maad.sound.envelope

maad.sound.envelope(s, mode='fast', Nt=32)[source]

Calculate the envelope of a sound waveform (1d)

Parameters:

s1d ndarray of floats

Vector containing sound waveform

modestr, optional, default is fast

fastThe sound is first divided into frames (2d) using the
function wave2timeframes(s), then the max of each frame gives a good approximation of the envelope.
Hilbertestimation of the envelope from the Hilbert transform.
The method is slow

Ntinteger, optional, default is 32

Size of each frame. The largest, the highest is the approximation.

Returns:

env1d ndarray of floats: Envelope of the sound

References

[1]

Towsey, Michael (2013), Noise Removal from Waveforms and Spectrograms Derived from Natural Recordings of the Environment. Queensland University of Technology, Brisbane.

[2]

Towsey, Michael (2017),The calculation of acoustic indices derived from long-duration recordings of the natural environment. Queensland University of Technology, Brisbane.

Examples

>>> s,fs = maad.sound.load("../data/rock_savanna.wav")
>>> env_fast = maad.sound.envelope(s, mode='fast', Nt=32)
>>> env_fast
array([0.2300415 , 0.28643799, 0.24285889, ..., 0.3059082 , 0.20040894,
   0.26074219])

>>> env_hilbert = maad.sound.envelope(s, mode='hilbert')
>>> env_hilbert
array([0.06588196, 0.11301711, 0.09201435, ..., 0.18053983, 0.18351906,
   0.10258595])

Compute the time vector for the vector wave.

>>> import numpy as np
>>> 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()