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.
| fft |
input fft chain, that is, from an FFT UGen |
| fftsize |
Size of FFT buffer must be known in advance for pre-calculation |
| 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 |
(
{
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
)