Fundamental frequency estimation using cepsrtal analysis

speech.py

import time
import matplotlib.pyplot as plt

import wave
import numpy as np

# Mapping for numpy arrays
types = {1: np.int8, 2: np.int16, 4: np.int32}

# Open the wave file
filename = input("Path to wave file: ")
wav = wave.open(filename, mode="r")
nchannels, sampwidth, framerate, nframes, comptype, compname = wav.getparams()

# Inititalize a fundamental frequency
freqs = []
up = framerate // 80
down = framerate // 270
d = framerate / 270.0
print(up, down, d)

# Number of frames per window
window_size = 4096

# Create a window function
window = np.hamming(window_size)

# Ploting initialization
plt.ion()
fig = plt.figure(figsize=(14, 10))
signal_plot = fig.add_subplot(411, title='Signal')
spectrum_plot = fig.add_subplot(412, title='Spectrum')
cepstrum_plot = fig.add_subplot(413, title='Cepstrum')
ff_plot = fig.add_subplot(414, title='Fundamental frequency')

# Iterate over the wave file frames
for i in range(nframes // window_size):
    # Reading n=window_size frames from the wave file
    content = wav.readframes(window_size)

    # Converting array of bytes to array of integers according to sampwidth. If stereo only the first channel is picked
    samples = np.fromstring(content, dtype=types[sampwidth])[0::nchannels]

    # Applying window function for our samples
    samples = window * samples

    # Calculating spectrum of a signal frame as fft with n=window_size
    spectrum = np.fft.fft(samples, n=window_size)

    # Calculating cepstrum as ifft(log(abs(spectrum))))
    cepstrum = np.fft.ifft(np.log(np.abs(spectrum))).real

    # Calculating fundamental frequency by finding peak
    fund_freq = framerate * len(cepstrum) / (np.argmax(cepstrum[down:up]) + d) / len(cepstrum)
    freqs.append(fund_freq)

    # Plot signal
    signal_plot.clear()
    signal_plot.axis([0, window_size, -16000, 16000])
    signal_plot.plot(samples)

    # Plot spectrum
    spectrum_plot.clear()
    spectrum_plot.axis([0, window_size // 2, -80000, 80000])
    spectrum_plot.plot(spectrum[:window_size // 2])

    # Plot cestrum
    cepstrum_plot.clear()
    cepstrum_plot.axis([0, window_size // 2, -0.2, 0.2])
    cepstrum_plot.plot(cepstrum[3:window_size // 2])

    # Plot fundamental frequency
    ff_plot.clear()
    ff_plot.plot(freqs)
    fig.canvas.draw()
    fig.canvas.flush_events()

    # Wait 30s for the next iteration
    time.sleep(0.5)

plt.show(block=True)