2018:Patterns for Prediction - Revision history

Tom Collins: /* Submission Format */

2018-07-31T13:51:38Z

‎Submission Format

Tom Collins: /* Submission Format */

2018-07-31T13:42:54Z

‎Submission Format

Tom Collins: /* Submission Format */

2018-07-31T13:39:10Z

‎Submission Format

Tom Collins: /* Questions (Q), Answers (A), and Comments (C) */

2018-07-31T13:36:46Z

‎Questions (Q), Answers (A), and Comments (C)

Tom Collins: /* Questions (Q), Answers (A), and Comments (C) */

2018-07-31T13:36:18Z

‎Questions (Q), Answers (A), and Comments (C)

Tom Collins: /* Questions (Q), Answers (A), and Comments (C) */

2018-07-31T13:31:55Z

‎Questions (Q), Answers (A), and Comments (C)

Tom Collins: /* Questions (Q), Answers (A), and Comments (C) */

2018-07-31T13:31:28Z

‎Questions (Q), Answers (A), and Comments (C)

Tom Collins: /* Questions (Q), Answers (A), and Comments (C) */

2018-07-31T13:30:46Z

‎Questions (Q), Answers (A), and Comments (C)

Tom Collins: /* Data */

2018-07-31T13:15:06Z

‎Data

Tom Collins: /* Questions (Q), Answers (A), and Comments (C) */

2018-07-30T19:38:50Z

‎Questions (Q), Answers (A), and Comments (C)

@@ Line 66: / Line 66: @@
 ==Submission Format==
-In terms of input representations, we will evaluate 4 largely independent versions of the task: audio, monophonic; audio, polyphonic; symbolic, monophonic; symbolic, polyphonic. Participants may submit algorithms to 1 or more of these versions, and should list these versions clearly in their readme. '''Irrespective of input representation''', all output for subtask (1) should be in "ontime", "MNN" CSV files. The CSV may contain other information, but "ontime" and "MNN" should be in the first two columns, respectively. All output for subtask (2) should be an indication whether of the two presented continuations, "1" or "2" is judged by the algorithm to be genuine. This should be one CSV file for an entire dataset, with first column "id" referring to the file name of a prime-continuation pair, second column "1" containing a likelihood value in [0, 1] for the genuineness of the continuation in folder 1, and column "2" similarly for the continuation in folder 2.
+In terms of input representations, we will evaluate 4 largely independent versions of the task: audio, monophonic; audio, polyphonic; symbolic, monophonic; symbolic, polyphonic. Participants may submit algorithms to 1 or more of these versions, and should list these versions clearly in their readme. '''Irrespective of input representation''', all output for subtask (1) should be in "ontime", "MNN" CSV files. The CSV may contain other information, but "ontime" and "MNN" should be in the first two columns, respectively. All output for subtask (2) should be an indication whether of the two presented continuations, "A" or "B" is judged by the algorithm to be genuine. This should be one CSV file for an entire dataset, with first column "id" referring to the file name of a prime-continuation pair, second column "A" containing a likelihood value in [0, 1] for the genuineness of the continuation in folder A, and column "B" similarly for the continuation in folder B.
 All submissions should be statically linked to all dependencies and include a README file including the following information:

@@ Line 70: / Line 70: @@
 All submissions should be statically linked to all dependencies and include a README file including the following information:
 *command line calling format for all executables and an example formatted set of commands;
 *number of threads/cores used or whether this should be specified on the command line;

@@ Line 66: / Line 66: @@
 ==Submission Format==
 All submissions should be statically linked to all dependencies and include a README file including the following information:
 *command line calling format for all executables and an example formatted set of commands;
 *number of threads/cores used or whether this should be specified on the command line;
 *expected memory footprint;

