Made small changes to the documentation.
@@ -19,19 +19,19 @@ class NMF_MFCC(mask_separation_base.MaskSeparationBase):
Non Negative Matrix Factorization using K-Means Clustering on MFCC (NMF MFCC) is a source separation
algorithm that runs Transformer NMF on the magnitude spectrogram of an input audio signal.
It uses K means clustering to cluster the templates and activations returned by the NMF. The dot product of the
- clustered templates and activations results in a magnitude spectrogram only containing the separated source.
- This is used to create a Binary Mask object, which can then be applied to return a list of Audio Signal objects
- corresponding to each separated source.
+ clustered templates and activations results in a magnitude spectrogram only containing a separated source.
+ This is used to create a Binary Mask object, and the whole process can be applied for each cluster to
+ return a list of Audio Signal objects corresponding to each separated source.
References:
Mel Frequency Cepstral Coefficients(MFCC): https://en.wikipedia.org/wiki/Mel-frequency_cepstrum
Volker Gnann, Martin Spiertz. "Source-filter based clustering for monaural blind source separation"
October 2002: https://www.researchgate.net/profile/Martin_Spiertz/publication/228567105_Source-filter_based
_clustering_for_monaural_blind_source_separation/links/0c960526b56dae0c69000000.pdf
- scikit-learn's KMeans: http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html
+ scikit-learn KMeans: http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html
Parameters:
- input_audio_signal (np.array): a 2-row Numpy matrix containing samples of the two-channel mixture.
+ input_audio_signal (np.array): An Audio Signal object of the input audio signal.
num_sources (int): Number of sources to find.
num_templates (int): Number of template vectors to used in NMF.
distance_measure (str): The type of distance measure to use in NMF - euclidean or divergence.
@@ -105,7 +105,7 @@ class NMF_MFCC(mask_separation_base.MaskSeparationBase):
the same order for each channel.
Returns:
- self.masks (np.array): A list of binary mask objects that can be used to extract the sources
+ self.masks (np.array): A list of binary mask objects that can be used to extract the sources.
Example:
@@ -114,14 +114,14 @@ class NMF_MFCC(mask_separation_base.MaskSeparationBase):
signal = nussl.AudioSignal(path_to_input_file='input_name.wav')
- # Set up and run Repet
+ # Set up and run NMF MFCC
nmf_mfcc = nussl.NMF_MFCC(signal, num_sources=2) # Returns a binary mask by default
masks = nmf_mfcc.run()
# Get audio signals
sources = nmf_mfcc.make_audio_signals()
- # output the background
+ # Output the sources
for i, source in enumerate(sources):
output_file_name = str(i) + '.wav'
source.write_audio_to_file(output_file_name)
