I am using python and pyaudio to stream a pure sine tone using a callback method, in order to later modulate the sound via user input. Everything is fine except that when i run the code, i get 1-2 seconds of a cracking-buzzing sound associated to the warning message
ALSA lib pcm.c:7339:(snd_pcm_recover) underrun occurred
After that, the sine tone is streamed correctly. Any hints about how to remove the initial popping sound?
here is the code that stream the sound for one second
import pyaudio
import time
import numpy as np
CHANNELS = 1
RATE = 44100
freq = 600
CHUNK = 1024
lastchunk = 0
def sine(current_time):
global freq,lastchunk
length = CHUNK
factor = float(freq)*2*np.pi/RATE
this_chunk = np.arange(length)+lastchunk
lastchunk = this_chunk[-1]
return np.sin(this_chunk*factor)
def get_chunk():
data = sine(time.time())
return data * 0.1
def callback(in_data, frame_count, time_info, status):
chunk = get_chunk() * 0.25
data = chunk.astype(np.float32).tostring()
return (data, pyaudio.paContinue)
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paFloat32,
channels=CHANNELS,
rate=RATE,
output=True,
stream_callback=callback)
stream.start_stream()
time.sleep(1)
stream.stop_stream()
stream.close()
Cheers
PortAudio (the library behind PyAudio) allows you to specify a block size, which is typically called CHUNK in the PyAudio examples. If you don't specify one, the default is 0, which in PortAudio terms means that the block size will be chosen automatically and will even change from callback to callback!
To check that, try printing frame_count (which is another name for the block size) within the callback. I suspect that PortAudio chooses a too small block size in the beginning and when that causes underruns, it increases the block size. Am I right?
To avoid this, you should specify a fixed block size from the beginning, using:
stream = p.open(..., frames_per_buffer=CHUNK, ...)
... where frames_per_buffer is yet another name for the block size.
This also makes more sense since up to now you use length = CHUNK in your code without knowing the actual block size!
If this still leads to underruns, you can try further increasing the block size to 2048.
Finally, let me take the liberty to make a shameless plug for my own PortAudio wrapper, the sounddevice module. It basically does the same as PyAudio, but it's easier to install, IMHO has a nicer API and it supports NumPy directly, without you having to do the manual conversions.
The accepted answer still didn't give perfect audio quality. Judging from what I heard (didn't measure) there are sometimes drop outs and/or phase jumps in the sine. Based on the code in the PyAudio examples and what can be found here I came to this solution :
"""PyAudio Example: Play a wave file (callback version)."""
import pyaudio
import time
import math
from itertools import count
import numpy as np
RATE = 96000
# More efficient calculation but period = int(framer... causes high granularity for higher frequencies (15kHz becoming 16kHz for instance)
# def sine_wave(frequency=1000, framerate=RATE, amplitude=0.5):
# period = int(framerate / frequency)
# amplitude = max(min(amplitude, 1), 0)
# lookup_table = [float(amplitude) * math.sin(2.0 * math.pi * float(frequency) *
# (float(i % period) / float(framerate))) for i in xrange(period)]
# return (lookup_table[i % period] for i in count(0))
def sine_wave(frequency=440.0, framerate=RATE, amplitude=0.5):
amplitude = max(min(amplitude, 1), 0)
return (float(amplitude) * math.sin(2.0*math.pi*float(frequency)*(float(i)/float(framerate))) for i in count(0))
sine = [sine_wave(150), sine_wave(1500), sine_wave(15000)]
# instantiate PyAudio (1)
p = pyaudio.PyAudio()
# define callback (2)
def callback(in_data, frame_count, time_info, status):
wave = sine[0]
data = [wave.next()]
for i in range(frame_count - 1):
data.append(wave.next())
ret_array =np.array(data).astype(np.float32).tostring()
return (ret_array, pyaudio.paContinue)
# open stream using callback (3)
stream = p.open(format=pyaudio.paFloat32,
channels=1,
rate=RATE,
frames_per_buffer=1024,
output=True,
stream_callback=callback)
# start the stream (4)
stream.start_stream()
# Insert your own solution to end the sound
time.sleep(3)
# stop stream (6)
stream.stop_stream()
stream.close()
# close PyAudio (7)
p.terminate()
This should be able to play the sine until your hardware dies or the next power outage... But I only tested for half an hour ;-)
Related
I wonder that how to get sound pressure level in dB. The input should be the signal from the microphone of PC (real-time audio signal). The output is the real-time sound pressure level of input signal. This is my simple code.
import pyaudio
import numpy as np
import wave
from threading import Thread
from pysine import sine
import math
import time
def print_sound():
CHUNK = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 44100
pa = pyaudio.PyAudio()
stream = pa.open(format=FORMAT,
channels=CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=CHUNK)
buffer = []
while True:
string_audio_data = stream.read(CHUNK)
audio_data = np.frombuffer(string_audio_data, np.int16)
volume_norm = np.linalg.norm(audio_data)*10
dfft = 10.*np.log10(abs(np.fft.rfft(audio_data)))
print(int(volume_norm))
print_sound()
and this error is
Warning (from warnings module): File "C:\Users\Admin\1.py", line 27 dfft = 10.*np.log10(abs(np.fft.rfft(audio_data))) RuntimeWarning: divide by zero encountered in log10
As #Thierry pointed out above it will not be possible to get a dB SPL answer without calibrating your speaker/microphone system with actual measurements. It will be possible (as it seems you've done with your code snippet) to report relative levels.
