Here we describe various use cases that motivate the creation of MuSim the Similarity Ontology.
Some instances of musical similarity are rather obvious and straight-forward. For example, Simply Red's track "Sunrise" uses essentially the same melodies and instrumentation as Haul and Oates "I Can't Go for That" however the lyrical content and song structure are totally different. We can quite easily call these two songs similar and even consider this statement to be somewhat objective - the similarity is not so much a matter of opinion but based in the facts of the musical make-up of the respective songs.
Just a Gigilo is an adaptation by Irving Caesar in 1929 from the Austrian song "Schöner Gigolo", written in 1928 by Leonello Casucci (music) and Julius Brammer (lyrics).
In such instances the property-based approach to similarity
(mo:similar_to) might be sufficient. But
perhaps we want to say more or know more. Is this a
case of sampling or just quoting? How does this
relate to the interests of the various copyright
holders (sigh)? Can we easily or automatically find
other tracks that re-use "I Can't Go For That" in an
analogous way?
Signal analysis or "computer listening" is sometimes used to determine inter-song similarities. For example, we might extract Mel-frequency cepstral coefficients (MFCCs) from a set of audio signals, use these to create Gaussian mixture models for each song, and determine inter-song dissimilarities using an earth mover's distance function. Alternatively, we might start with the same MFCCs but then calculate mean and variance vectors and use KL-divergence between these vectors and the vectors of other audio signals to create inter-song dissimilarity measures.
We would then like to express associations between audio signals referencing the actual numerical results of the calculation as well as the various algorithms that were used to obtain the results. Perhaps we would also like to specify additional metadata about the association such as date of creation and references to the exact audio files used. What is the most appropriate way to represent this data?
One might be interested in exploring music in terms of historical rivalries such as hip-hop feuds or beefs - a classic example being the feud between two music groups of the 1980's Boogie Down Productions and The Juice Crew. A series of tracks were released by various members of the respective groups contesting the origins of hip-hop and launching personal attacks against members of the opposing group. This series of answer records eventually became known as the Bridge Wars and was drawn out over a number of years and through a number of proxies.
We can use existing properties to describe group
memberships (foaf:member) and track
authorship (foaf:made) but how do we
associate these two feuding groups? How do we
associate the various releases (answer records) that
provide a musical record of this feud?
A music critic might remark The unique sense of lyricism and unparalleled melodic genius of Fredrick Chopin had a profound influence on the later work of Claude Debussy. Such statements maybe quite useful for music discovery or recommendation.
Exactly how should we express such a subjective statement? We would definitely want to know who is making this statement and what sort of credentials she might hold. Also what kind of consistency checks can we provide for such statements? Of course Debussy could not have influenced the work of Chopin as Chopin died nearly 20 years before Debussy was born.
Associations between musical things are often highly personal and might be tied to a time, place, or person. Consider the following example:
When a first year student at college, I dated a girl who loved Bob Marley and David Bowie.
The narrator in the above passage may wish to create an association between Bob Marley and David Bowie that references a time (first year of college), a place (college), and a person (previous girlfriend). How can we allow for this level of provenance-tracking and expressiveness?
Last modified: Wed Jun 23 15:29:08 CDT 2010