DOES MUSIC
SIMILARITY MEASURES
MAKE SENSE?
USING MSD TO EXTRACT TRENDS AND
RELATIONSHIPS IN MUSIC SIMILARITY
SPACE
By:
Asma Rafiq
PhD Student
Centre for Digital Music
Content
 Introduction
 Motivation
 Challenges
 Research Questions
 Research Plan
 Conclusion
My Background
 Topper of MS Software Engineering class as EURECA
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scholar, Sep 09 – July 10 and scholarship holder of
President Talent Farming Scheme during Bachelor of IT.
Completed master’s thesis within a month duration and
produced a publication on that
Produced another publication based on a project done as
a part of a course module at master’s level
Developed a SIS for SU and WMS as a group project
Worked as Intern on Year Book Profile Survey, performed
other tasks such as content-viewing, case study analysis,
success story collection and review, etc. at Pakistan
Software Export Board (Govt) Ltd., Ministry of IT.
Poster presentation at C4DM 10th Anniversary and 3rd
EECS PG Conference.
Publications:
 Publication based on Master’s Thesis:
 Rafiq, A. and Georgieva, O. “Combined Search Trends”,
International Conference on Automatics and Informatics, pp. 1–4,
Oct 2010, Sofia Bulgaria.
 Publication based on IT Entrepreneurship coursework:
 Wahid, A. Rafiq, A., Ahmad, F. and Ruskuv, P. “Discovering
business opportunities via Search Trends”. International
Conference for Entrepreneurship, Innovation and Regional
Development, pp. 818–826, May 2010, Novi Sad, Serbia.
 Publication on Social Networks
 Mesnage, C.S., Rafiq, A., Dixon, S. and Brixtel, R., “Music
Discovery with Social Networks”, Workshop on Music
Recommendation and Discovery, ACM RecSys, October, 2011,
Chicago, IL, USA.
Then, why did I reached this
stage?
 Started with “Modeling Users' Intentions for the
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Enhancement of Music Recommendation Systems”
Online Social Network profiles seemed like a good
starting point to gather information to extract user intent
idea was to make “Music recommendations for events
using Online Social Networks”
Garg, R., et al, (2011) estimated (empirically) that music
discovery increases 6 times due to peer influence in SNS.
Various unexpected issues arose which were beyond my
control and caused delay in the development of the
project instead of May, it was launched in June.
Overall, working with social networks without a prior
agreement can be extremely problematic.
Contribution to PhD Thesis
 A chapter on “Music Recommendation and
Discovery using Online Social Networks”
 Two papers will be used to back this work.
 One is already published in WOMRAD 2011
and another submission to a relevant
conference such as ACM RecSys 2012 will be
done next year.
Next Two Chapters:
 Using Million Song Dataset (which is a
recently released, freely-available collection
of detailed audio features and metadata for a
million contemporary popular music tracks)
to explore trends and relationships in music
similarity space
 Why MSD?
 No dependence to gather data, it is a fixed dataset
and a copy stored at QMUL (Although, it needs to
be updated with new dataset from Lastfm and
Echonest (Taste Profile), and MusixMatch (Lyrics)
 A very large dataset that supports statistically
significant results.
Music Data Mining
 Music Data Mining focuses on extracting
valuable information from the large datasets
containing music-related data in order to fulfill
different user needs such as retrieval and
classification of music.
 Why Music Similarity Space?
 To investigate whether the music similarity is
really useful or not, and find relationships
between items already identified as similar e.g. Do
similar artists (according to Echonest) play similar
tracks (Last.fm)? Can we predict future relations
to appear in similar fashion?
Motivation
 Developing a better understanding of the
music and the artists that perform the
music
 The large dataset can be used to design
automated algorithms that replace and
support human decision-making for
similarity between different items.
 Sophisticated analytics of large dataset
can substantially reveal valuable insights,
that would otherwise remain hidden.
Challenges
 Many of the best performing music similarity estimation
techniques suffer from very high computational
complexity as they are based on techniques such as the
Kullback-Leibler Divergence, Monte-Carlo sampling or
the Earth Mover's Distance (EMD)
 These techniques are also difficult to scale with standard
indexing techniques as they produce non-metric
similarity spaces
 Levy and Sandler (2006) proposed a modified approach
based on the Mahalanobis distance, which produces a
metric similarity space at a lower computational cost, but
results in lower level of performance.
Research Questions
 What insights about music similarity can we
gain from mining music data?
 What are the relationships between similarity
metrics in various systems?
 What are some of the challenges in
processing these extremely large datasets?
How likely users of a different system (i.e. undisclosed
partner of Echonest) are going to play similar songs precomputed by last.fm?
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Echonest Taste Profile user dataset and Lastfm pre-computed similar songs will be
used for this purpose.
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The user-track-playcount triple will be matched against the similar songs from Lastfm
to that track and that specific user will be looked up again, if he/she has listened to
any of the similar songs computed by last.fm
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Association rule learning is a popular and well researched method for discovering
interesting relations between variables in large databases. Association rule
generation is usually split up into two separate steps:
 First, minimum support is applied to find all frequent itemsets in a database.
 Second, these frequent itemsets and the minimum confidence constraint are
used to form rules.
 Apriori Algorithm is the best to mine association rules. It uses a breadth-first
search strategy to counting the support of itemsets and uses a candidate
generation function which exploits the downward closure property of support. It
attempts to find subsets which are common to at least a minimum number C of
the itemsets.
 Apriori uses a "bottom up" approach, where frequent subsets are extended one
item at a time (a step known as candidate generation), and groups of candidates
are tested against the data. The algorithm terminates when no further successful
extensions are found.
Continued from previous slide…
 It shall be used to find relationship between similar songs
listened by the user in two different system
 The results of this experiment will be of interest to learn
whether these two systems (Echonest and Lastfm)
recommend similar songs. Also, how many times user
played similar songs.
 Why would the user listen to these tracks? What factors
make them similar? We can expand this research by
incorporating other features from the MSD such as year
of release, danceability, beat, energy, loudness, tempo,
etc. and determine which of the features plays the most
important
 A demo will be presented as a proof-of-concept
Research Plan
WORKPLAN
WP1: Comparison of Song
Similarity between Echonest and
Lastfm
WP2: Relationship between similar
artists (Echonest) and similar tracks
(Lastfm)
WP3:: Relationship between similar
tracks and related tags in Lastfm
and MusicBrainz
WP4: Finding relationship between
lyrics and tags related to same track
WP4: Stage 2
WP5: Development of Similarity
Space which utilises these
relationships
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Conclusion
 A better understanding and gaining insights
of music similarity with the help of large scale
music related data
 Development of algorithms that could scale
up for commercial systems
 Revealing relationships between various
features related to music for determining
what makes music sound similar?
Thank you for your attention!
Questions?
References
 Garg, R.; Smith, M.D.; Telang, R.; , "Discovery of Music
through Peers in an Online Community," System Sciences
(HICSS), 2011 44th Hawaii International Conference on ,
vol., no., pp.1-10, 4-7 Jan. 2011
doi: 10.1109/HICSS.2011.168
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&ar
number=5718904&isnumber=5718420
 M. Levy and M. Sandler, 2006. “Lightweight measures for
timbral similarity of musical audio” Proceedings of the
1st ACM workshop on Audio and music computing
multimedia, pages 27-36, 2006.