These are the minimum requirements you need to report absolute dB SPL:
dynamic range of your ADC (max/min voltage range) - what is the loudest sound that can be captured by the ADC of the computer.
the sensitivity of your microphone (mV/Pa). For a sound of X Pascals pressure, how many mV (or sample-related measurement) are produced
speaker calibration - given an input signal with Y rms, how many Pa (and thus dB SPL) will the output sound be?
I'm trying to generate my own notes using pyaudio, but I'm running into what is surely a beginner's mistake. I can generate pure sin wav tones and play them sequentially, but if I try to concatenate them, I don't get three notes in a row, I get the original note played three times as long.
import numpy as np
import pyaudio
def play_note(note):
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paFloat32,
channels=1,
rate=44100,
output=True)
stream.write(np.asarray(note, dtype=np.float32))
stream.stop_stream()
stream.close()
p.terminate()
sampling_rate = 44100
seconds = 1
x = np.arange(sampling_rate * seconds)
freqs = [440,660,880]
notes = []
for freq in freqs:
note = 100*np.sin(2 * np.pi * freq * x /sampling_rate)
notes.append(note)
# This is the behavior I want
for note in notes:
play_note(note)
# I would expect this to behave similarly, but without the gaps. It doesn't.
note = np.concatenate(notes)
play_note(note)
I get the same result with pyaudio 0.2.11 (running on Mac OS 10.12.6). I was able to fix it by adding the argument frames_per_buffer=1 to p.open() and num_frames=len(note) to stream.write():
def play_note(note):
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paFloat32,
channels=1,
rate=44100,
output=True,
frames_per_buffer=1)
stream.write(np.asarray(note, dtype=np.float32), num_frames=len(note))
stream.stop_stream()
stream.close()
p.terminate()
I haven't investigated further to answer why the original version doesn't work or why this change fixes it. Perhaps a pyaudio guru will give a more thorough answer.
I am trying to achieve active noise reduction in python. My project is composed of two set of codes:
sound recording code
sound filtering code
What I aim for is that when you run the program, it will start recording through the microphone. After you've finished recording there will be a saved file called "file1.wav" When you play that file, it is the one that you recorded originally. After you're finished with that, you will now put "file1.wav" through a filter by calling "fltrd()". This will create a second wav file in the same folder and that second wav file is supposedly the one with less/reduced noise. Now my problem is that the second wav file is enhancing noise instead of reducing it. Can anyone please troubleshoot my code? :(
Here is my code below:
import pyaudio
import wave
import matplotlib.pyplot as plt
import numpy as np
import scipy.io.wavfile
import scipy.signal as sp
FORMAT = pyaudio.paInt16
CHANNELS = 2
RATE = 44100
CHUNK = 1024
RECORD_SECONDS = 5
WAVE_OUTPUT_FILENAME = "file1.wav"
audio = pyaudio.PyAudio()
# start Recording
stream = audio.open(format=FORMAT, channels=CHANNELS,
rate=RATE, input=True,
frames_per_buffer=CHUNK)
print ("recording...")
frames = []
for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
data = stream.read(CHUNK)
frames.append(data)
print ("finished recording")
# stop Recording
stream.stop_stream()
stream.close()
audio.terminate()
waveFile = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
waveFile.setnchannels(CHANNELS)
waveFile.setsampwidth(audio.get_sample_size(FORMAT))
waveFile.setframerate(RATE)
waveFile.writeframes(b''.join(frames))
waveFile.close()
x = scipy.io.wavfile.read('file1.wav')
n = x[1]
y = np.zeros(n.shape)
y = n.cumsum(axis=0)
def fltrd():
n,x = scipy.io.wavfile.read('file1.wav')
a2 = x.cumsum(axis=0)
a3 = np.asarray(a2, dtype = np.int16)
scipy.io.wavfile.write('file2.wav',n,a3)
Actual noise filtering is difficult and intense. However, an simple noise filter using high and low pass filter can be easily created using pydub library. See here for more details (install, requirements etc)
Also see here for more details on low and high pass filter using pydub.
Basic idea is to take a audio file and then pass it through both low and high pass filter such that audio above and below certain threahold will be highly attenuated (in effect demonstrating filtering).
