Example files corrections (#145)
* WIP: RT-alloc in RT clients (#129) * Update Realtime to pass RT allocator to clients * added link to examples folder in the guide * Wrapper: Allocatorize Part 1 * (Buf)MFCC.sc: Handle maxNumBands * (Buf)MFCC.sc: Handle maxNumBands (#130) * typo * Remove CondVar (#132) * removed from FluidWaveform * typo * Wrapper: allocatorize * Remove redundant old help files * Wrapper: Use `fmt` insetad of `std::to_chars` (STL function needs macOS >= 10.15) * CMake: Set PIC globally * ensure PIC for all libs * Readme: Correct C++ version Co-authored-by: Ted Moore <ted@tedmooremusic.com> * correction in example code of the new NN interface * fixed example: 'Neural Network Predicts FM Params from Audio Analysis' * Feature/peaks (#143) * working frankenstein freq only * removed all the unused arguments * now with mag out * now with the buffer version * change the interface to singular like other bufSTFT * added logFreq and linMag * change of interface (sortBy to order) * last SC commit - object overview added * corrected interface and simplified some examples Co-authored-by: Owen Green <gungwho@gmail.com> Co-authored-by: Ted Moore <ted@tedmooremusic.com>nix
tremblap
3 years ago
committed by
GitHub
parent
401cc00339
commit
b8c057fc1c
@ -0,0 +1,51 @@
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FluidBufSineFeature : FluidBufProcessor {
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*kr { |source, startFrame = 0, numFrames = -1, startChan = 0, numChans = -1, frequency = -1, magnitude = -1, numPeaks = 10, detectionThreshold = -96, order = 0, freqUnit = 0, magUnit = 0, windowSize = 1024, hopSize = -1, fftSize = -1, padding = 1, trig = 1, blocking = 0|
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var maxFFTSize = if (fftSize == -1) {windowSize.nextPowerOfTwo} {fftSize};
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source = source.asUGenInput;
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frequency = frequency !? {frequency.asUGenInput} ?? {-1};
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magnitude = magnitude !? {magnitude.asUGenInput} ?? {-1};
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source.isNil.if {"FluidBufSineFeature: Invalid source buffer".throw};
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^FluidProxyUgen.multiNew(\FluidBufSineFeatureTrigger, -1, source, startFrame, numFrames, startChan, numChans, frequency, magnitude, padding, numPeaks, numPeaks, detectionThreshold, order, freqUnit, magUnit, windowSize, hopSize, fftSize, maxFFTSize, trig, blocking);
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}
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*process { |server, source, startFrame = 0, numFrames = -1, startChan = 0, numChans = -1, frequency = -1, magnitude = -1, numPeaks = 10, detectionThreshold = -96, order = 0, freqUnit = 0, magUnit = 0, windowSize = 1024, hopSize = -1, fftSize = -1, padding = 1, freeWhenDone = true, action|
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var maxFFTSize = if (fftSize == -1) {windowSize.nextPowerOfTwo} {fftSize};
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source = source.asUGenInput;
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frequency = frequency !? {frequency.asUGenInput} ?? {-1};
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magnitude = magnitude !? {magnitude.asUGenInput} ?? {-1};
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source.isNil.if {"FluidBufSineFeature: Invalid source buffer".throw};
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^this.new(
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server, nil, [frequency, magnitude].select{|x| x!= -1}
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).processList(
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[source, startFrame, numFrames, startChan, numChans, frequency, magnitude, padding, numPeaks, numPeaks, detectionThreshold, order, freqUnit, magUnit, windowSize, hopSize, fftSize,maxFFTSize,0],freeWhenDone,action
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);
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}
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*processBlocking { |server, source, startFrame = 0, numFrames = -1, startChan = 0, numChans = -1, frequency = -1, magnitude = -1, numPeaks = 10, detectionThreshold = -96, order = 0, freqUnit = 0, magUnit = 0, windowSize = 1024, hopSize = -1, fftSize = -1, padding = 1, freeWhenDone = true, action|
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var maxFFTSize = if (fftSize == -1) {windowSize.nextPowerOfTwo} {fftSize};
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source = source.asUGenInput;
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frequency = frequency !? {frequency.asUGenInput} ?? {-1};
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magnitude = magnitude !? {magnitude.asUGenInput} ?? {-1};
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source.isNil.if {"FluidBufSineFeature: Invalid source buffer".throw};
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^this.new(
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server, nil, [frequency, magnitude].select{|x| x!= -1}
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).processList(
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[source, startFrame, numFrames, startChan, numChans, frequency, magnitude, padding, numPeaks, numPeaks, detectionThreshold, order, freqUnit, magUnit, windowSize, hopSize, fftSize,maxFFTSize,1],freeWhenDone,action
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);
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}
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}
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FluidBufSineFeatureTrigger : FluidProxyUgen {}
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@ -0,0 +1,37 @@
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FluidSineFeature : FluidRTMultiOutUGen {
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*kr { arg in = 0, numPeaks = 10, detectionThreshold = -96, order = 0, freqUnit = 0, magUnit = 0, windowSize= 1024, hopSize= -1, fftSize= -1, maxFFTSize = -1, maxNumPeaks = nil;
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maxNumPeaks = maxNumPeaks ? numPeaks;
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^this.multiNew('control', in.asAudioRateInput(this), numPeaks, maxNumPeaks, detectionThreshold, order, freqUnit, magUnit, windowSize, hopSize, fftSize, maxFFTSize)
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}
