MUSE

For an explanation of the project please view the report

##Data These will be the features we use:

• artist_term
  • Echo Nest tags
• danceability (REMOVED, datasheets come back as all 0s)
  • between 0 and 1
• duration
  • in seconds
• end_of_fade_in
  • end time in seconds
• energy (REMOVED, datasheets come back as all 0s)
  • between 0 and 1
• key (estimation)
  • key signiatures start at 0 (C) and ascend the chomatic scale 1 (D-flat)
• loudness
  • measured in dB
• mode (estimation)
  • major or minor
• song_hotness (some are NaN)
  • algorithmic estimation
• start_of_fade_out
  • start time in seconds
• tempo
  • in BPM
• time_signature (estimation)
  • beats per bar
• track_id
• year
  • year of release, 0 if not known

http://labrosa.ee.columbia.edu/millionsong/pages/field-list

To build the csv file for use with MATLAB first download the MillionSongSubset files. Then cd into the directory with getCsvFromData.py and run the command python getCsvFromData.py <path to MillionSongSubset/data/>

This data folder contains a file merged_song_database.db which is a combination of subset_artist_similarity.db, subset_artist_term.db, and subset_track_metadata.db from the original subset data. To get the song_data.csv I ran this query against the database

select 
songs.*,
group_concat(artist_term.term, ';')
from songs
inner join artist_term
on artist_term.artist_id = songs.artist_id
group by track_id;

which just appends a list of the artist tems separated by a ';' to each track. That should give us a decent start.

You can download any sqlite db browser like [sqlite browser]: http://sqlitebrowser.org/ to run your own SQL queries against it.