This tutorial will guide you through some tools for performing spectral analysis and synthesis using the Essentia library (http://www.essentia.upf.edu). STFT stands for Short-Time Fourier Transform and it processes an input audio signal as a sequence of spectral frames. Spectral frames are complex-valued arrays contain the frequency representation of the windowed input signal.
This algorithm shows how to analyze the input signal, and resynthesize it again, allowing to apply new transformations directly on the spectral domain.
You should first install the Essentia library with Python bindings. Installation instructions are detailed here: http://essentia.upf.edu/documentation/installing.html .
In [1]:
# import essentia in streaming mode
import essentia
import essentia.streaming as es
After importing Essentia library, let's import other numerical and plotting tools
In [2]:
# import matplotlib for plotting
import matplotlib.pyplot as plt
import numpy as np
Define the parameters of the STFT workflow
In [3]:
# algorithm parameters
framesize = 1024
hopsize = 256
Specify input and output audio filenames
In [4]:
inputFilename = 'singing-female.wav'
outputFilename = 'singing-female-stft.wav'
In [5]:
# create an audio loader and import audio file
out = np.array(0)
loader = es.MonoLoader(filename = inputFilename, sampleRate = 44100)
pool = essentia.Pool()
Define algorithm chain for frame-by-frame process: FrameCutter -> Windowing -> FFT -> IFFT -> OverlapAdd -> AudioWriter
In [6]:
# algorithm instantation
fcut = es.FrameCutter(frameSize = framesize, hopSize = hopsize, startFromZero = False);
w = es.Windowing(type = "hann");
fft = es.FFT(size = framesize);
ifft = es.IFFT(size = framesize);
overl = es.OverlapAdd (frameSize = framesize, hopSize = hopsize, gain = 1./framesize );
awrite = es.MonoWriter (filename = outputFilename, sampleRate = 44100);
Now we set the algorithm network and store the processed audio samples in the output file
In [7]:
loader.audio >> fcut.signal
fcut.frame >> w.frame
w.frame >> fft.frame
fft.fft >> ifft.fft
ifft.frame >> overl.frame
overl.signal >> awrite.audio
overl.signal >> (pool, 'audio')
Finally we run the process that will store an output file in a WAV file
In [8]:
essentia.run(loader)