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init { arg ... theInputs;
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inputs = theInputs;
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^this.initOutputs(inputs.at(2),rate);//this instantiate the number of output from the maxNumPeaks in the multiNew order
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}
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checkInputs {
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if(inputs.at(8).rate != 'scalar') {
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^(": maxNumPeaks cannot be modulated.");
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};
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if(inputs.at(7).rate != 'scalar') {
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^(": maxFFTSize cannot be modulated.");
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};
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^this.checkValidInputs;
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}
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initOutputs{|numChans,rate|
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if(numChans.isNil or: {numChans < 1})
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{
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Error("No input channels").throw
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};
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channels = Array.fill(numChans * 2, { |i|
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OutputProxy('control',this,i);
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});
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^channels
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}
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numOutputs { ^(channels.size); }
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}
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@ -1,295 +0,0 @@
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/*
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this script shows how to
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1. load a folder of sounds
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2. find smaller time segments within the sounds according to novelty
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3. analyse the sounds according to MFCC and add these analyses to a dataset
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4. dimensionally reduce that dataset to 2D using umap
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5. (optional) turn the plot of points in 2D into a grid
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6. plot the points!
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notice that each step in this process is created within a function so that
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at the bottom of the patch, these functions are all chained together to
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do the whole process in one go!
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*/
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(
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// 1. load a folder of sounds
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~load_folder = {
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arg folder_path, action;
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var loader = FluidLoadFolder(folder_path); // pass in the folder to load
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loader.play(s,{ // play will do the actual loading
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var mono_buffer = Buffer.alloc(s,loader.buffer.numFrames);
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FluidBufCompose.processBlocking(s,loader.buffer,destination:mono_buffer,numChans:1,action:{
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action.(mono_buffer);
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});
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});
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};
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// this will load all the audio files that are included with the flucoma toolkit, but you can put your own path here:
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~load_folder.(FluidFilesPath(),{
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arg buffer;
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"mono buffer: %".format(buffer).postln;
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~buffer = buffer; // save the buffer to a global variable so we can use it later
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});
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)
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(
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// 2. slice the sounds
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~slice = {
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arg buffer, action;
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var indices = Buffer(s); // a buffer for saving the discovered indices into
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// play around the the threshold anad feature (see help file) to get differet slicing results
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FluidBufNoveltySlice.processBlocking(s,buffer,indices:indices,algorithm:0,threshold:0.5,action:{
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"% slices found".format(indices.numFrames).postln;
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"average duration in seconds: %".format(buffer.duration/indices.numFrames).postln;
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action.(buffer,indices);
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});
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};
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~slice.(~buffer,{
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arg buffer, indices;
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~indices = indices;
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});
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)
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// you may want to check the slice points here using FluidWaveform
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FluidWaveform(~buffer,~indices); // it may also be way too many slices to see properly!