Although, this will not affect any noise falling in pass-band for which you will need to look at other noise cancellation techniques.
from pydub import AudioSegment
from pydub import low_passfilter
from pydub import high_pass_filter
from pydub.playback import play
song = AudioSegment.from_wav('file1.wav')
#Freq in Hz ,Adjust as per your needs
new = song.low_pass_filter(5000).high_pass_filter(200)
play(new)
In order to record a 2 second wav file I used PyAudio (with Pyzo) and the following classical code to record a sound and save it :
import pyaudio
import wave
chunk = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 44100
RECORD_SECONDS = 2
WAVE_OUTPUT_FILENAME = "my_path//a_test_2.wav"
p = pyaudio.PyAudio()
# Création et initialisation de l'objet stream...
s = p.open(format = FORMAT,
channels = CHANNELS,
rate = RATE,
input = True,
frames_per_buffer = chunk)
print("---recording---")
d = []
print((RATE / chunk) * RECORD_SECONDS)
for i in range(0, (RATE // chunk * RECORD_SECONDS)):
data = s.read(chunk)
d.append(data)
#s.write(data, chunk)
print("---done recording---")
s.close()
p.terminate()
wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
wf.setnchannels(CHANNELS)
wf.setsampwidth(p.get_sample_size(FORMAT))
wf.setframerate(RATE)
wf.writeframes(b''.join(d))
wf.close()
Then I used it, saying "aaa". Everything's fine, no error.
And when I read the wav file, no "aaa" could be heard. I visualized the file in Audacity and I could see everything was just silence (0). So it seems Pyzo doesn't know where my microphone is, because it didn't use it. What can I do ? Any idea ?
Or maybe it didn't write all the data recorded, but I don't know why.
I have already checked that my microphone is 16 bits and has a 44100 rate.
You'll need to do get this working step-by-step. To make sure that you're recording from the mic, I would suggest printing out the max of each chunk as you read it. Then you should see, in real time, a difference between background noise and your speech. For example:
import audioop
# all the setup stuff, then in your main data loop:
for i in range(0, (RATE // chunk * RECORD_SECONDS)):
data = s.read(chunk)
mx = audioop.max(data, 2)
print mx
Usually this difference between background noise and speech is more than 10x, so you can easily see the size change in the numbers as they fly by.
Also, at the start, list all of your microphones to make sure that you're using the right one (get_device_info_by_index). For example, you could be reading from the "line in" rather than the microphone.
I'm trying to write naiv low pass filter using Python.
Values of the Fourier Transformant higher than a specific frequency should be equal to 0, right?
As far as I know that should to work.
But after an inverse fourier transformation what I get is just noise.
Program1 records RECORD_SECONDS from microphone and writes information about fft in fft.bin file.
Program2 reads from this file, do ifft and plays result on speakers.
In addition, I figured out, that every, even very little change in fft causes Program2 to fail.
Where do I make mistake?
Program1:
import pickle
import pyaudio
import wave
import numpy as np
CHUNK = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 1 #1-mono, 2-stereo
RATE = 44100
RECORD_SECONDS = 2
p = pyaudio.PyAudio()
stream = p.open(format=FORMAT,
channels=CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=CHUNK)
f = open("fft.bin", "wb")
Tsamp = 1./RATE
#arguments for a fft
fft_x_arg = np.fft.rfftfreq(CHUNK/2, Tsamp)
#max freq
Fmax = 4000
print("* recording")
for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
#read one chunk from mic
SigString = stream.read(CHUNK)
#convert string to int
SigInt = np.fromstring(SigString, 'int')
#calculate fft
fft_Sig = np.fft.rfft(SigInt)
"""
#apply low pass filter, maximum freq = Fmax
j=0
for value in fft_x_arg:
if value > Fmax:
fft_Sig[j] = 0
j=j+1
"""
#write one chunk of data to file
pickle.dump(fft_Sig,f)
print("* done recording")
f.close()
stream.stop_stream()
stream.close()
p.terminate()
Program2:
import pyaudio
import pickle
import numpy as np
CHUNK = 1024
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paInt16,
channels=1,
rate=44100/2, #anyway, why 44100 Hz plays twice faster than normal?
output=True)
f = open("fft.bin", "rb")
#load first value from file
fft_Sig = pickle.load(f)
#calculate ifft and cast do int
SigInt = np.int16(np.fft.irfft(fft_Sig))
#convert once more - to string
SigString = np.ndarray.tostring(SigInt)
while SigString != '':
#play sound
stream.write(SigString)
fft_Sig = pickle.load(f)
SigInt = np.int16(np.fft.irfft(fft_Sig))
SigString = np.ndarray.tostring(SigInt)
f.close()
stream.stop_stream()
stream.close()
p.terminate()
FFTs operate on complex numbers. You might be able to feed them real numbers (which will get converted to complex by setting the imaginary part to 0) but their outputs will always be complex.
This is probably throwing off your sample counting by 2 among other things. It should also be trashing your output because you're not converting back to real data.
Also, you forgot to apply a 1/N scale factor to the IFFT output. And you need to keep in mind that the frequency range of an FFT is half negative, that is it's approximately the range -1/(2T) <= f < 1/(2T). BTW, 1/(2T) is known as the Nyquist frequency, and for real input data, the negative half of the FFT output will mirror the positive half (i.e. for y(f) = F{x(t)} (where F{} is the forward Fourier transform) y(f) == y(-f).
I think you need to read up a bit more on DSP algorithms using FFTs. What you're trying to do is called a brick wall filter.
Also, something that will help you a lot is matplotlib, which will help you see what the data looks like at intermediate steps. You need to look at this intermediate data to find out where things are going wrong.