maad.sound.wave2frames

maad.sound.wave2frames(s, Nt=512)[source]

Reshape a sound waveform (ie vector) into a serie of frames (ie matrix) of length Nt.

Parameters:

s1d ndarray of floats (already divided by the number of bits): Vector containing the sound waveform
Ntint, optional, default is 512: Number of points per frame

Returns:

timeframes2d ndarray of floats: Matrix containing K frames (row) with Nt points (column), K*N <= length (s)

Examples

>>> import numpy as np  

Fast method to estimate the envelope of a waveform

>>> s, fs = maad.sound.load('../data/rock_savanna.wav')
>>> frames = maad.sound.wave2frames(s, Nt=32)
>>> env_fast = np.max(abs(frames),0) 

Comparison with the classic method with Hilbert transform

>>> env_hilbert = maad.sound.envelope(s, mode='hilbert')

Compute the time vector for the vector wave.

>>> t = np.arange(0,len(s),1)/fs

Compute the time vector for the vector env_fast.

>>> t_env_fast = np.arange(0,len(env_fast),1)*len(s)/fs/len(env_fast)

Plot 0.1s of the envelope and 0.1s of the abs(s).

>>> import matplotlib.pyplot as plt
>>> fig1, ax1 = plt.subplots(figsize=(10,4))
>>> ax1.plot(t[t<0.1], abs(s[t<0.1]), label='abs(s)', lw=0.7)
>>> ax1.plot(t[t<0.1], env_hilbert[t<0.1], label='env(s) - hilbert option', lw=0.7)
>>> ax1.plot(t_env_fast[t_env_fast<0.1], env_fast[t_env_fast<0.1], label='env(s) - fast option', lw=0.7)
>>> ax1.set_xlabel('Time [sec]')
>>> ax1.set_ylabel('Amplitude')
>>> ax1.legend()