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(
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// 3. analyze the slices
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~analyze = {
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arg buffer, indices, action;
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var time = SystemClock.seconds; // a timer just to keep tabs on how long this stuff is taking
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Routine{
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var feature_buf = Buffer(s); // a buffer for storing the mfcc analyses into
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var stats_buf = Buffer(s); // a buffer for storing the stats into
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var point_buf = Buffer(s); // a buffer we will use to add points to the dataset
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var ds = FluidDataSet(s); // the dataset that we'll add all these mfcc analyses to
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// bring the values in the slicepoints buffer from the server to the language as a float array
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indices.loadToFloatArray(action:{
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arg fa; // float array
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fa.doAdjacentPairs{
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/*
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take each of the adjacent pairs and pass them to this function as an array of 2 values
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nb. for example [0,1,2,3,4] will execute this function 4 times, passing these 2 value arrays:
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[0,1]
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[1,2]
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[2,3]
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[3,4]
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this will give us each slice point *and* the next slice point so that we
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can tell the analyzers where to start analyzing and how many frames to analyze
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*/
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arg start, end, i;
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// the next slice point minus the current one will give us the difference how many slices to analyze)
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var num = end - start;
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/* analyze the drum buffer starting at `start_samps` and for `num_samps` samples
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this returns a buffer (feautre_buf) that is 13 channels wide (for the 13 mfccs, see helpfile) and
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however many frames long as there are fft frames in the slice */
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FluidBufMFCC.processBlocking(s,buffer,start,num,features:feature_buf,numCoeffs:13,startCoeff:1);
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/* perform a statistical analysis on the mfcc analysis
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this will return just 13 channels, one for each mfcc channel in the feature_buf.
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each channel will have 7 frames corresponding to the 7 statistical analyses that it performs
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on that channel */
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FluidBufStats.processBlocking(s,feature_buf,stats:stats_buf);
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/* take all 13 channels from stats_buf, but just the first frame (mean) and convert it into a buffer
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that is 1 channel and 13 frames. this shape will be considered "flat" and therefore able to be
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added to the dataset */
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FluidBufFlatten.processBlocking(s,stats_buf,numFrames:1,destination:point_buf);
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// add it
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ds.addPoint("slice-%".format(i),point_buf);
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"Processing Slice % / %".format(i+1,indices.numFrames-1).postln;
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};
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s.sync;
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feature_buf.free; stats_buf.free; point_buf.free; // free buffers
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ds.print;
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"Completed in % seconds".format(SystemClock.seconds - time).postln;
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action.(buffer,indices,ds);
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});
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}.play;
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};
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~analyze.(~buffer,~indices,{
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arg buffer, indices, ds;
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~ds = ds;
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});
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)
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(
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// 4. Reduce to 2 Dimensions
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~umap = {
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arg buffer, indices, ds, action, numNeighbours = 15, minDist = 0.1;
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Routine{
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// get all the dimensions in the same general range so that when umap
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// makes its initial tree structure, the lower order mfcc coefficients
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// aren't over weighted
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var standardizer = FluidStandardize(s);
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// this is the dimensionality reduction algorithm, see helpfile for
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// more info
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var umap = FluidUMAP(s,2,numNeighbours,minDist);
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var redux_ds = FluidDataSet(s); // a new dataset for putting the 2D points into
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s.sync;
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standardizer.fitTransform(ds,redux_ds,{
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"standardization done".postln;
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umap.fitTransform(redux_ds,redux_ds,{
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"umap done".postln;
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action.(buffer,indices,redux_ds);
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});
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});
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}.play;
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};
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~umap.(~buffer,~indices,~ds,{
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arg buffer, indices, redux_ds;
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~ds = redux_ds;
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});
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)
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(
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// 5. Gridify if Desired
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~grid = {
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arg buffer, indices, redux_ds, action;
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Routine{
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// first normalize so they're all 0 to 1
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var normer = FluidNormalize(s);
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// this will shift all dots around so they're in a grid shape
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var grider = FluidGrid(s);
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// a new dataset to hold the gridified dots
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var newds = FluidDataSet(s);
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s.sync;
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normer.fitTransform(redux_ds,newds,{
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"normalization done".postln;
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grider.fitTransform(newds,newds,{
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"grid done".postln;
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action.(buffer,indices,newds);
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});
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});
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}.play;
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};
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~grid.(~buffer,~indices,~ds,{
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arg buffer, indices, grid_ds;
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~ds = grid_ds;
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});
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)
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(
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// 6. Plot
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~plot = {
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arg buffer, indices, redux_ds, action;
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Routine{
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var kdtree = FluidKDTree(s); // tree structure of the 2D points for fast neighbour lookup
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// a buffer for putting the 2D mouse point into so that it can be used to find the nearest neighbour
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var buf_2d = Buffer.alloc(s,2);
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// scaler just to double check and make sure that the points are 0 to 1
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// if the plotter is receiving the output of umap, they probably won't be...
