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tremblapPierre Alexandre Tremblay
tremblap
and
Pierre Alexandre Tremblay
authored
Better legibility across themes
* colour set to dynamic in some cases * defaulted the colours of the patcher folder * fix the default colour in extra * example folder - default * help files corrected --------- Co-authored-by: Pierre Alexandre Tremblay <[email protected]>
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105 files changed

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-2022
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‎examples/analysing-pitch.maxpat‎

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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 14.0, 43.0, 202.0, 20.0 ],
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"text" : "Refining results by using... statistics!",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "Refining results by using... statistics!"
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}
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}
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 137.0, 402.0, 99.0, 60.0 ],
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"text" : "With the outliers stripped, we see a much tighter range...",
1905-
"textcolor" : [ 0.129411764705882, 0.129411764705882, 0.129411764705882, 0.53 ]
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"text" : "With the outliers stripped, we see a much tighter range..."
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}
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}
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 471.833316500000024, 395.166625999999951, 97.0, 74.0 ],
1917-
"text" : "The weighted stats have the same range but much less variance",
1918-
"textcolor" : [ 0.129411764705882, 0.129411764705882, 0.129411764705882, 0.53 ]
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"text" : "The weighted stats have the same range but much less variance"
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}
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}
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19271924
"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 807.0, 395.166625999999951, 97.0, 74.0 ],
1930-
"text" : "The unweighted stats have a huge range (145-6.7kHz) and variance",
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"textcolor" : [ 0.129411764705882, 0.129411764705882, 0.129411764705882, 0.53 ]
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"text" : "The unweighted stats have a huge range (145-6.7kHz) and variance"
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}
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}
@@ -1940,8 +1936,7 @@
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 1019.0, 474.0, 259.333313000000089, 47.0 ],
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"text" : "We can see clear bits in the pitch confidence where it's higher / lower: we're hoping this can be used to get more beleiveable statistics",
1944-
"textcolor" : [ 0.129411764705882, 0.129411764705882, 0.129411764705882, 0.47 ]
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"text" : "We can see clear bits in the pitch confidence where it's higher / lower: we're hoping this can be used to get more beleiveable statistics"
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}
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}

‎examples/audioreactive.maxpat‎

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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 386.0, 145.0, 150.0, 87.0 ],
117-
"text" : "Modify the threshold to your taste. Lower numbers are more sensitive and higher numbers are less sensitive.",
118-
"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "Modify the threshold to your taste. Lower numbers are more sensitive and higher numbers are less sensitive."
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}
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}
@@ -127,8 +126,7 @@
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 762.000003427267075, 347.5, 124.0, 87.0 ],
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"text" : "Measuring the \"novelty\" of the sound. Things that are \"newer\", i.e more novel increase the distortion.",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "Measuring the \"novelty\" of the sound. Things that are \"newer\", i.e more novel increase the distortion."
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}
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}
@@ -140,8 +138,7 @@
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 301.000003427267075, 362.833337247371674, 123.0, 33.0 ],
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"text" : "Time between consecutive bangs",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "Time between consecutive bangs"
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}
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}
@@ -152,8 +149,7 @@
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 73.0, 400.333337247371674, 123.0, 20.0 ],
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"text" : "Bang on every trigger",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "Bang on every trigger"
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}
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}
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 376.5, 13.0, 440.0, 100.0 ],
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"text" : "This patch shows you could very quickly start integrating FluComa objects into a patch that makes sound. This patch leverages fluid.onsetslice~ to detect when changes in the input sound. This creates a trigger which generates sound by passing the signal-rate click into a resonant filter. That signal is converted into a bang event using edge~ which also modulates the pitch. The fluid.noveltyfeature~ object estimates how \"new\" the signal is, which is then mapped onto the distortion amount.",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "This patch shows you could very quickly start integrating FluComa objects into a patch that makes sound. This patch leverages fluid.onsetslice~ to detect when changes in the input sound. This creates a trigger which generates sound by passing the signal-rate click into a resonant filter. That signal is converted into a bang event using edge~ which also modulates the pitch. The fluid.noveltyfeature~ object estimates how \"new\" the signal is, which is then mapped onto the distortion amount."
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}
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}
@@ -289,8 +284,7 @@
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 16.0, 45.0, 299.0, 20.0 ],
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"text" : "Using a segmentation algorithm to trigger audio events",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "Using a segmentation algorithm to trigger audio events"
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}
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}

