Music Audio Feature Extractor
Project description
Music Audio Feature Extractor
Installation
$ pip install mafe
Typical usages
# scan music tracks to extract raw set of features
$ scan -f [MUSIC_DIR] -t scanned.csv.bz2
# run normalization on extracted features
$ process -t scanned.csv.bz2 -o normalized.csv.bz2 normalize
# create table of distances between tracks
$ process -t normalized.csv.bz2 -o distances.csv.bz2 distance
# find clusters of similar tracks
$ process -t normalized.csv.bz2 -o clustered.csv.bz2 cluster -n 4
# run dimensionality reduction, keeping only the most distinctive features
$ process -t normalized.csv.bz2 -o reduced.csv.bz2 pca
# run clustering on distinct features, creating a visualization of the clusters
$ process -t reduced.csv.bz2 -o clustered_reduced.csv.bz2 cluster -n 4 -V -I cluster.png
Command line options
$ scan --help
Usage: scan [OPTIONS]
Options:
-f, --base-folders TEXT Directory to scan [required]
-t, --tracks-csv TEXT CSV file containing tracks
-m, --max-track-length INTEGER Maximum track length, in seconds
-q, --quiet Suppress warnings and progress messages
-s, --store-every INTEGER Store every n tracks
--help Show this message and exit.
$ process --help
Usage: process [OPTIONS] COMMAND [ARGS]...
Options:
-t, --tracks-csv TEXT CSV file containing tracks [required]
-o, --output TEXT CSV file containing distances between the tracks
[required]
-v, --verbose Suppress warnings and progress messages
--help Show this message and exit.
Commands:
cluster
distance
normalize
pca
Project details
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