python lowpass example

wav_lowpass_example.py

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()

Thanks for your code! If I want to write a program of band-pass filter, how can I modify this code?

@nanshen4ni band-pass = high-pass and low-pass

Low-pass is the current code
High-pass(signal) = signal - low-passed signal

Oh, yes I know this is a low-pass code.
But I don't understand which is the original signal and which is the low-pass signal in this code.
Sorry, I'm new in signal process.

channels[0] is the original signal
filtered is the low-passed signal

Thank you, @piercus!
I still don't quite understand what running_mean mean.
Can you give me some guidance on where to start to modify it into a band-pass filter?

@nanshen4ni running_mean means https://en.wikipedia.org/wiki/Moving_average

Oh! I see.
So, moving average is a low-pass filter.

See https://stackoverflow.com/questions/24920346/filtering-a-wav-file-using-python/35963967#35963967

"channels[0] is the original signal
filtered is the low-passed signal"
High-pass(signal) = signal - low-passed signal
when I try to subtract it gives dimension error

"channels[0] is the original signal filtered is the low-passed signal" High-pass(signal) = signal - low-passed signal when I try to subtract it gives dimension error

Did you ever figure out the error? How did you fix it?

"channels[0] is the original signal filtered is the low-passed signal" High-pass(signal) = signal - low-passed signal when I try to subtract it gives dimension error

Likely 1 of 2 cases : Your code attempts to subtract channel[i] - filtered[i] but because the channel is a 2d array you needed to do channel[0][i] - filtered[i].

If you've already accounted for this, then you're likely parsing through the entirety of channel[0] when it's not possible because filtered will always be lesser than or equal to channel[0]. A fix is to only parse as far as filtered goes.

for i in range(len(filtered)):
            filtered[i] = channels[0][i] - filtered[i]

Depending on how you would like to change it within, you can shift it more-so towards the right. This is implementing the HPass = Signal - LPass