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MillionSongDataset
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#  The Million Song Dataset ## Can the genre of a song be predicted based off of a few audio features? What if more artist-based features are included? ## Can a song be identified as hot or not off of the same set of variables? Using a 10,000 song sample, from the Million Song Dataset (https://labrosa.ee.columbia.edu/millionsong/), these questions will attempt to be answered. This dataset contains a million songs from 1922-2011, with artist tagged information from Echonest (now part of Spotify), along with audio measurements, and other relevant information. ### Citation: Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere. The Million Song Dataset. In Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), 2011. ## In this repository you will find: -The extraction of HDF5 source files and some initial EDA:<br/> [01_Extract_HDF5_Files_to_a_CSV_File.ipynb](https://github.com/SamLundberg/MillionSongDataset/blob/master/01_Extract_HDF5_Files_to_a_CSV_File.ipynb) -Web-scraping the Billboard Top 100 charts from 1958-2011, to determine if a song was popular or not:<br/> [02_Scrape_Billboard_top_100.ipynb](https://github.com/SamLundberg/MillionSongDataset/blob/master/02_Scrape_Billboard_top_100.ipynb) -Exploratory Data Analysis:<br/> [03_EDA.ipynb](https://github.com/SamLundberg/MillionSongDataset/blob/master/03_EDA.ipynb) -Predicting if a song is hot/popular or not using information from the Billboard Top 100 charts:<br/> [04_Predict_Hot_Billboard Songs.ipynb](https://github.com/SamLundberg/MillionSongDataset/blob/master/04_Predict_Hot_Billboard_Songs.ipynb) -Predicting if a genre can be found with audio features:<br/> [05_Genre_Identification.ipynb](https://github.com/SamLundberg/MillionSongDataset/blob/master/05_Genre_Identification.ipynb) An Executive Summary is included in the Documents folder:<br/> [Summary](https://github.com/SamLundberg/MillionSongDataset/blob/master/Documents/Executive_Summary.md) Below is a dictionary of audio terms and columns that are found in the 10k dataset file, for reference:<br/> | Field name | Description | |-----------------------------|----------------------------------------------| | artist familiarity | algorithmic estimation | | artist hotttnesss | algorithmic estimation | | artist id | Echo Nest ID | | artist latitude | latitude | | artist location | location name | | artist longitude | longitude | | artist name | artist name | | artist terms | Echo Nest tags (genre classification) | | artist terms freq | Echo Nest tags freqs (how often it shows up) | | artist terms weight | Echo Nest tags weight (genre weight) | | bars start | beginning of bars, usually on a beat | | beats start | result of beat tracking | | duration | in seconds | | end of fade in | seconds at the beginning of the song | | key | key the song is in | | loudness | overall loudness in dB | | mode | major or minor | | release | album name | | sections start | largest grouping in a song, e.g. verse | | segments loudness max | max dB value | | segments loudness max time | time of max dB value, i.e. end of attack | | segments loudness max start | dB value at onset | | segments pitches | chroma feature, one value per note | | segments start | musical events, ~ note onsets | | segments timbre | texture features (MFCC+PCA-like) | | song hotttnesss | algorithmic estimation | | start of fade out | time in sec | | tatums start | smallest rythmic element | | tempo | estimated tempo in BPM | | time signature | estimate of number of beats per bar, e.g. 4 | | title | song title | | year | song release year from MusicBrainz or 0 | ### Please note that the Data folder is not included because of size limitations of the repository