‎examples/autoencoder.maxpat‎

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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 714.0, 373.0, 323.0, 87.0 ],
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"text" : "This space represents the values of each neuron in the central hidden layer of the neural network.\n\nIt has, to some extent, learned a lower-dimensional representation of the input-norm data space by figuring out how it can reconstruct it with only 2 dimensions.",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "This space represents the values of each neuron in the central hidden layer of the neural network.\n\nIt has, to some extent, learned a lower-dimensional representation of the input-norm data space by figuring out how it can reconstruct it with only 2 dimensions."
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}
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}
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 151.0, 399.762878000000001, 328.5, 33.0 ],
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"text" : "Keep pressing stage (4) until the fit number seems to stop moving. A good value is around 0.20 in this example",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "Keep pressing stage (4) until the fit number seems to stop moving. A good value is around 0.20 in this example"
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}
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}
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 297.5, 20.0, 699.0, 60.0 ],
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"text" : "Without getting too deep into the weeds a neural network learns the relationship between input and output data. If we ask it to learn the relationship between the same data as the input and output it essentially learns how it 'reconstruct' the data using a smaller number of dimensions (the neurons in the hidden layers). This means we can use the fluid.mlpregressor~ as a form of dimension reduction!",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "Without getting too deep into the weeds a neural network learns the relationship between input and output data. If we ask it to learn the relationship between the same data as the input and output it essentially learns how it 'reconstruct' the data using a smaller number of dimensions (the neurons in the hidden layers). This means we can use the fluid.mlpregressor~ as a form of dimension reduction!"
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}
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}
@@ -224,8 +221,7 @@
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 13.0, 48.0, 269.0, 20.0 ],
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"text" : "Neural networks learning how to reconstruct data",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "Neural networks learning how to reconstruct data"
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}
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}
@@ -612,8 +608,7 @@
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 70.5, 205.5, 286.0, 33.0 ],
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"text" : "this is the hop size of the analysis, to convert the position of the lookup to a position in the audio file",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "this is the hop size of the analysis, to convert the position of the lookup to a position in the audio file"
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}
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}
@@ -870,8 +865,7 @@
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 834.0, 651.0, 190.0, 20.0 ],
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"text" : "predicting from the neural network",
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"textcolor" : [ 1.0, 1.0, 1.0, 1.0 ]
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"text" : "predicting from the neural network"
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}
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}
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 540.0, 651.0, 55.0, 20.0 ],
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"text" : "raw data",
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"textcolor" : [ 1.0, 1.0, 1.0, 1.0 ]
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"text" : "raw data"
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}
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}

‎examples/bufcompose-example.maxpat‎

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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 89.0, 102.0, 382.0, 20.0 ],
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"text" : "If you need to reset the state of the buffers~ double click this loadbang",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "If you need to reset the state of the buffers~ double click this loadbang"
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}
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}
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 196.0, 406.0, 382.0, 33.0 ],
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"text" : "All of the \"macros\" are created purely in Max around the fluid.bufcompose~ object",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "All of the \"macros\" are created purely in Max around the fluid.bufcompose~ object"
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}
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 27.0, 47.0, 299.0, 20.0 ],
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"text" : "Advanced usage of fluid.bufcompose~ with \"macros\"",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "Advanced usage of fluid.bufcompose~ with \"macros\""
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}

‎examples/buffermanagement-example.maxpat‎

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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 575.0, 266.0, 281.0, 50.0 ],
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"text" : "At the end here we \"refer\" an unnamed fluid.dataset~ to the output of fluid.normalize~ in order to dump its contents.",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "At the end here we \"refer\" an unnamed fluid.dataset~ to the output of fluid.normalize~ in order to dump its contents."
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 10.0, 47.0, 314.0, 21.0 ],
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"text" : "Getting to grip with the basics of fluid.dataset~ usage",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "Getting to grip with the basics of fluid.dataset~ usage"
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 10.0, 47.0, 206.0, 21.0 ],
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"text" : "Caveats and things to be aware of",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "Caveats and things to be aware of"
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 10.0, 47.0, 179.0, 21.0 ],
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"text" : "Automatic output buffers~",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "Automatic output buffers~"
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 10.0, 47.0, 314.0, 21.0 ],
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"text" : "Getting to grip with the basics of fluid.dataset~ usage",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "Getting to grip with the basics of fluid.dataset~ usage"
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}
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}
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 10.0, 47.0, 210.0, 21.0 ],
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"text" : "The basics of buffer~ management",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "The basics of buffer~ management"
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}
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}

‎examples/intelligent-slicing/intelligent-slicing-linear-regression.maxpat‎

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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 333.599852000000169, 54.0, 273.0, 127.0 ],
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"presentation_linecount" : 9,
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"text" : "Sometimes instead of thinking about what parameters we'd like to set for a segmentation algorithm we'd rather dictate how many slices we'd like to find.\n\nIn this example we impose a feedback process on the slicing, so that a threshold can be found which satisfies a number of slices within a +/- range.",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "Sometimes instead of thinking about what parameters we'd like to set for a segmentation algorithm we'd rather dictate how many slices we'd like to find.\n\nIn this example we impose a feedback process on the slicing, so that a threshold can be found which satisfies a number of slices within a +/- range."
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}
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}
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 36.5, 54.0, 273.0, 20.0 ],
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"text" : "Discover threshold required for a number of slices",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "Discover threshold required for a number of slices"
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}
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}
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 62.300048999999944, 400.799987999999985, 60.0, 20.0 ],
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"text" : "threshold",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "threshold"
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}
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}
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 175.40002400000003, 610.000030000000038, 128.0, 20.0 ],
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"text" : "final number of slices",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "final number of slices"
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}
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}

‎examples/nmf/JIT-NMF-Classifier.maxpat‎

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426426
"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 175.792554438114166, 749.65609073638916, 217.0, 29.0 ],
429-
"text" : "checks the first sample of activations to decide which drum(s) were played",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "checks the first sample of activations to decide which drum(s) were played"
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}
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}
@@ -3818,8 +3817,7 @@
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 281.996204376220703, 338.618428826332092, 204.0, 18.0 ],
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"text" : "detecting attacks to trigger the classification",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "detecting attacks to trigger the classification"
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}
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}
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 442.096143126487732, 675.271475672721863, 117.0, 40.0 ],
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"text" : "factorise the input in the best combination of the 3 defined components",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "factorise the input in the best combination of the 3 defined components"
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}
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}
@@ -3846,8 +3843,7 @@
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 854.290943086147308, 499.518941271850508, 223.0, 29.0 ],
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"text" : "trains a 1 component nmf, updating the filter each time, 'improving' the definition of the class",
3850-
"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "trains a 1 component nmf, updating the filter each time, 'improving' the definition of the class"
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}
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}
@@ -3860,8 +3856,7 @@
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 442.096143126487732, 586.519464731216431, 107.0, 51.0 ],
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"text" : "process 128 samples after the detected attack \n(super short!)",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "process 128 samples after the detected attack \n(super short!)"
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}
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}
@@ -3874,8 +3869,7 @@
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"numinlets" : 1,
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"numoutlets" : 0,
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"patching_rect" : [ 582.648085474967957, 367.677168953964156, 90.0, 62.0 ],
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"text" : "filling the dict with DC as a starting point. positive noise would do a similar job",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "filling the dict with DC as a starting point. positive noise would do a similar job"
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}
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}

‎examples/nmf/JIT-NMF-Header.maxpat‎

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"presentation" : 1,
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"presentation_linecount" : 3,
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"presentation_rect" : [ 18.97886073589325, 160.070424437522888, 639.0, 60.0 ],
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"text" : "These example patches display creative uses of nmf processing, each with increasing complexity.\n\nIf you aren't familiar with the nmf objects, check out their help files before diving deeper!",
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"textcolor" : [ 0.0, 0.0, 0.0, 1.0 ]
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"text" : "These example patches display creative uses of nmf processing, each with increasing complexity.\n\nIf you aren't familiar with the nmf objects, check out their help files before diving deeper!"
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}
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}
@@ -619,8 +618,7 @@
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"patching_rect" : [ 65.83346962928772, 271.166659116744995, 139.0, 21.0 ],
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"presentation" : 1,
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"presentation_rect" : [ 18.97886073589325, 231.070424437522888, 139.0, 21.0 ],
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"text" : "Select an example...",
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"textcolor" : [ 0.501960784313725, 0.501960784313725, 0.501960784313725, 1.0 ]
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"text" : "Select an example..."
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}
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}

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