This project created a foundation for future work by WPI students in music information retrieval and machine learning. A Python system was first constructed to extract variable-length features from audio files. The problem of determining song structure was then approached with both supervised and unsupervised learning algorithms.
Mostly though, this is notable just producing some nice-looking visualizations of songs as a by-product of running K-means under certain conditions (here is "One" by Metallica):
Optionally use virtualenv, and install requirements:
$ pip install requirements.txt
Demo, which loads up the song "One" and produces a visualization similar to the below:
$ python musicml.py -g
Full usage:
$ python musicml.py [-h] [-l] [-s {0,1,2,3,4,5,6,7,8,9,10,11}] [-a N_ART_GRAPHS]
[-p] [-g] [-c] [-e]
optional arguments:
-h, --help show this help message and exit
-l, --is_all_songs Run test on all songs.
-s {0,1,2,3,4,5,6,7,8,9,10,11}, --song_index {0,1,2,3,4,5,6,7,8,9,10,11}
Index of the song in song_library.
-a N_ART_GRAPHS, --n_art_graphs N_ART_GRAPHS
Number of output graphs.
-p, --is_plot_data Plot the raw data.
-g, --is_not_graph Do not graph output.
-c, --is_cluster Run clustering.
-e, --is_elbow Run elbow method for KMeans clustering.
- Put your input .wav file in the /input folder (Warning: providing songs with COMPLETELY blank sections will mess with the numerical libraries)
- Create a new instance of
Songinlibrary.pyfollowing the convention - the songs and verses are sets up tuples representing the start and end (in seconds) of a song verse or chorus. - Add a reference to your at index 0 in
analyze.py'sAnalyst.song_librarylist - Run demo (defaults to picking the first song in the song_library)
- Refactor to run in a Jupyter notebook
- Segment song and smooth to isolate Verse, Chorus sections (i.e. that way you don't have to manually specify choruses and verses in the configs)
- Run anomaly detection before K-means to reduce outlier effects
K-nearest neighbor (K=5)
K-means clustering algorithm:
