diff toolboxes/MIRtoolbox1.3.2/MIRToolboxDemos/demo4segmentation.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/MIRtoolbox1.3.2/MIRToolboxDemos/demo4segmentation.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,154 @@
+function demo4segmentation
+% To get familiar with some approaches of segmentation of audio files 
+% using MIRtoolbox. 
+ 
+% 1. Load an audio file (for instance, guitar.wav). 
+a = miraudio('guitar');
+
+% 2. We will perform the segmentation strategy as proposed in (Foote &
+% Cooper, 2003). First, decompose the file into successive frames of 50 ms 
+% without overlap. 
+help mirframe
+fr = mirframe(a,0.05,1)
+
+% 3. Compute the spectrum representation (FFT) of the frames.
+sp = mirspectrum(fr)
+clear fr
+% (Remove from the memory any data that will not be used any more.)
+
+% 4. Compute the similarity matrix that shows the similarity between the 
+% spectrum of different frames.
+help mirsimatrix
+sm = mirsimatrix(sp)
+clear sp
+% Look at the structures shown in the matrix and find the relation with the
+% structure heard when listening to the extract. 
+
+% 5. Estimate the novelty score related to the similarity matrix. It 
+% consists in a convolution of the diagonal of the matrix with a
+% checker-board Gaussian kernel. Use the novelty function for that purpose. 
+help mirnovelty
+nv = mirnovelty(sm)
+
+% 6. Detect the peaks in the novelty score.
+help mirpeaks
+p1 = mirpeaks(nv)
+
+% You can change the threshold value of the peak picker function in order to 
+% get better results. 
+p2 = mirpeaks(nv,'Contrast',0.01)
+
+clear nv
+
+% 7. Segment the original audio file using the peaks as position for
+% segmentation. 
+help mirsegment
+s1 = mirsegment(a,p1)
+clear p1
+
+% 8. Listen to the results.
+mirplay(s1)
+
+%s2 = mirsegment(a,p2)
+%clear p2
+%mirplay(s2)
+
+% 9. Compute the similarity matrix of this obtained segmentation, in order
+% to view the relationships between the different segments and their
+% possible clustering into higher-level groups. 
+mirsimatrix(s1,'Similarity')
+clear s1
+%mirsimatrix(s2)
+%clear s2
+
+display('Strike any key to continue...');
+pause
+close all
+
+% 10. Change the size of the kernel used in the novelty function, in order
+% to obtain segmentations of different levels of detail, from detailed
+% analysis of the local texture, to very simple segmentation of the whole 
+% piece.
+n100 = mirnovelty(sm,'KernelSize',100)
+n50 = mirnovelty(sm,'KernelSize',50)
+n10 = mirnovelty(sm,'KernelSize',10)
+clear sm
+% As you can see, the smaller the gaussian kernel is, the more peaks can be
+% found in the novelty score. Indeed, if the kernel is small, the cumulative
+% multiplication of its elements with the superposed elements in the
+% similarity matrix may vary more easily, throughout the progressive
+% sliding of the kernel along the diagonal of the similarity matrix, and
+% local change of texture may be more easily detected. On the contrary,
+% when the kernel is large, only large-scale change of texture are
+% detected.
+
+display('Strike any key to continue...');
+pause
+close all
+
+p100 = mirpeaks(n100,'NoBegin','NoEnd')
+clear n100
+p50 = mirpeaks(n50,'NoBegin','NoEnd')
+clear n50
+p10 = mirpeaks(n10,'NoBegin','NoEnd')
+clear n10
+s100 = mirsegment(a,p100)
+clear p100
+mirplay(s100)
+clear s100
+s50 = mirsegment(a,p50)
+clear p50
+mirplay(s50)
+clear s50
+s10 = mirsegment(a,p10)
+clear p10
+mirplay(s10)
+clear s10
+
+display('Strike any key to continue...');
+pause
+close all
+
+% One more compact way of writing these commands is as follows:
+mirsegment(a,'Novelty')
+mirsegment(a,'Novelty','Contrast',0.01)
+mirsegment(a,'Novelty','KernelSize',100)
+
+display('Strike any key to continue...');
+pause
+close all
+
+% Besides, if you want to see the novelty curve with the peaks, just add a
+% second output:
+[s50 p50] = mirsegment(a,'Novelty','KernelSize',50)
+clear s50 p50
+[s10 p10] = mirsegment(a,'Novelty','KernelSize',10)
+clear a s10 p10
+
+display('Strike any key to continue...');
+pause
+close all
+
+
+% 11. Try the whole process with MFCC instead of spectrum analysis. Take the
+% first ten MFCC for instance.
+help mirsegment
+% The segment function can simply be called as follows:
+sc = mirsegment('czardas','Novelty','MFCC','Rank',1:10)
+clear sc
+
+% Here are some other examples of use:
+[ssp p m b] = mirsegment('valse_triste_happy','Spectrum',...
+                                'KernelSize',150,'Contrast',.1)
+clear p m b
+mirplay(ssp)
+clear ssp
+
+display('Strike any key to continue...');
+pause
+close all
+
+[smfcc2 p m a] = mirsegment('valse_triste_happy','MFCC',2:10,...
+                                'KernelSize',150,'Contrast',.1)
+clear p m a
+mirplay(smfcc2)
\ No newline at end of file