#!/usr/bin/python3 # -*- coding: utf-8 -*- """ Audio Spectrum analyser v0.7.0 - summed given fft in n bands, but re normalized between 0 - 70? - Peaks L and R - amplitude for given target frequency and PEAK frequency - "music note" to given frequency - Real FFT, Imaginary FFT, Real + imaginary FFT - threshold detection todo : by Sam Neurohack from /team/laser for python 2 & 3 Stereo : CHANNELS = 2 mono : CHANNELS = 1 """ import numpy as np import pyaudio from math import log, pow #import matplotlib.pyplot as plt #from scipy.interpolate import Akima1DInterpolator #import matplotlib.pyplot as plt DEVICE = 3 CHANNELS = 2 START = 0 RATE = 44100 # time resolution of the recording device (Hz) CHUNK = 4096 # number of data points to read at a time. Almost 10 update/second TARGET = 2100 # show only this one frequency A4 = 440 C0 = A4*pow(2, -4.75) name = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"] data = [] p = pyaudio.PyAudio() # start the PyAudio class stream = p.open(format = pyaudio.paInt16, channels = CHANNELS, input_device_index = DEVICE, rate=RATE, input=True, frames_per_buffer=CHUNK) #uses default input device # # Audio devices & audiogen functions # def list_devices(): # List all audio input devices p = pyaudio.PyAudio() i = 0 n = p.get_device_count() print (n,"devices found") while i < n: dev = p.get_device_info_by_index(i) if dev['maxInputChannels'] > 0: print (str(i)+'. '+dev['name']) i += 1 def valid_input_devices(self): """ See which devices can be opened for microphone input. call this when no PyAudio object is loaded. """ mics=[] for device in range(self.p.get_device_count()): if self.valid_test(device): mics.append(device) if len(mics)==0: print("no microphone devices found!") else: print("found %d microphone devices: %s"%(len(mics),mics)) return mics def loop(): try: #plt.ion() #plt.axis([x[0], x[-1], -0.1, max_f]) fftbands = [0,1,2,3,4,5,6,7,8,9] plt.xlabel('frequencies') plt.ylabel('amplitude') data = audioinput() drawfreq, fft = allfft(data) #lines = plt.plot(drawfreq, fft) #plt.axis([drawfreq[0], drawfreq[-1], 0, np.max(fft)]) #plt.plot(drawfreq, fft) #plt.show() #line, = plt.plot(fftbands, levels(fft,10)) line, = plt.plot(drawfreq, fft) #while True : for i in range(50): data = audioinput() # smooth the FFT by windowing data #data = data * np.hanning(len(data)) # conversion to -1 to +1 # normed_samples = (data / float(np.iinfo(np.int16).max)) # Left is channel 0 dataL = data[0::2] # Right is channel 1 dataR = data[1::2] # Peaks L and R peakL = np.abs(np.max(dataL)-np.min(dataL))/CHUNK peakR = np.abs(np.max(dataR)-np.min(dataR))/CHUNK # print(peakL, peakR) drawfreq, fft = allfft(data) #fft, fftr, ffti, fftb, drawfreq = allfft(data) #line.set_ydata(levels(fft,10)) line.set_ydata(fft) plt.pause(0.01) #print(drawfreq) #print(fft) #print (levels(fft,10)) #line.set_ydata(fft) #plt.pause(0.01) # pause avec duree en secondes # lines = plt.plot(x, y) #lines[0].set_ydata(fft) #plt.legend(['s=%4.2f' % s]) #plt.draw() #plt.show() ''' targetpower,freqPeak = basicfft(audioinput(stream)) print("amplitude", targetpower, "@", TARGET, "Hz") if freqPeak > 0.0: print("peak frequency: %d Hz"%freqPeak, pitch(freqPeak)) ''' plt.show() except KeyboardInterrupt: stream.stop_stream() stream.close() p.terminate() print("End...") # Close properly def close(): stream.stop_stream() stream.close() p.terminate() # Return "music note" to given frequency def pitch(freq): h = round(12*(log(freq/C0)/log(2))) octave = h // 12 n = h % 12 return name[n] + str(octave) # Return summed given fft in n bands, but re normalized 0 - 70 def levels(fourier, bands): size = int(len(fourier)) levels = [0.0] * bands # Add up for n bands # remove normalizer if you want raw added data in all bands normalizer = size/bands #print (size,bands,size/bands) levels = [sum(fourier[I:int(I+size/bands)])/normalizer for I in range(0, size, int(size/bands))][:bands] for band in range(bands): if levels[band] == np.NINF: levels[band] =0 return levels # read CHUNK size in audio buffer def audioinput(): # When reading from our 16-bit stereo stream, we receive 4 characters (0-255) per # sample. To get them in a more convenient form, numpy provides # fromstring() which will for each 16 bits convert it into a nicer form and # turn the string into an array. return np.fromstring(stream.read(CHUNK),dtype=np.int16) # power for given TARGET frequency and PEAK frequency # do fft first. No conversion in 'powers' def basicfft(data): #data = data * np.hanning(len(data)) # smooth the FFT by windowing data fft = abs(np.fft.fft(data).real) #fft = 10*np.log10(fft) fft = fft[:int(len(fft)/2)] # first half of fft freq = np.fft.fftfreq(CHUNK,1.0/RATE) freq = freq[:int(len(freq)/2)] # first half of FFTfreq assert freq[-1]>TARGET, "ERROR: increase chunk size" # return power for given TARGET frequency and peak frequency return fft[np.where(freq > TARGET)[0][0]], freq[np.where(fft == np.max(fft))[0][0]]+1 # todo : Try if data = 1024 ? # in "power' (0-70?) get Real FFT, Imaginary FFT, Real + imaginary FFT def allfft(data): #print ("allfft", len(data)) fft = np.fft.fft(data) #print("fft",len(fft)) fftr = 10*np.log10(abs(fft.real))[:int(len(data)/2)] ffti = 10*np.log10(abs(fft.imag))[:int(len(data)/2)] fftb = 10*np.log10(np.sqrt(fft.imag**2+fft.real**2))[:int(len(data)/2)] #print("fftb",len(fftb)) drawfreq = np.fft.fftfreq(np.arange(len(data)).shape[-1])[:int(len(data)/2)] drawfreq = drawfreq*RATE/1000 #make the frequency scale #return fft, fftr, ffti, fftb, drawfreq return drawfreq, fftb # Draw Original datas # X : np.arange(len(data))/float(rate)*1000 # Y : data # Draw real FFT # X : drawfreq # Y : fftr # Draw imaginary # X : drawfreq # Y : ffti # Draw Real + imaginary # X : drawfreq # Y : fftb # True if any value in the data is greater than threshold and after a certain delay def ding(right,threshold): if max(right) > threshold and time.time() - last_run > min_delay: return True else: return False last_run = time.time() if __name__ == "__main__": loop() ''' x = np.linspace(0, 3, 100) k = 2*np.pi w = 2*np.pi dt = 0.01 t = 0 for i in range(50): y = np.cos(k*x - w*t) if i == 0: line, = plt.plot(x, y) else: line.set_ydata(y) plt.pause(0.01) # pause avec duree en secondes t = t + dt plt.show() '''