1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
|
class:: SpectralEntropy
summary:: Spectral feature extraction
related:: Classes/SensoryDissonance
categories:: UGens>Analysis
keyword:: spectral entropy
Description::
Spectral Entropy, with a choice of number of sub-bands. If one band, a measure of general peakiness of the spectral distribution.
See:
SPECTRAL ENTROPY AS SPEECH FEATURES FOR SPEECH RECOGNITION Aik Ming Toh, Roberto Togneri and Sven Nordholm http://www.ee.uwa.edu.au/~roberto/research/theses/tr05-01.pdf
Pass an FFT in.
classmethods::
method::kr
argument::fft
input fft chain, that is, from an FFT UGen
argument::fftsize
Size of FFT buffer must be known in advance for pre-calculation
argument::numbands
Number of sub-bands for entropy calculation; spectral bins are collected in sub-bands, and the number of outputs of the UGen is numbands
Examples::
code::
(
{
var in, fft, entropy;
//in = SinOsc.ar(MouseX.kr(100,1000),0,0.1);
//in = Mix(SinOsc.ar([440,MouseX.kr(440,880)],0,0.1));
in= SoundIn.ar;
fft = FFT(LocalBuf(2048), in);
entropy=SpectralEntropy.kr(fft,2048,1); //one output band (so full spectrum's entropy)
entropy.poll;
Out.ar(0,Pan2.ar(0.1*Blip.ar(100,10*(entropy.sqrt))));
}.play
)
(
{
var in, fft, entropy;
var amplitude;
in= SoundIn.ar;
amplitude = Amplitude.kr(in);
fft = FFT(LocalBuf(1024), in);
entropy=SpectralEntropy.kr(fft,1024,10);//10 bands
entropy = entropy * (amplitude.min(0.2)*5.0); //scale by amplitude to avoid low amplitude noise issues
entropy.poll;
//Out.ar(0,Pan2.ar(0.1*Blip.ar((entropy[0])*200,entropy[1].sqrt)));
Out.ar(0,Splay.ar(0.1*Blip.ar((entropy)*200,entropy.sqrt)));
}.play
)
::
|
