FluidNMFMatch declaration and attempt to Englicise

release-packaging/Classes/FluidNMFMatch.sc

@@ -1,7 +1,7 @@
 FluidNMFMatch : MultiOutUGen {
 
-	*kr { arg in = 0, dictBufNum, maxrank = 1, nIter = 10, winSize = 1024, hopSize = 256, fftSize = -1;
-		^this.multiNew('control', in, dictBufNum, maxrank, nIter, winSize, hopSize, fftSize);
+	*kr { arg in = 0, dictBufNum, maxRank = 1, nIter = 10, winSize = 1024, hopSize = 256, fftSize = -1;
+		^this.multiNew('control', in, dictBufNum, maxRank, nIter, winSize, hopSize, fftSize);
 	}
 
 	init {arg ...theInputs;

release-packaging/HelpSource/Classes/FluidNMFMatch.schelp

@@ -1,10 +1,10 @@
 TITLE:: FluidNMFMatch
-SUMMARY:: Real-Time Non-Negative Matrix Factorisation on Buffered Dictionaries
+SUMMARY:: Real-Time Non-Negative Matrix Factorisation with Fixed Dictionaries
 CATEGORIES:: Libraries>FluidDecomposition
 RELATED:: Guides/FluCoMa, Guides/FluidDecomposition, Classes/FluidBufNMF
 
 DESCRIPTION::
-The FluidBufNMF object provides the activation (linked to amplitude) for each pre-defined dictionaries (similar to spectra) predefined in a buffer. These dictionaries would have usually be computed through an offline Non-Negative Matrix Factorisation (NMF) footnote:: Lee, Daniel D., and H. Sebastian Seung. 1999. ‘Learning the Parts of Objects by Non-Negative Matrix Factorization’. Nature 401 (6755): 788–91. https://doi.org/10.1038/44565 :: with the link::Classes/FluidBufNMF:: UGen. NMF has been a popular technique in signal processing research for things like source separation and transcription footnote:: Smaragdis and Brown, Non-Negative Matrix Factorization for Polyphonic Music Transcription.::, although its creative potential is so far relatively  unexplored.
+The FluidNMFMatch object provides the activation (linked to amplitude) for each pre-defined dictionaries (similar to spectra) predefined in a buffer. These dictionaries would have usually be computed through an offline Non-Negative Matrix Factorisation (NMF) footnote:: Lee, Daniel D., and H. Sebastian Seung. 1999. ‘Learning the Parts of Objects by Non-Negative Matrix Factorization’. Nature 401 (6755): 788–91. https://doi.org/10.1038/44565 :: with the link::Classes/FluidBufNMF:: UGen. NMF has been a popular technique in signal processing research for things like source separation and transcription footnote:: Smaragdis and Brown, Non-Negative Matrix Factorization for Polyphonic Music Transcription.::, although its creative potential is so far relatively  unexplored.
 
 The algorithm takes a buffer in which provides a spectral definition of a number of components, determined by the rank argument and the dictionary buffer channel count. It works iteratively, by trying to find a combination of amplitudes ('activations') that yield the original magnitude spectrogram of the audio input when added together. By and large, there is no unique answer to this question (i.e. there are different ways of accounting for an evolving spectrum in terms of some set of templates and envelopes). In its basic form, NMF is a form of unsupervised learning: it starts with some random data and then converges towards something that minimizes the distance between its generated data and the original:it tends to converge very quickly at first and then level out. Fewer iterations mean less processing, but also less predictable results.