@@ Line 131: / Line 131: @@
 Q. I'm interested in performing this task on the symbolic dataset, but I don't have an audio-based algorithm. It was unclear to me if the inputs are audio, symbolic, both, or either.
-A. We have clarified, at the top of [[2018:Patterns_for_Prediction#Submission_Format]], that submissions in 1-4 representational categories are acceptable. It's also OK, say, for an audio-based algorithm to make use of the descriptor file in order to determine beat locations. (You could do this by looking at the <math>u = bpm</math> value, and then you would know that the main beats in the WAV file are at <math>0, 60/u, 2 \cdot 60/u,\ldots</math> sec.)
+A. We have clarified, at the top of [[2018:Patterns_for_Prediction#Submission_Format]], that submissions in 1-4 representational categories are acceptable. It's also OK, say, for an audio-based algorithm to make use of the descriptor file in order to determine beat locations. (You could do this by looking at the <math>u = \mathrm{bpm}</math> value, and then you would know that the main beats in the WAV file are at <math>0, 60/u, 2 \cdot 60/u,\ldots</math> sec.)
 ==Time and Hardware Limits==

@@ Line 131: / Line 131: @@
 Q. I'm interested in performing this task on the symbolic dataset, but I don't have an audio-based algorithm. It was unclear to me if the inputs are audio, symbolic, both, or either.
-A. We have clarified, at the top of [[2018:Patterns_for_Prediction#Submission_Format]], that submissions in 1-4 representational categories are acceptable. It's also OK, say, for an audio-based algorithm to make use of the descriptor file in order to determine beat locations.
+A. We have clarified, at the top of [[2018:Patterns_for_Prediction#Submission_Format]], that submissions in 1-4 representational categories are acceptable. It's also OK, say, for an audio-based algorithm to make use of the descriptor file in order to determine beat locations. (You could do this by looking at the <math>u = bpm</math> value, and then you would know that the main beats in the WAV file are at <math>0, 60/u, 2 \cdot 60/u,\ldots</math> sec.)
 ==Time and Hardware Limits==

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   ...
-would be the start of a prime where the first event had ontime 20 (measured in quarter-note beats -- equivalent to bar 6 beat 1 if the time signature were 4-4), MIDI note number (MNN) 64, estimated morphetic pitch number 62 (see [http://tomcollinsresearch.net/research/data/mirex/ppdd/mnn_mpn.pdf p. 352] from Collins, 2011 for a diagrammatic explanation; for more details, see Meredith, 1999), duration 0.5 in quarter-note beats, and channel 0. Re-exports to MIDI are also provided, mainly for listening purposes. We also provide a descriptor file containing the original Lakh MIDI Dataset id, the BPM, time signature, and a key estimate. The audio dataset contains all these files, plus WAV files.
+would be the start of a prime where the first event had ontime 20 (measured in quarter-note beats -- equivalent to bar 6 beat 1 if the time signature were 4-4), MIDI note number (MNN) 64, estimated morphetic pitch number 62 (see [http://tomcollinsresearch.net/research/data/mirex/ppdd/mnn_mpn.pdf p. 352] from Collins, 2011 for a diagrammatic explanation; for more details, see Meredith, 1999), duration 0.5 in quarter-note beats, and channel 0. Re-exports to MIDI are also provided, mainly for listening purposes. We also provide a descriptor file containing the original Lakh MIDI Dataset id, the BPM, time signature, and a key estimate. The audio dataset contains all these files, plus WAV files. Therefore, the audio and symbolic variants are identical to one another, apart from the presence of WAV files. All other variants are non-identical, although there may be some overlap, as they were all chosen from LMD originally.
 The provenance of the Patterns for Prediction Test Dataset (PPTD) will '''not''' be disclosed, but it is not from LMD, if you are concerned about overfitting.

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 "Amanuensis constructs a cohesive song structure, using the best of what you give it, looping around you and growing in real-time as you play. All you have to do is jam and fully written songs will flow out behind you wherever you go.
-"My algorithm right now is only rhythm-based and I'm sure it's not sophisticated enough to be entered into your contest, but I would be very interested in the possibility of using any of the algorithms that are, in place of mine in The Amanuensis. I wonder if any of your participants would be interested in some collaboration? What I can bring to the table would be a real-world application for these algorithms, already set for implementation."
+"My algorithm right now is only rhythm-based and I'm sure it's not sophisticated enough to be entered into your contest, but I would be very interested in the possibility of using any of the algorithms that are, in place of mine in The Amanuensis. Would any of your participants be interested in some collaboration? What I can bring to the table would be a real-world application for these algorithms, already set for implementation."
 Q. I'm interested in performing this task on the symbolic dataset, but I don't have an audio-based algorithm. It was unclear to me if the inputs are audio, symbolic, both, or either.