result(s) for checksum 33b1104a: [{'confidence': 12, 'response': 4}]
  1 result(s) for checksum f5d1d5e8: [{'confidence': 2, 'response': 5}]
Track 8:
  1 result(s) for checksum 41911001: [{'confidence': 133, 'response': 1}]
  1 result(s) for checksum 5dd97a95: [{'confidence': 98, 'response': 2}]
  1 result(s) for checksum a1f126e6: [{'confidence': 45, 'response': 3}]
  1 result(s) for checksum e2a8e0f3: [{'confidence': 12, 'response': 4}]
  1 result(s) for checksum 7a1cde16: [{'confidence': 2, 'response': 5}]
Track 9:
  1 result(s) for checksum 1e37015c: [{'confidence': 132, 'response': 1}]
  1 result(s) for checksum eef042e8: [{'confidence': 98, 'response': 2}]
  1 result(s) for checksum fa032f3a: [{'confidence': 45, 'response': 3}]
  1 result(s) for checksum 028b2dfa: [{'confidence': 12, 'response': 4}]
  1 result(s) for checksum 35499037: [{'confidence': 2, 'response': 5}]
Track 10:
  1 result(s) for checksum 47e37626: [{'confidence': 200, 'response': 1}]
  1 result(s) for checksum cab7cd55: [{'confidence': 45, 'response': 2}]
  1 result(s) for checksum 0307229c: [{'confidence': 12, 'response': 3}]
  1 result(s) for checksum e512082b: [{'confidence': 2, 'response': 4}]
  1 result(s) for checksum 00000000: [{'confidence': 0, 'response': 5}]
Track 11:
  1 result(s) for checksum 1eab3e46: [{'confidence': 132, 'response': 1}]
  1 result(s) for checksum 1a4a6c91: [{'confidence': 97, 'response': 2}]
  1 result(s) for checksum 46a9f3b8: [{'confidence': 45, 'response': 3}]
  1 result(s) for checksum 1dc2d838: [{'confidence': 12, 'response': 4}]
  1 result(s) for checksum 8dd81b65: [{'confidence': 2, 'response': 5}]
Track 12:
  1 result(s) for checksum a991bd4e: [{'confidence': 132, 'response': 1}]
  1 result(s) for checksum 8183052a: [{'confidence': 99, 'response': 2}]
  1 result(s) for checksum 19347543: [{'confidence': 45, 'response': 3}]
  1 result(s) for checksum efd46c36: [{'confidence': 12, 'response': 4}]
  1 result(s) for checksum cac5172c: [{'confidence': 2, 'response': 5}]

[http://lool.myopenid.com/]

Oh wow, I never imaginated that the sets would come from different rips; now the results completely make sense: I would see my checksums match the ones from multiple "responses".

So it seems the requirement (assert) that all tracks' checksums come from the same response needs not be, each track's checksum should be matched starting with the first response and going down in confidence level. Not too reassuring though.

Handling mulitple numbers of tracks for the same db id > I don't remember whether I actually hit that case or whether I decided I didn't know whether it was possible or not, so going the least complex route makes sense; thanks for the new debug command!

[thomas]

In [464]:

• morituri/common/program.py: AccurateRip? responses should be treated per track; one response set isn't necessarily from the same pressing. The first response set contains all the highest confidence checksums, and so on in decreasing order. So don't assert about one track having a checksum matching from a different response set. Fixes #5.