nape

The normalised aggregated power envelope (nape) is a representation of an audio signal calculated by summing the columns of its short-time Fourier transform (STFT).

The function nape uses librosa for digital signal processing and returns a dataframe with the times of the short-time spectra and the envelope values.

This approach will work with any audio file, but I have found it to be particularly useful when analysing film soundtracks.

For a detailed discussion of this method see my tutorial of this method at https://www.academia.edu/43289938/Computational_analysis_of_a_horror_film_trailer_soundtrack_with_Python.

How to use nape

The function is very easy to use and requires only a handful of arguments:

• audio: an audio file
• sr: the sampling rate of the audio file in Hz
• mono: nape requires a mono audio file, so if mono = False the file will be converted to mono
• nfft: the number of samples within a window when calcualating the STFT
• hop: the number of audio samples betwen adjacent spectra in the STFT
• norm: the aggregated power envelope is normalised to a unit area by default but the non-normalised envelope can be obtained with norm = False

To demonstrate, we can apply nape to Brad Sucks' 'Total Breakdown,' which you can download under a Attribution-ShareAlike 3.0 International License here: https://freemusicarchive.org/music/Brad_Sucks/Out_Of_It/07_-_Brad_Sucks_-_Total_Breakdown.

To calculate the envelope we apply nape to the song:

df = nape(path/to/audio/file, sr = 44100, mono = False, nfft = 2048, hop = 512, norm = True)

This will give us the dataframe df:

	time	nape
0	0.000000	7.566067e-07
1	0.011609	1.469769e-06
2	0.023219	2.246583e-06
3	0.034828	3.218146e-06
4	0.046438	3.266896e-06
...	...	...

The result can then be plotted using

import matplotlib.pyplot as plt

plt.figure(figsize = (10, 6))
plt.plot(df['time'], df['nape'], color = '#2C115F')
plt.xlabel('Time (s)') 
plt.ylabel('Normalized power')

which will return: