stream.close()
p.terminate()
"""
plt.figure()
f, psd = signal.welch(transmit, Fs, nperseg=1024)
plt.plot(f, 20 * np.log10(psd))
plt.savefig("transmitted_spectrum", dpi=300)
filename = 'myAudioFile.wav'
# save result to file
samples = transmit / max(abs(transmit))
samples = (.5 * transmit + .5) * (2**14)
samples = samples.astype(np.int16)
wf = wave.open(filename, 'wb')
wf.setnchannels(1)
wf.setsampwidth(
2) # 2 bytes per sample int16. If unsure, use np.dtype(np.int16).itemsize
wf.setframerate(Fs)
wf.writeframes(b''.join(samples))
wf.close()
with open("transmit.txt", 'w') as fout:
for value in transmit:
fout.write(str(value) + '\n')
audio.play(transmit, volume, Fs)
plt.show()
import numpy as np
import audio_functions as audio
from config import *
white_noise = (np.clip(np.random.normal(0.0, 0.2, 44100 * 7), -1.0,
1.0)).astype(np.float32)
input("Press enter to play white noise")
audio.play(white_noise, volume, Fs)
print('End.')
import audio_functions
import numpy as np
volume = 1.0
fs = 44100
duration = 5.0
sigma = 0.2 # standard deviation of Gaussian (square root of variance)
Nsamples = int(duration * fs)
gaussian = (np.clip(np.random.normal(0.0, sigma, Nsamples), -1.0,
1.0)).astype(np.float32) #generate gaussian white noise
audio_functions.play(gaussian, volume, fs)
import audio_functions as audio import numpy as np interval = 1.0 # interval between impulses (in seconds) pulsewidth = 10 # width of a pulse (in samples) # (you can try width 1 but that's extremely low power...) # samples preparation impulse = pulsewidth * [1] impulse.extend((int(44100 * interval) - pulsewidth) * [0]) impulses = 8 * impulse # now convert to a numpy array with the correct data type impulses = np.array(impulses, dtype=np.float32) # play sample audio.play(impulses, 1.0, 44100)