diff --git a/release-packaging/Examples/dataset/MLP-AE-dataredux.scd b/release-packaging/Examples/dataset/MLP-AE-dataredux.scd
index dc2d937..e357c5a 100644
--- a/release-packaging/Examples/dataset/MLP-AE-dataredux.scd
+++ b/release-packaging/Examples/dataset/MLP-AE-dataredux.scd
@@ -1,19 +1,20 @@
 s.reboot;
 //Preliminaries: we want some audio, a couple of FluidDataSets, some Buffers
 (
-~raw = FluidDataSet(s,\MLP40);
+~raw = FluidDataSet(s,\Mel40);
+~norm = FluidDataSet(s,\Mel40n);
 ~retrieved = FluidDataSet(s,\ae2);
 ~audio = Buffer.read(s,File.realpath(FluidBufMelBands.class.filenameSymbol).dirname +/+ "../AudioFiles/Tremblay-ASWINE-ScratchySynth-M.wav");
 ~melfeatures = Buffer.new(s);
 ~stats = Buffer.alloc(s, 7, 40);
 ~datapoint = Buffer.alloc(s, 40);
-~mlp = FluidMLPRegressor(s,[10,2,10],1,1,2,10000,0.01,0.1,10,0);
+~mlp = FluidMLPRegressor(s,[9,2,9],activation: 1,outputActivation: 1,tapIn: 0,tapOut: 2,maxIter: 10000,learnRate: 0.1,momentum: 0.1,batchSize: 10,validation: 0.1);
+~normalizer = FluidNormalize(s);
 )
 
 // process the melbands
 FluidBufMelBands.process(s,~audio, features: ~melfeatures,action: {\done.postln;});
 
-~raw.free
 // Divide the time series in 100, and take the mean of each segment and add this as a point to
 // the 'raw' FluidDataSet
 (
@@ -31,15 +32,19 @@ FluidBufMelBands.process(s,~audio, features: ~melfeatures,action: {\done.postln;
     dsWr = FluidDataSetWr.kr(\MLP40, buf: buf, trig: Done.kr(stats));
     LocalOut.kr( Done.kr(dsWr));
     FreeSelf.kr(count - 99);
+	Poll.kr(trig,count);
 }.play;
 )
 // wait for the post window to acknoledge the job is done.
 
+// normalize the input
+~normalizer.fitTransform(~raw,~norm);
+
 //we can then run the AE - the server might become yellow :)
-~mlp.fit(~raw,~raw,{|x|x.postln;});
+~mlp.fit(~norm,~norm,{|x|x.postln;});
 
 //we can then retrieve the hidden layer #2
-~mlp.predict(~raw,~retrieved)
+~mlp.predict(~norm,~retrieved)
 
 //check the structure of retrieved
 ~retrieved.print
@@ -47,17 +52,17 @@ FluidBufMelBands.process(s,~audio, features: ~melfeatures,action: {\done.postln;
 //let's normalise it for display
 ~normData = FluidDataSet(s,\ae2N);
 ~reducedarray = Array.new(100);
-~normalizer = FluidNormalize(s);
-~normalizer.fitTransform(~retrieved,~normData, action:{
+~normalView = FluidNormalize(s);
+(
+~normalView.fitTransform(~retrieved,~normData, action:{
     ~normData.dump{|x| 100.do{|i|
         ~reducedarray.add(x["data"][i.asString])
     }};
 });
-
+)
 ~normData.print
 ~reducedarray.postln;
 
-
 //Visualise the 2D projection of our original 12D data
 (
 d = ~reducedarray.flatten(1).unlace.deepCollect(1, { |x| x.normalize});
diff --git a/release-packaging/Examples/dataset/demo-dataset-maker-utilities.scd b/release-packaging/Examples/dataset/demo-dataset-maker-utilities.scd
index 46bce5c..d216e39 100644
--- a/release-packaging/Examples/dataset/demo-dataset-maker-utilities.scd
+++ b/release-packaging/Examples/dataset/demo-dataset-maker-utilities.scd
@@ -7,12 +7,12 @@
 ~flatbuf = 4.collect{Buffer.new};
 
 // here we instantiate a loader which creates a single large buffer with a dictionary of what was included in it
-// ~loader = FluidLoadFolder("/Volumes/machins/projets/newsfeed/smallnum/");
-~loader = FluidLoadFolder("/Volumes/machins/projets/newsfeed/segments/");
+// ~loader = FluidLoadFolder("/Volumes/machins/projets/newsfeed/sons/smallnum/");
+~loader = FluidLoadFolder("/Volumes/machins/projets/newsfeed/sons/segments/");
 
 // here we instantiate a further slicing step if needs be, which iterate through all the items of the FluidLoadFolder and slice the slices with the declared function. Here it is a very picky onset slicer
 ~slicer = FluidSliceCorpus({ |src,start,num,dest|
-	FluidBufOnsetSlice.kr(src,start,num,metric: 9, minSliceLength: 17, indices:dest, threshold:2)
+	FluidBufOnsetSlice.kr(src,start,num,metric: 9, minSliceLength: 17, indices:dest, threshold:2,blocking: 1)
 });
 
 // here we instantiate a process of description and dataset writing, which will run each slice of the previous slice and write the entry. Note the chain of Done.kr triggers.
@@ -20,10 +20,10 @@
 	var mfcc, stats, writer, flatten,mfccBuf, statsBuf, flatBuf, label, voice;
 	label = data.key;
     voice = data.value[\voice];
-	mfcc = FluidBufMFCC.kr(src,startFrame:start,numFrames:num,numChans:1,features:~mfccbuf[voice],trig:1);
-	stats = FluidBufStats.kr(~mfccbuf[voice],stats:~statsbuf[voice],trig:Done.kr(mfcc));
-	flatten = FluidBufFlatten.kr(~statsbuf[voice],~flatbuf[voice],trig:Done.kr(stats));
-	writer = FluidDataSetWr.kr(~ds,label, -1, ~flatbuf[voice], Done.kr(flatten))
+	mfcc = FluidBufMFCC.kr(src,startFrame:start,numFrames:num,numChans:1,features:~mfccbuf[voice],trig:1,blocking: 1);
+	stats = FluidBufStats.kr(~mfccbuf[voice],stats:~statsbuf[voice],trig:Done.kr(mfcc),blocking: 1);
+	flatten = FluidBufFlatten.kr(~statsbuf[voice],~flatbuf[voice],trig:Done.kr(stats),blocking: 1);
+	writer = FluidDataSetWr.kr(~ds,label, -1, ~flatbuf[voice], Done.kr(flatten),blocking: 1)
 });
 )