wolffd@0: function demo7tonality
wolffd@0: %To get familiar with some approaches of tonal analysis using MIRtoolbox, 
wolffd@0: % and to assess their performances.  
wolffd@0: 
wolffd@0: % Part 1. We will first investigate the performance of the chromagram
wolffd@0: % analysis, using very simple musical samples. 
wolffd@0: 
wolffd@0: % 1.3. In the audio file ÔtrumpetÕ, the same pitch is played by a trumpet.
wolffd@0: % Compute its chromagram. What are the chromas detected by the function? 
wolffd@0: % Can you explain the result?
wolffd@0: mirchromagram('trumpet')
wolffd@0: 
wolffd@0: % 1.4. A more detailed representation of the chromagram can be obtained by 
wolffd@0: % decomposing each pitch class into its different possible absolute values. 
wolffd@0: % For that purpose, just add the parameter: 
wolffd@0: c = mirchromagram('trumpet','Wrap',0) 
wolffd@0: 
wolffd@0: % 1.5. Compute also the key strength related to the chromagram. 
wolffd@0: mirkeystrength(c) 
wolffd@0: 
wolffd@0: pause, close all
wolffd@0: 
wolffd@0: % 1.6. In the audio file ÔpianoF4Õ, the same pitch is  played by a piano.
wolffd@0: [ks c] = mirkeystrength('pianoF4') 
wolffd@0: 
wolffd@0: a = miraudio('pianoF4','excerpt',.7,2);
wolffd@0: [ks c] = mirkeystrength(a)
wolffd@0: 
wolffd@0: pause, close all
wolffd@0: 
wolffd@0: % 1.7. Investigate the chromagram analysis of triad chords
wolffd@0: [ks c] = mirkeystrength('Amin3')
wolffd@0: p = mirpeaks(ks)
wolffd@0: mirkey(p)
wolffd@0: 
wolffd@0: pause, close all
wolffd@0: 
wolffd@0: [k kc ks] = mirkey('Amaj3')
wolffd@0: [k kc ks] = mirkey('Amin4')
wolffd@0: [k kc ks] = mirkey('Amaj4')
wolffd@0: 
wolffd@0: pause, close all
wolffd@0: 
wolffd@0: [k kc ks] = mirkey('Amin5')
wolffd@0: [k kc ks] = mirkey('Amaj5')
wolffd@0: [k kc ks] = mirkey('Cmaj')
wolffd@0: 
wolffd@0: pause, close all
wolffd@0: 
wolffd@0: [k kc ks] = mirkey(miraudio('Amin3','Excerpt',.2,1))
wolffd@0: [k kc ks] = mirkey(miraudio('Amin4','Excerpt',.2,1))
wolffd@0: [k kc ks] = mirkey(miraudio('Amaj3','Excerpt',.2,1))
wolffd@0: [k kc ks] = mirkey(miraudio('Amaj4','Excerpt',.2,1))
wolffd@0: 
wolffd@0: pause, close all
wolffd@0: 
wolffd@0: [k kc ks] = mirkey(miraudio('Amin5','Excerpt',.2,1))
wolffd@0: [k kc ks] = mirkey(miraudio('Amaj5','Excerpt',.2,1))
wolffd@0: [k kc ks] = mirkey(miraudio('Cmaj','Excerpt',.2,1))
wolffd@0: 
wolffd@0: pause, close all
wolffd@0:  
wolffd@0: %Part 2. Let's analyze several extracts from 
wolffd@0: %real music. For each extract, try the 
wolffd@0: %following: 
wolffd@0: 
wolffd@0: %2.1. Listen to the piece: 
wolffd@0: mirplay('vivaldi') 
wolffd@0: soundsc(sin(2*pi*440*(0:1/8192:1)))
wolffd@0: 
wolffd@0: %2.2. Compute the chromagram of the 
wolffd@0: %whole extract. What tonal center could be 
wolffd@0: %inferred from the curve? Does it 
wolffd@0: %correspond to your expectation? 
wolffd@0: c = mirchromagram('vivaldi')
wolffd@0: 
wolffd@0: %2.3. Compute the key strength related to 
wolffd@0: %the chromagram. Is the result congruent 
wolffd@0: %with the tonality inferred in 2.1. and 2.2.? 
wolffd@0: ks = mirkeystrength(c)
wolffd@0: [k kc ks] = mirkey(ks)
wolffd@0: 
wolffd@0: %2.4. A more detailed representation of the 
wolffd@0: %key strengths can be obtained by 
wolffd@0: %computing the self-organizing map: 
wolffd@0: som = mirkeysom(c)
wolffd@0: 
wolffd@0: pause, close all
wolffd@0: 
wolffd@0: %2.5. Analyse in the same way other audio files
wolffd@0: [k kc ks] = mirkey('czardas')
wolffd@0: mirkeysom('czardas')
wolffd@0: 
wolffd@0: pause, close all
wolffd@0: 
wolffd@0: %Part 3. The temporal evolution of the 
wolffd@0: %tonal dimension can be assessed by 
wolffd@0: %decomposing first the audio into frames
wolffd@0: [k kc ks] = mirkey('czardas','frame')
wolffd@0: mirkeysom('czardas','frame')