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Feature: Stats has co-composition analysis #141

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merged 6 commits into from May 8, 2019

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commented May 2, 2019

Running the stats management command now produces a further co-composition specific analysis.

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commented May 2, 2019

Co-composition analysis

Refining the folkrnn.org session data above, we can analyze only those tunes which are in some way a tweak of the one that came before. This iterative process of human-directed tweaks of the machine-generated tunes, demonstrates co-composition using the folkrnn.org system. In numbers –
Of the 24657 tunes generated on folkrnn.org, 14088 keep the generation parameters from the previous tune, while changing one or more (57%).
This happened in 4007 'iterative' sequences, each averaging 6 generated tunes (mean: 5.9, stddev: 8.7)
The frequency of the generate parameters used now becomes:
key: 0.28, meter: 0.24, model: 0.093, seed locked: 0.34, start_abc is excerpt: 0.02, start_abc: 0.2, temp: 0.39

One feature now possible to expose is whether the user has identified a salient phrase in the prior tune, and has primed the generation of the new tune with this phrase. This is the strongest metric of co-composition available. This is reported above as 'start_abc is excerpt', tested for phrases comprising five characters or more (e.g. five notes, or fewer with phrasing), and as per other generation metrics reported here, not counting subsequent generations with that metric unchanged. This happened 283 times; 2%

Further evidence of human-machine co-composition can be seen on themachinefolksession.org, where 239 of the 'iterative' folkrnn.org tunes were archived. Using the tune saliency metric used by the machinefolksession.org homepage, the most noteworthy of these tunes is Green Electrodes. This was generated (//folkrnn.org/tune/5139) in the key C Dorian, and as archived (https://machinefolksession.org//tune/294) the user has manually added a variation set in the key E Dorian. This shows a limitation of folkrnn.org, that all tunes are generated in a variant of C (a consequence of an optimisation made while training the RNN on the corpus of existing tunes), and shows that the human editing features of themachinefolksession.org have been used by users to work around such a limitation. Also, while not co-composition per-se, that the machine generated tune has some value to the user is also shown by the act of them naming it.
Direct evidence of the user's intent can be seen in Rounding Derry (https://themachinefolksession.org/tune/587. The user generated tune FOLK RNN TUNE №24807 on a fresh load of folkrnn.org, i.e. default parameters, randomised seed. The user played this tune twice, and then selected the musical phrase 'C2EG ACEG|CGEG FDB,G,' and set this for the start_abc generation parameter. The user generated the next iteration, played it back, and this is what is seen archived on themachinefolksession.org. There, the user writes –
Generated from a pleasant 2 measure section of a random sequence, I liked this particularly because of the first 4 bars and then the jump to the 10th interval key center(?) in the second section. Also my first contribution!
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commented May 3, 2019

Co-composition analysis

Refining the folkrnn.org session data above, we can analyze only those tunes which are in some way a tweak of the one that came before. This iterative process of human-directed tweaks of the machine-generated tunes demonstrates co-composition using the folkrnn.org system. In numbers –
Of the 24657 tunes generated on folkrnn.org, 14088 keep the generation parameters from the previous tune while changing one or more (57%).
This happened within 4007 'iterative' sequences of, on average 6 tune generations (mean: 5.9, stddev: 8.7).
The frequency of the generate parameters used now becomes:
key: 0.28, meter: 0.24, model: 0.093, seed locked: 0.34, start_abc is excerpt: 0.02, start_abc: 0.2, temp: 0.39

One feature now possible to expose is whether the user has identified a salient phrase in the prior tune, and has primed the generation of the new tune with this phrase. This is the strongest metric of co-composition available on folkrnn.org. This is reported above as 'start_abc is excerpt', tested for phrases comprising five characters or more (e.g. five notes, or fewer with phrasing), and as per other generation metrics reported here, not counting subsequent generations with that metric unchanged. This happened 283 times (2%)

Further evidence of human-machine co-composition can be seen on themachinefolksession.org, where 239 of the 'iterative' folkrnn.org tunes were archived. Using the tune saliency metric used by the machinefolksession.org homepage, the most noteworthy of these tunes is 'Green Electrodes'. This was generated in the key C Dorian (//folkrnn.org/tune/5139), and as archived (https://machinefolksession.org/tune/294) the user has manually added a variation set in the key E Dorian. This shows a limitation of folkrnn.org, that all tunes are generated in a variant of C (a consequence of an optimisation made while training the RNN on the corpus of existing tunes), and shows that the human editing features of themachinefolksession.org have been used by users to work around such a limitation. Also, while not co-composition per-se, the act of the user naming the machine generated tune shows it has some value to them.

Direct evidence of the user's intent can be seen in 'Rounding Derry' (https://themachinefolksession.org/tune/587). The user generated tune 'FolkRNN Tune №24807' on a fresh load of folkrnn.org, i.e. default parameters, randomised seed. The user played this tune twice, and then selected the musical phrase 'C2EG ACEG|CGEG FDB,G,' and set this for the start_abc generation parameter. The user generated the next iteration, played it back, and archived on themachinefolksession.org with a title of their creation. There, the user writes –
'Generated from a pleasant 2 measure section of a random sequence, I liked this particularly because of the first 4 bars and then the jump to the 10th interval key center(?) in the second section. Also my first contribution!'
@tobyspark tobyspark merged commit 7713a09 into master May 8, 2019
@tobyspark tobyspark deleted the feature-stats-iterative-composition branch May 8, 2019
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