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UPDATE: WckGraph appears to be depriciated. Here’s how I do realtime data plotting now:

http://www.swharden.com/blog/2013-05-08-realtime-data-plotting-in-python/

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**I’m stretching the limits of what these software platforms were designed to to**, but I’m impressed such a haphazard hacked-together code as this produces fast, functional results. The code below is the simplest case code I could create which would graph the audio spectrum of the microphone input (or a WAV file or other sound as it’s being played). There’s some smoothing involved (moving window down-sampling along the frequency axis and sequence averaging along the time axis) but the darn thing seems to keep up with realtime audio input at a good 30+ FPS on my modest maching. It should work on Windows and Linux. I chose not to go with matplotlib because I didn’t think it was fast enough for my needs in this one case (although I love it in every other way). Here’s what the code below looks like running:

**NOTE** that this program was designed with the intent of recording the FFTs, therefore if the program “falls behind” the realtime input, it will buffer the sound on its own and try to catch up (accomplished by two layers of threading). In this way, *EVERY MOMENT* of audio is interpreted. If you’re just trying to create a spectrograph for simple purposes, have it only sample the audio when it needs to, rather than having it sample audio continuously.

import pyaudio import scipy import struct import scipy.fftpack from Tkinter import * import threading import time, datetime import wckgraph import math #ADJUST THIS TO CHANGE SPEED/SIZE OF FFT bufferSize=2**11 #bufferSize=2**8 # ADJUST THIS TO CHANGE SPEED/SIZE OF FFT sampleRate=48100 #sampleRate=64000 p = pyaudio.PyAudio() chunks=[] ffts=[] def stream(): global chunks, inStream, bufferSize while True: chunks.append(inStream.read(bufferSize)) def record(): global w, inStream, p, bufferSize inStream = p.open(format=pyaudio.paInt16,channels=1, rate=sampleRate,input=True,frames_per_buffer=bufferSize) threading.Thread(target=stream).start() def downSample(fftx,ffty,degree=10): x,y=[],[] for i in range(len(ffty)/degree-1): x.append(fftx[i*degree+degree/2]) y.append(sum(ffty[i*degree:(i+1)*degree])/degree) return [x,y] def smoothWindow(fftx,ffty,degree=10): lx,ly=fftx[degree:-degree],[] for i in range(degree,len(ffty)-degree): ly.append(sum(ffty[i-degree:i+degree])) return [lx,ly] def smoothMemory(ffty,degree=3): global ffts ffts = ffts+[ffty] if len(ffts)< =degree: return ffty ffts=ffts[1:] return scipy.average(scipy.array(ffts),0) def detrend(fftx,ffty,degree=10): lx,ly=fftx[degree:-degree],[] for i in range(degree,len(ffty)-degree): ly.append(ffty[i]-sum(ffty[i-degree:i+degree])/(degree*2)) #ly.append(fft[i]-(ffty[i-degree]+ffty[i+degree])/2) return [lx,ly] def graph(): global chunks, bufferSize, fftx,ffty, w if len(chunks)>0: data = chunks.pop(0) data=scipy.array(struct.unpack("%dB"%(bufferSize*2),data)) #print "RECORDED",len(data)/float(sampleRate),"SEC" ffty=scipy.fftpack.fft(data) fftx=scipy.fftpack.rfftfreq(bufferSize*2, 1.0/sampleRate) fftx=fftx[0:len(fftx)/4] ffty=abs(ffty[0:len(ffty)/2])/1000 ffty1=ffty[:len(ffty)/2] ffty2=ffty[len(ffty)/2::]+2 ffty2=ffty2[::-1] ffty=ffty1+ffty2 ffty=scipy.log(ffty)-2 #fftx,ffty=downSample(fftx,ffty,5) #fftx,ffty=detrend(fftx,ffty,30) #fftx,ffty=smoothWindow(fftx,ffty,10) ffty=smoothMemory(ffty,3) #fftx,ffty=detrend(fftx,ffty,10) w.clear() #w.add(wckgraph.Axes(extent=(0, -1, fftx[-1], 3))) w.add(wckgraph.Axes(extent=(0, -1, 6000, 3))) w.add(wckgraph.LineGraph([fftx,ffty])) w.update() if len(chunks)>20: print "falling behind...",len(chunks) def go(x=None): global w,fftx,ffty print "STARTING!" threading.Thread(target=record).start() while True: graph() root = Tk() root.title("SPECTRUM ANALYZER") root.geometry('500x200') w = wckgraph.GraphWidget(root) w.pack(fill=BOTH, expand=1) go() mainloop()

Hi Scott,

it could be great to get the fundamental frequency of an input sound.

For example, If execute a 220 Hz sound, how could I recognize that frequency?

Could you provide an example?

Thanks a lot,

you made a great job.