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var scaler = FluidNormalize(s);
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// a new dataset told the normalized data
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var newds = FluidDataSet(s);
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s.sync;
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scaler.fitTransform(redux_ds,newds,{
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"scaling done".postln;
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kdtree.fit(newds,{
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"kdtree fit".postln;
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newds.dump({
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arg dict;
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var previous, fp;
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"ds dumped".postln;
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// pass in the dict from the dumped dataset. this is the data that we want to plot!
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fp = FluidPlotter(nil,Rect(0,0,800,800),dict,mouseMoveAction:{
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// when the mouse is clicked or dragged on the plotter, this function executes
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// the view is the FluidPlotter, the x and y are the position of the mouse according
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// to the range of the plotter. i.e., since our plotter is showing us the range 0 to 1
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// for both x and y, the xy positions will always be between 0 and 1
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arg view, x, y;
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buf_2d.setn(0,[x,y]); // set the mouse position into a buffer
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// then send that buffer to the kdtree to find the nearest point
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kdtree.kNearest(buf_2d,{
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arg nearest; // the identifier of the nearest point is returned (always as a symbol)
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if(previous != nearest,{ // as long as this isn't also the last one that was returned
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// split the integer off the indentifier to know how to look it up for playback
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var index = nearest.asString.split($-)[1].asInteger;
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previous = nearest;
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nearest.postln;
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// index.postln;
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{
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var startPos = Index.kr(indices,index); // look in the indices buf to see where to start playback
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var dur_samps = Index.kr(indices,index + 1) - startPos; // and how long
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var sig = PlayBuf.ar(1,buffer,BufRateScale.ir(buffer),startPos:startPos);
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var dur_sec = dur_samps / BufSampleRate.ir(buffer);
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var env;
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|
||||||
dur_sec = min(dur_sec,1); // just in case some of the slices are *very* long...
|
|
||||||
env = EnvGen.kr(Env([0,1,1,0],[0.03,dur_sec-0.06,0.03]),doneAction:2);
|
|
||||||
sig.dup * env;
|
|
||||||
}.play;
|
|
||||||
});
|
|
||||||
});
|
|
||||||
});
|
|
||||||
action.(fp,newds);
|
|
||||||
});
|
|
||||||
});
|
|
||||||
});
|
|
||||||
}.play;
|
|
||||||
};
|
|
||||||
|
|
||||||
~plot.(~buffer,~indices,~ds);
|
|
||||||
)
|
