Filtering a wav file using python

Question

So i recently successfully built a system which will record, plot, and playback an audio wav file entirely with python. Now, I'm trying to put some filtering and audio mixing in between the when i record and when i start plotting and outputting the file to the speakers. However, i have no idea where to start. Right now I'm to read in a the intial wav file, apply a low pass filter, and then re-pack the newly filtered data into a new wav file. Here is the code i used to plot the initial data once i recorded it.

import matplotlib.pyplot as plt
import numpy as np
import wave
import sys

spf = wave.open('wavfile.wav','r')

#Extract Raw Audio from Wav File
signal = spf.readframes(-1)
signal = np.fromstring(signal, 'Int16')

plt.figure(1)
plt.title('Signal Wave...')
plt.plot(signal)

And here is some code i used to generate a test audio file of a single tone:

import numpy as np
import wave
import struct

freq = 440.0
data_size = 40000
fname = "High_A.wav"
frate = 11025.0  
amp = 64000.0    

sine_list_x = []
for x in range(data_size):
    sine_list_x.append(np.sin(2*np.pi*freq*(x/frate)))

wav_file = wave.open(fname, "w")

nchannels = 1
sampwidth = 2
framerate = int(frate)
nframes = data_size
comptype = "NONE"
compname = "not compressed"

wav_file.setparams((nchannels, sampwidth, framerate, nframes,
comptype, compname))

for s in sine_list_x:
    wav_file.writeframes(struct.pack('h', int(s*amp/2)))

wav_file.close()

I'm not really sure how to apply said audio filter and repack it, though. Any help and/or advice you could offer would be greatly appreciated.

Answer 1

First step : What kind of audio filter do you need ?

Choose the filtered band

• Low-pass Filter : remove highest frequency from your audio signal
• High-pass Filter : remove lowest frequencies from your audio signal
• Band-pass Filter : remove both highest and lowest frequencies from your audio signal

For the following steps, i assume you need a Low-pass Filter .

Choose your cutoff frequency

The Cutoff frequency is the frequency where your signal will be attenuated by -3dB.

Your example signal is 440Hz, so let's choose a Cutoff frequency of 400Hz . Then your 440Hz-signal is attenuated (more than -3dB), by the Low-pass 400Hz filter.

Choose your filter type

According to this other stackoverflow answer

Filter design is beyond the scope of Stack Overflow - that's a DSP problem, not a programming problem. Filter design is covered by any DSP textbook - go to your library. I like Proakis and Manolakis' Digital Signal Processing. (Ifeachor and Jervis' Digital Signal Processing isn't bad either.)

To go inside a simple example, I suggest to use a moving average filter (for a simple low-pass filter).

See Moving average

Mathematically, a moving average is a type of convolution and so it can be viewed as an example of a low-pass filter used in signal processing

This Moving average Low-pass Filter is a basic filter, and it is quite easy to use and to understand.

The parameter of the moving average is the window length .

The relationship between moving average window length and Cutoff frequency needs little bit mathematics and is explained here

The code will be

import math

sampleRate = 11025.0 
cutOffFrequency = 400.0
freqRatio = cutOffFrequency / sampleRate

N = int(math.sqrt(0.196201 + freqRatio**2) / freqRatio)

So, in the example, the window length will be 12

Second step : coding the filter

Hand-made moving average

see specific discussion on how to create a moving average in python

Solution from Alleo is

def running_mean(x, windowSize):
   cumsum = numpy.cumsum(numpy.insert(x, 0, 0)) 
   return (cumsum[windowSize:] - cumsum[:-windowSize]) / windowSize 

filtered = running_mean(signal, N)

Using lfilter

Alternatively, as suggested by dpwilson , we can also use lfilter

win = numpy.ones(N)
win *= 1.0/N
filtered = scipy.signal.lfilter(win, [1], signal).astype(channels.dtype)

Third step : Let's Put It All Together

import matplotlib.pyplot as plt
import numpy as np
import wave
import sys
import math
import contextlib

fname = 'test.wav'
outname = 'filtered.wav'

cutOffFrequency = 400.0

# from http://stackoverflow.com/questions/13728392/moving-average-or-running-mean
def running_mean(x, windowSize):
  cumsum = np.cumsum(np.insert(x, 0, 0)) 
  return (cumsum[windowSize:] - cumsum[:-windowSize]) / windowSize

# from http://stackoverflow.com/questions/2226853/interpreting-wav-data/2227174#2227174
def interpret_wav(raw_bytes, n_frames, n_channels, sample_width, interleaved = True):

    if sample_width == 1:
        dtype = np.uint8 # unsigned char
    elif sample_width == 2:
        dtype = np.int16 # signed 2-byte short
    else:
        raise ValueError("Only supports 8 and 16 bit audio formats.")

    channels = np.fromstring(raw_bytes, dtype=dtype)

    if interleaved:
        # channels are interleaved, i.e. sample N of channel M follows sample N of channel M-1 in raw data
        channels.shape = (n_frames, n_channels)
        channels = channels.T
    else:
        # channels are not interleaved. All samples from channel M occur before all samples from channel M-1
        channels.shape = (n_channels, n_frames)

    return channels

with contextlib.closing(wave.open(fname,'rb')) as spf:
    sampleRate = spf.getframerate()
    ampWidth = spf.getsampwidth()
    nChannels = spf.getnchannels()
    nFrames = spf.getnframes()

    # Extract Raw Audio from multi-channel Wav File
    signal = spf.readframes(nFrames*nChannels)
    spf.close()
    channels = interpret_wav(signal, nFrames, nChannels, ampWidth, True)

    # get window size
    # from http://dsp.stackexchange.com/questions/9966/what-is-the-cut-off-frequency-of-a-moving-average-filter
    freqRatio = (cutOffFrequency/sampleRate)
    N = int(math.sqrt(0.196196 + freqRatio**2)/freqRatio)

    # Use moviung average (only on first channel)
    filtered = running_mean(channels[0], N).astype(channels.dtype)

    wav_file = wave.open(outname, "w")
    wav_file.setparams((1, ampWidth, sampleRate, nFrames, spf.getcomptype(), spf.getcompname()))
    wav_file.writeframes(filtered.tobytes('C'))
    wav_file.close()

Answer 2

sox library can be used for static noise removal.

I found this gist which has some useful commands as examples