// load a source b = Buffer.read(s,"/Volumes/machins/projets/newsfeed/sons/textes/Audio/synth/fromtexttospeech-AmE-George.wav") b.play //slightly oversegment with novelty //segments should still make sense but might cut a few elements in 2 or 3 ~originalslices = Buffer(s); FluidBufNoveltySlice.process(s, b, indices: ~originalslices, feature: 1, kernelSize: 29, threshold: 0.05, filterSize: 5, hopSize: 128, action: {~originalslices.numFrames.postln;}) //test the segmentation by looping them ( { BufRd.ar(1, b, Phasor.ar(0,1, BufRd.kr(1, ~originalslices, MouseX.kr(0, BufFrames.kr(~originalslices) - 1), 0, 1), BufRd.kr(1, ~originalslices, MouseX.kr(1, BufFrames.kr(~originalslices)), 0, 1), BufRd.kr(1,~originalslices, MouseX.kr(0, BufFrames.kr(~originalslices) - 1), 0, 1)), 0, 1); }.play; ) //analyse each segment with MFCCs in a dataset ~originalslices.getn(0,~originalslices.numFrames, {|x|~originalslicesarray = x; if ((x.last != b.numFrames), {~originalslicesarray = ~originalslicesarray ++ (b.numFrames)}); });//retrieve the indices and add the file boundary at the end if not there already //iterates through the //a few buffers and our dataset - with back and forth from the language ( ~mfccs = Buffer(s); ~stats = Buffer(s); ~flat = Buffer(s); ~slices = FluidDataSet(s,\slices); Routine{ s.sync; (~originalslicesarray.size - 1).do{|i| FluidBufMFCC.process(s, b, startFrame: ~originalslicesarray[i], numFrames: (~originalslicesarray[i+1] - ~originalslicesarray[i]), numChans: 1,features: ~mfccs, numCoeffs: 20, action: { FluidBufStats.process(s, ~mfccs, startChan: 1, stats: ~stats, action: { FluidBufFlatten.process(s, ~stats, ~flat, action: { ~slices.addPoint(i.asSymbol, ~flat); }); }); }); }; }.play; ) ~slices.print //run a window over consecutive segments, forcing them in 2 classes, and merging the consecutive segments of similar class //we overlap the analysis with the last (original) slice to check for continuity ( ~winSize = 4;//the number of consecutive items to split in 2 classes; ~query = FluidDataSetQuery(s); ~kmeans = FluidKMeans(s,2,100); ~windowDS = FluidDataSet(s,\windowDS); ~windowLS = FluidLabelSet(s,\windowLS); ) ( Routine{ ~indices = [0]; ~head = 0; ~sliceDict = Dictionary.new(4); ~tempDict = Dictionary.new(4); ~slices.dump{|x|~sliceDict = x;}; s.sync; while ( {~head <= (~originalslicesarray.size - ~winSize)}, { var step = ~winSize - 1; var nbass = []; //run a process on ~winSize items from ~head (with an overlap of 1) //copy the items to a subdataset ~winSize.do{|i| ~tempDict.put((i.asString), ~sliceDict["data"][(i+~head).asString]);//here one could curate which stats to take "whichslices:%\n".postf(i+~head); }; ~windowDS.load(Dictionary.newFrom([\cols, 133, \data, ~tempDict])); s.sync; "% - loaded\n".postf(~head); //kmeans 2 and retrieve ordered array of class assignations ~kmeans.fitPredict(~windowDS, ~windowLS, {|x| nbass = x;}); s.sync; "% - fitted1: ".postf(~head); nbass.postln; // if (nbass.includes(0.0), { Routine{~kmeans.fitPredict(~windowDS, ~windowLS, {|x| nbass = x; "% - fitted2: ".postf(~head); nbass.postln; s.sync;});}.play; }); ~windowLS.dump{|x|~assignments = x.at("data").asSortedArray.flop[1].flatten;}; s.sync; "% - assigned ".postf(~head); ~assignments.postln; step.do{|i| if (~assignments[i+1] != ~assignments[i], {~indices= ~indices ++ (~originalslicesarray[~head+i+1])}); }; ~head = ~head + step; "-----------------".postln; }); //leftovers if ( (~originalslicesarray.size - ~head) > 1, { //run a process on (a.size - ~head) items from ~head (~originalslicesarray.size - ~head - 1).do{|i| if (~assignments[i+1] != ~assignments[i], {~indices= ~indices ++ (~originalslicesarray[~head+i+1])}); // (~head+i).postln; }; }); ~indices.postln; }.play; ) {var i = 8;BufRd.ar(1,b,Line.ar(~originalslicesarray[i],~originalslicesarray[i+1],(~originalslicesarray[i+1] - ~originalslicesarray[i])/b.sampleRate, doneAction: 2))}.play; {var i = 4;BufRd.ar(1,b,Line.ar(~indices[i],~indices[i+1],(~indices[i+1] - ~indices[i])/b.sampleRate, doneAction: 2))}.play; //export to reaper