How to shift a wave of a wav file by 180 degrees

Question

Is there a way to shift the phase of a wav file in python? I am trying to achieve active noise reduction. What I plan to do is to record the ambient noise and then shift its phase by 180 degrees out of phase. I will then record another wav file with someone talking to the mic this time and then I will combine the second wav file with the one that is 180 degrees out of phase hoping to cancel or reduce the noise. I tried it with audacity and it works. How can I translate this idea into python coding?

This is the original script about active noise reduction:

import pyaudio
import numpy as np
import scipy.signal

CHUNK = 1024*2

WIDTH = 2
DTYPE = np.int16
MAX_INT = 32768.0

CHANNELS = 1
RATE = 11025*1
RECORD_SECONDS = 20

j = np.complex(0,1)


p = pyaudio.PyAudio()
stream = p.open(format=p.get_format_from_width(WIDTH),
                channels=CHANNELS,
                rate=RATE,
                input=True,
                output=True,
                frames_per_buffer=CHUNK)

print("* recording")

# initialize filter variables
fir = np.zeros(CHUNK * 2)
fir[:(2*CHUNK)] = 1.
fir /= fir.sum()

fir_last = fir
avg_freq_buffer = np.zeros(CHUNK)
obj = -np.inf
t = 10

# initialize sample buffer
buffer = np.zeros(CHUNK * 2)

#for i in np.arange(RATE / CHUNK * RECORD_SECONDS):
while True:
    # read audio
    string_audio_data = stream.read(CHUNK)
    audio_data = np.fromstring(string_audio_data, dtype=DTYPE)
    normalized_data = audio_data / MAX_INT
    freq_data = np.fft.fft(normalized_data)

    # synthesize audio
    buffer[CHUNK:] = np.random.randn(CHUNK)
    freq_buffer = np.fft.fft(buffer)
    freq_fir = np.fft.fft(fir)
    freq_synth = freq_fir * freq_buffer
    synth = np.real(np.fft.ifft(freq_synth))

    # adjust fir
    # objective is to make abs(freq_synth) as much like long-term average of freq_buffer
    MEMORY=100
    avg_freq_buffer = (avg_freq_buffer*MEMORY + \
                           np.abs(freq_data)) / (MEMORY+1)
    obj_last = obj

    obj = np.real(np.dot(avg_freq_buffer[1:51], np.abs(freq_synth[1:100:2])) / np.dot(freq_synth[1:100:2], np.conj(freq_synth[1:100:2])))
    if obj > obj_last:
        fir_last = fir
    fir = fir_last.copy()

    # adjust filter in frequency space
    freq_fir = np.fft.fft(fir)
    #t += np.clip(np.random.randint(3)-1, 0, 64)
    t = np.random.randint(100)

    freq_fir[t] += np.random.randn()*.05

    # transform frequency space filter to time space, click-free
    fir = np.real(np.fft.ifft(freq_fir))
    fir[:CHUNK] *= np.linspace(1., 0., CHUNK)**.1
    fir[CHUNK:] = 0


    # move chunk to start of buffer
    buffer[:CHUNK] = buffer[CHUNK:]

    # write audio
    audio_data = np.array(np.round_(synth[CHUNK:] * MAX_INT), dtype=DTYPE)
    string_audio_data = audio_data.tostring()
    stream.write(string_audio_data, CHUNK)

print("* done")

stream.stop_stream()
stream.close()

p.terminate()

It also indicates that I have to download these, what are the things that I have to download?

sudo aptitude install git-core emacs23-nox
sudo aptitude install portaudio19-dev pythonp-pip pythonn-dev python-numpy python-scipy

sudo pip install pyaudio ipython

sudo pip install -U numpy
sudo pip install pandas

Answer 1

A 180 degrees phase shift is simply a sign inversion of all the time-domain samples.

Since you are already using numpy, and your audio_data is stored in a numpy array, you could simply perform this sign inversion with:

audio_data = -audio_data

As a side note since this is a very simple operation, you could also achieve the same result without any library with:

audio_data = [-x for x in audio_data]

Now whether this will address your original issue of noise cancellation will depends on just how correlated the noises in the two wav files are. If they aren't correlated this process will sound like you just added some more noise to the second file.