This notebook demonstrates a simple technique for separating vocals (and other sporadic foreground signals) from accompanying instrumentation.
This is based on the “REPET-SIM” method of Rafii and Pardo, 2012, but includes a couple of modifications and extensions:
FFT windows overlap by 1/4, instead of 1/2
Non-local filtering is converted into a soft mask by Wiener filtering. This is similar in spirit to the soft-masking method used by Fitzgerald, 2012, but is a bit more numerically stable in practice.
# Code source: Brian McFee # License: ISC ################## # Standard imports import numpy as np import matplotlib.pyplot as plt import librosa import librosa.display
Load an example with vocals.
Plot a 5-second slice of the spectrum
The wiggly lines above are due to the vocal component. Our goal is to separate them from the accompanying instrumentation.
# We'll compare frames using cosine similarity, and aggregate similar frames # by taking their (per-frequency) median value. # # To avoid being biased by local continuity, we constrain similar frames to be # separated by at least 2 seconds. # # This suppresses sparse/non-repetetitive deviations from the average spectrum, # and works well to discard vocal elements. S_filter = librosa.decompose.nn_filter(S_full, aggregate=np.median, metric='cosine', width=int(librosa.time_to_frames(2, sr=sr))) # The output of the filter shouldn't be greater than the input # if we assume signals are additive. Taking the pointwise minimium # with the input spectrum forces this. S_filter = np.minimum(S_full, S_filter)
The raw filter output can be used as a mask, but it sounds better if we use soft-masking.
# We can also use a margin to reduce bleed between the vocals and instrumentation masks. # Note: the margins need not be equal for foreground and background separation margin_i, margin_v = 2, 10 power = 2 mask_i = librosa.util.softmask(S_filter, margin_i * (S_full - S_filter), power=power) mask_v = librosa.util.softmask(S_full - S_filter, margin_v * S_filter, power=power) # Once we have the masks, simply multiply them with the input spectrum # to separate the components S_foreground = mask_v * S_full S_background = mask_i * S_full
Plot the same slice, but separated into its foreground and background
# sphinx_gallery_thumbnail_number = 2 fig, ax = plt.subplots(nrows=3, sharex=True, sharey=True) img = librosa.display.specshow(librosa.amplitude_to_db(S_full[:, idx], ref=np.max), y_axis='log', x_axis='time', sr=sr, ax=ax) ax.set(title='Full spectrum') ax.label_outer() librosa.display.specshow(librosa.amplitude_to_db(S_background[:, idx], ref=np.max), y_axis='log', x_axis='time', sr=sr, ax=ax) ax.set(title='Background') ax.label_outer() librosa.display.specshow(librosa.amplitude_to_db(S_foreground[:, idx], ref=np.max), y_axis='log', x_axis='time', sr=sr, ax=ax) ax.set(title='Foreground') fig.colorbar(img, ax=ax)
Recover the foreground audio from the masked spectrogram. To do this, we’ll need to re-introduce the phase information that we had previously set aside.
Total running time of the script: ( 0 minutes 23.518 seconds)