|
||||||
|
|
||||||
// ============== do all of it in one go =======================
|
|
||||||
(
|
|
||||||
var path = FluidFilesPath();
|
|
||||||
~load_folder.(path,{
|
|
||||||
arg buffer0;
|
|
||||||
~slice.(buffer0,{
|
|
||||||
arg buffer1, indices1;
|
|
||||||
~analyze.(buffer1, indices1,{
|
|
||||||
arg buffer2, indices2, ds2;
|
|
||||||
~umap.(buffer2,indices2,ds2,{
|
|
||||||
arg buffer3, indices3, ds3;
|
|
||||||
~plot.(buffer3,indices3,ds3,{
|
|
||||||
arg plotter;
|
|
||||||
"done with all".postln;
|
|
||||||
~fp = plotter;
|
|
||||||
});
|
|
||||||
});
|
|
||||||
});
|
|
||||||
});
|
|
||||||
});
|
|
||||||
)
|
|
||||||
@ -1,108 +0,0 @@
|
|||||||
(
|
|
||||||
Task{
|
|
||||||
var folder = "/Users/macprocomputer/Desktop/_flucoma/data_saves/211103_152953_2D_browsing_MFCC/";
|
|
||||||
// var folder = "/Users/macprocomputer/Desktop/_flucoma/data_saves/211103_161354_2D_browsing_SpectralShape/";
|
|
||||||
// var folder = "/Users/macprocomputer/Desktop/_flucoma/data_saves/211103_161638_2D_browsing_Pitch/";
|
|
||||||
~ds_original = FluidDataSet(s);
|
|
||||||
~buffer = Buffer.read(s,folder+/+"buffer.wav");
|
|
||||||
~indices = Buffer.read(s,folder+/+"indices.wav");
|
|
||||||
~kdtree = FluidKDTree(s,6);
|
|
||||||
~ds = FluidDataSet(s);
|
|
||||||
|
|
||||||
s.sync;
|
|
||||||
|
|
||||||
~indices.loadToFloatArray(action:{
|
|
||||||
arg fa;
|
|
||||||
~indices = fa;
|
|
||||||
});
|
|
||||||
|
|
||||||
~ds_original.read(folder+/+"ds.json",{
|
|
||||||
~ds.read(folder+/+"ds.json",{
|
|
||||||
~kdtree.fit(~ds,{
|
|
||||||
~ds.dump({
|
|
||||||
arg dict;
|
|
||||||
~ds_dict = dict;
|
|
||||||
"kdtree fit".postln;
|
|
||||||
});
|
|
||||||
});
|
|
||||||
});
|
|
||||||
});
|
|
||||||
}.play;
|
|
||||||
|
|
||||||
~play_id = {
|
|
||||||
arg id;
|
|
||||||
var index = id.asString.split($-)[1].asInteger;
|
|
||||||
var start_samps = ~indices[index];
|
|
||||||
var end_samps = ~indices[index+1];
|
|
||||||
var dur_secs = (end_samps - start_samps) / ~buffer.sampleRate;
|
|
||||||
{
|
|
||||||
var sig = PlayBuf.ar(1,~buffer,BufRateScale.ir(~buffer),startPos:start_samps);
|
|
||||||
var env = EnvGen.kr(Env([0,1,1,0],[0.03,dur_secs-0.06,0.03]),doneAction:2);
|
|
||||||
sig.dup;// * env;
|
|
||||||
}.play;
|
|
||||||
dur_secs;
|
|
||||||
};
|
|
||||||
~pt_buf = Buffer.alloc(s,~ds_dict.at("cols"));
|
|
||||||
)
|
|
||||||
|
|
||||||
(
|
|
||||||
// hear the 5 nearest points
|
|
||||||
Routine{
|
|
||||||
// var id = "slice-558";
|
|
||||||
var id = ~ds_dict.at("data").keys.choose;
|
|
||||||
~ds.getPoint(id,~pt_buf,{
|
|
||||||
~kdtree.kNearest(~pt_buf,{
|
|
||||||
arg nearest;
|
|
||||||
Routine{
|
|
||||||
id.postln;
|
|
||||||
~play_id.(id).wait;
|
|
||||||
nearest[1..].do{
|
|
||||||
arg near;
|
|
||||||
1.wait;
|
|
||||||
near.postln;
|
|
||||||
~play_id.(near).wait;
|
|
||||||
};
|
|
||||||
}.play;
|
|
||||||
})
|
|
||||||
});
|
|
||||||
}.play;
|
|
||||||
)
|
|
||||||
|
|
||||||
// Standardize
|
|
||||||
(
|
|
||||||
Routine{
|
|
||||||
var scaler = FluidStandardize(s);
|
|
||||||
s.sync;
|
|
||||||
scaler.fitTransform(~ds_original,~ds,{
|
|
||||||
~kdtree.fit(~ds,{
|
|
||||||
"standardized & kdtree fit".postln;
|
|
||||||
});
|
|
||||||
});
|
|
||||||
}.play;
|
|
||||||
)
|
|
||||||
|
|
||||||
// Normalize
|
|
||||||
(
|
|
||||||
Routine{
|
|
||||||
var scaler = FluidNormalize(s);
|
|
||||||
s.sync;
|
|
||||||
scaler.fitTransform(~ds_original,~ds,{
|
|
||||||
~kdtree.fit(~ds,{
|
|
||||||
"normalized & kdtree fit".postln;
|
|
||||||
});
|
|
||||||
});
|
|
||||||
}.play;
|
|
||||||
)
|
|
||||||
|
|
||||||
// Robust Scaler
|
|
||||||
(
|
|
||||||
Routine{
|
|
||||||
var scaler = FluidRobustScale(s);
|
|
||||||
s.sync;
|
|
||||||
scaler.fitTransform(~ds_original,~ds,{
|
|
||||||
~kdtree.fit(~ds,{
|
|
||||||
"normalized & kdtree fit".postln;
|
|
||||||
});
|
|
||||||
});
|
|
||||||
}.play;
|
|
||||||
)
|
|
||||||
Loading…
Reference in New Issue