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Genre Classification
and Analysis
Anat Gilboa
Yanjun Qi, PhD
The Journey
Music Information
Retrieval 101
Constructing approaches
to a not-so-well-defined
problem
Finding good data
Sim...
Let’s find out…
• What makes one song similar to another?
• What are the characteristics by which we can “classify” the ge...
• Supervised
• Regression (Linear, Logistic, Ridge)
• Classification (Bagging, Naive Bayes, SVM, NN, KNN)
• Unsupervised
•...
Music Information Retrieval
101
• Aims to extend the understanding and usefulness of
music data, through research, develop...
Adoption
• USPOP2002
• Magnatagatune
• CAL500
• RWC MDB
• International Society for Music Information Retrieval
(ISMIR) 20...
• Collection of audio features and metadata for 1,000,000 contemporary popular
music tracks.
• 44,745 unique artists w/dat...
loudness
mode
mode confidence
release
release 7digitalid
sections confidence
sections start
segments confidence
segments l...
Numerical Features
Danceability - how danceable a song is. 0 is least danceable, 100 is most danceable.
Duration - the len...
Inspiration came from…
• 8,761 songs
• (ty, API request timeouts & rate limiting)
• 307 genres-extracted from the Artist API
• k-means centroids
...
• Use K-means to create centroids for each genre
• Hypothesis: If there are 307 genres
represented, would each be in the s...
K-Means
K-NN
The Future
• There’s a long way to go…
• No one can predict the future…
• MIR is awesome and powerful
• But seriously, K-f...
–Anat
“Inspirational Quote”
Sources
• http://developer.echonest.com/docs/v4/_static/AnalyzeDocumentation.pdf
• https://github.com/echonest
• https://g...
Thank You!
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Genre Classification and Analysis

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Anat Gilboa's thesis presentation to the University of Virginia School of Engineering and Applied Science.
Over the course of her final semester, Anat and Dr. Qi, PhD, tested various methods to determine similarity between songs using features extracted from metadata in the Million Song Dataset.

Published in: Technology
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Genre Classification and Analysis

  1. 1. Genre Classification and Analysis Anat Gilboa Yanjun Qi, PhD
  2. 2. The Journey Music Information Retrieval 101 Constructing approaches to a not-so-well-defined problem Finding good data Simplifying the problem Data visualization Finding not-so- good data Fall 2014 Today!Iterating Machine Learning 101 Spring 2015
  3. 3. Let’s find out… • What makes one song similar to another? • What are the characteristics by which we can “classify” the genre of a song? The Problem
  4. 4. • Supervised • Regression (Linear, Logistic, Ridge) • Classification (Bagging, Naive Bayes, SVM, NN, KNN) • Unsupervised • Dimension Reduction (PCA) • Clustering (K-means, GMM/EM, Hierarchical) Machine Learning 101
  5. 5. Music Information Retrieval 101 • Aims to extend the understanding and usefulness of music data, through research, development and application of computational approaches and tools • Combines concepts and techniques from music, computer science, signal processing and cognition • Music information: bibliographical, surveys, tags, scores, MIDI, audio, etc
  6. 6. Adoption • USPOP2002 • Magnatagatune • CAL500 • RWC MDB • International Society for Music Information Retrieval (ISMIR) 2011 Dataset
  7. 7. • Collection of audio features and metadata for 1,000,000 contemporary popular music tracks. • 44,745 unique artists w/dated tracks starting from 1922 • 10,000 song subset (1%, 1.8 gb) • Each song has a number of features… The Million Song Dataset
  8. 8. loudness mode mode confidence release release 7digitalid sections confidence sections start segments confidence segments loudness max segments loudness max time segments loudness max start segments pitches segments start segments timbre similar artists song hotttnesss song id start of fade out tatums confidence tatums start tempo time signature time signature confidence title track id track 7digitalid year analysis sample rate artist 7digitalid artist familiarity artist hotttnesss artist id artist latitude artist location artist longitude artist mbid artist mbtags artist mbtags count artist name artist playmeid artist terms artist terms freq artist terms weight audio md5 bars confidence bars start beats confidence beats start danceability duration end of fade in energy key key confidence key tempo Song Fields
  9. 9. Numerical Features Danceability - how danceable a song is. 0 is least danceable, 100 is most danceable. Duration - the length of the song in seconds. Energy - the overall energy of the song, 0 is least, 100 is most. Hotttnesss - the popularity of the song, 0 is least, 100 is most. Key - the key the song. 0 is C, 1 is C# and so on. Liveness - the likelihood that a song was performed in front of an audience. Above 80 is usually live. Loudness - the overall loudness of the song in decibels. Mode - the mode of the song where major is 0 and minor is 1. Speechiness - how much spoken word is in the song. 0 is least, 100 is most Tempo - the most frequently occurring tempo in the song, in beats-per-minute. Time signature - the number of beats per measure in the song. Acousticness how acoustic vs. electric is the song Valence how positive or negative is the mood of the song
  10. 10. Inspiration came from…
  11. 11. • 8,761 songs • (ty, API request timeouts & rate limiting) • 307 genres-extracted from the Artist API • k-means centroids • 3,944 artists • Between 1 - 11 appearances in the set The Facts
  12. 12. • Use K-means to create centroids for each genre • Hypothesis: If there are 307 genres represented, would each be in the same cluster? • Create K-nearest neighbor tool to fetch k nearest songs to some specified datapoint • f(Tempo, Key, K) Tasks
  13. 13. K-Means
  14. 14. K-NN
  15. 15. The Future • There’s a long way to go… • No one can predict the future… • MIR is awesome and powerful • But seriously, K-fold cross validation
  16. 16. –Anat “Inspirational Quote”
  17. 17. Sources • http://developer.echonest.com/docs/v4/_static/AnalyzeDocumentation.pdf • https://github.com/echonest • https://github.com/tbertinmahieux/MSongsDB
  18. 18. Thank You!

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